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Economics of energy conservation in commercial greenhouses : microcomputer spreadsheet model Shell, Barry 1985

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E C O N O M I C S O F E N E R G Y C O N S E R V A T I O N I N C O M M E R C I A L G R E E N H O U S E S : M I C R O C O M P U T E R S P R E A D S H E E T M O D E L by B A R R Y S H E L L A THESIS S U B M I T T E D I N P A R T I A L F U L F I L M E N T O F T H E R E Q U I R E M E N T S F O R T H E D E G R E E O F M A S T E R O F S C I E N C E in T H E F A C U L T Y O F G R A D U A T E S T U D I E S Resource Management Science Programme We accept this thesis as conforming to the required standard T H E U N I V E R S I T Y O F B R I T I S H C O L U M B I A A p r i l 1985 Barry Shell, 1985 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the The University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the Head of my Department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Resource Management Science Programme The University of British Columbia 2075 Wesbrook Place Vancouver, Canada V6T 1W5 Date: A p r i l 1985 Abstract Microcomputer software for capital cost analysis in greenhouse energy management is developed for use by extention workers in agriculture. A "template" for proprietary "spreadsheet" software is created that models greenhouse operation and performs a Net Present Value analysis of the cash flow for the life of up to ten energy saving techniques chosen by the grower. The results are displayed on the screen or printed out. Variables can be altered to suit specific user's needs and to check sensitivity of the model. A survey of local greenhouse growers was done to determine the specifications for the software developed and to verify the accuracy of the model. The resulting program is designed to run under M S - D O S on the I B M personal computer or any other similar microcomputer. i i Table of Contents Chapter Page I. I N T R O D U C T I O N A N D L I T E R A T U R E S U R V E Y 1 A . Introduction 1 Industrial Energy Use 2 B .C . Greenhouse Industry Overview 4 Energy Saving Measures 8 B . Previous Work 10 History of Greenhouse Research 11 Biophysical Studies 12 Agricultural Engineering Studies 15 Energy Balance 16 Economically Optimizing Models 19 Capital Budgeting 20 Computer Simulation 23 II. D E V E L O P M E N T O F A G R E E N H O U S E S I M U L A T I O N M O D E L 26 A . Introduction 26 The Questionnaire 26 Summary of Questionnaire Results 27 Conclusions Derived from the Questionnaire 28 B. Project Software and Hardware 29 Microcomputers and Simulation Models 29 Microcomputer Spreadsheet Modelling Programs 30 III. T H E S P R E A D S H E E T M O D E L 32 A . Introduction 32 Program Overview 32 B. Input Variables 34 L o o k - u p Tables 34 i$i C . The Greenhouse Energy Balance 35 Basic Assumptions 36 The Energy Fluxes and Balance Equations 36 Convective and Thermal Losses 36 Conduction Losses 37 Infiltration and Ventilation Losses 38 Net Solar Radiation 40 Energy Saving Greenhouse Simulation 41 D . Economic Analysis 41 Crop Y i e l d and Value 41 Capital Budgeting 42 Calculation of Net Present Value 45 IV. V E R I F I C A T I O N A N D T E S T I N G O F T H E M O D E L 48 A . Test of Model Accuracy 48 Heat Loss Predictions 48 Financial Analysis 50 Sensitivity Analysis 52 B. User Testing of the Model 55 C . Test of the Model with Greenhouse Operators 56 V . C O N C L U S I O N 59 A . Energy Use and Y i e l d in B .C. Greenhouses 59 B. Value and Limitations of the Study 60 C . Future Use of Greenhouse Spreadsheet Models 62 References 64 A P P E N D I X A : G R E E N H O U S E Q U E S T I O N N A I R E 69 A P P E N D I X B: G R E E N D A T L I S T I N G 73 A P P E N D I X C : G R E E N S I M L I S T I N G 77 IV A P P E N D I X D : G R E E N S I M E Q U A T I O N S A P P E N D I X E : U S E R ' S M A N U A L A P P E N D I X F : U S E R ' S C H E C K L I S T List of Tables Number Page 1. Table of relative energy consumption for delivery of fresh winter vegetables to a Northern U.S. market 7 2. Energy conservation measures included in this study 9 3. Effects of energy saving measures on energy balance variables 42 4. Sample cash flow analysis 44 5. Characteristics of commercial greenhouses used for program verification 49 6. Comparison of actual and simulated heat loss 49 7. Comparison of energy saving investments in four types of greenhouses 51 8. Results of sensitivity analysis on selected program variables 53 v i : List of Figures Number Page 1. World energy demand predictions 2 2. Production costs of cucumbers 7 3. Market price and crop yield dynamics of B.C. greenhouse cucumbers 14 4. Schematic illustration of typical greenhouse energy fluxes 17 5. How spreadsheet programs work 31 6. Logical flow of the G R E E N S I M program 33 7. Energy use versus yield in B .C. greenhouses 60 vii i Acknowledgements The financial support through a G . R . E . A . T . award to the author by the B .C. Science Council ; and the greenhouse energy use data base provided by Chris Dyble of B .C. Hydro, the cooperating agency, are gratefully acknowledged. I would like to thank Len Staley for his steady guidance during my years at U . B . C . Special mention is also due to Dr . M . Novak and D r . R. Heinkel of U . B . C . for providing input towards greenhouse energy modelling and cost/benefit analysis, respectively. Finally, the development of this software would not have been possible without the provision of microcomputer facilities by the U . B . C . Centre for Continuing Education. vii? I. I N T R O D U C T I O N A N D L I T E R A T U R E S U R V E Y A . Introduction In 1983 the British Columbia Greenhouse Vegetable Growers Research Committee published a review of Greenhouse Energy Conservation (Bryenton eL al. 1983). This report revealed two major shortcomings: "Few North American researchers supply (1) economic analyses or (2) productivity changes for their recommended measures for energy conservation, leaving report readers on their own to perform cost/benefit analyses." The objective of this research project is to create a computer model that brings together crop productivity data and energy consumption data in order to conduct a proper cost/benefit analysis of energy saving measures in commercial greenhouses. The computer model in this project is designed to work on many popular microcomputers using software that allows an individual grower to simulate his own greenhouse operation. The key parameters of an individual greenhouse are included in each simulation to increase its authenticity, and lend credence to the resulting recommendations. The marriage of a simple energy balance model with a popular spreadsheet program allows the user to "experiment" with a variety of energy saving options (see Table 2). The goal is not to find an absolute answer (i.e. the exact amount of energy or money saved), but to aid in the choice among alternative energy conservation schemes. The introduction is divided into three parts. It begins with a general discussion of energy conservation efforts in industry. The second part is an overview of the B .C. commercial greenhouse business with an emphasis on energy use problems. Finally, in the third section, the energy saving measures that are the subject of this thesis are introduced. 1 2 I World* er lergy demand proje ctions ^---^^—- . s r - S ^ 1965 1970 1975 1980 1985 Figure 1: World energy demand predictions (Ocean Industry, October 1981). Industrial Energy Use Annual forecasts of world energy demand have been decreasing steadily for the last ten years (Fig. 1). This trend is in part due to a world wide recession, but this fact alone cannot account for the projected decrease in energy demand. The widespread use of energy conservation techniques initiated as a result of the "energy crises" of the 1970's is thought to be a major reason for the reduction in demand trends (Wilmer 1982). This does not imply that actual demand is decreasing. It continues its inexorable climb; though at a slower rate. Despite the apparent energy glut that we are now enjoying, recent estimates of world energy supplies are pessimistic. For example, a Rand Corporation study predicted a 90% chance of running out of North American oil by 2000. (Nehring 1981) The potential for energy conservation in all sectors is still great It is estimated that, overall, only 5-10% of possible savings have been realized. For example, in Canada's largest industry, pulp and paper, the 1980 five year goal to decrease energy use by 30% is not likely to be met To date they have only reduced energy consumption by 2% (Tutton 1984). 3 A need therefore exists for research into the application of energy conservation techniques in industry. A n analytical tool addressing two of the problems that hinder the widespread proliferation of these techniques is the subject of this thesis. The first problem is the capital intensive nature of investment in energy conservation. Often a substantial initial investment is required to realize future energy savings. The economic pay-of f of these savings to the investor is often not clear. It is felt that i f these potential savings could be made more tangible, a much greater level of investment would ensue (Jackson 1983). In addition, energy is often expressed in terms of Watts or Megajoules; terms that are practically meaningless to the average energy consumer. Energy use engineering studies often use these units of measure where actual cash flows would be more meaningful to plant managers. A comprehensive financial analysis of energy conservation investment in industry is needed to demonstrate its viability. A second problem is that end users are often skeptical about energy conservation measures because they believe there wil l be undesirable side effects. They may indeed expect energy savings, but they feel there may also be production losses or higher labour costs associated with energy conservation. These costs are rarely (if ever) addressed by engineering studies in this field. These problems will be dealt with by presenting energy savings as discounted future cash flows that allow for the losses associated with some energy conservation techniques. The analysis wil l be presented as an interactive computer simulation. A typical industrial energy user or government extension worker should find it relatively easy to use and understand. The commercial greenhouse industry was chosen for this study because it is particularly energy intensive and there has been a recent call for assistance (Mauza 1982). A greenhouse crop cannot survive a loss of energy in cold weather. Yields (and consequently profits) are strong functions of operating temperatures. Since energy 4 presently represents approximately one third of total production costs, there is a great incentive to conserve. B . C . Greenhouse Industry Overview The first commercial greenhouses in B .C. were built by Japanese immigrants on the G u l f Islands in the early 1920's. Today there are over 300 greenhouse growers in B .C. representing approximately 120 hectares "under glass". About 90% of these are located in the south coastal region. The greenhouse industry accounts for about 10% of the total agricultural production of B .C. In recent years the percent market share of greenhouse crops has grown considerably (Scott 1984). The industry can be roughly divided into two groups. Horticultural crops such as cut flowers, tropical house plants, and ornamental shrubs make up about 60% of the trade. The remainder is devoted to market vegetables; mainly tomatoes and cucumbers. Cultural practices vary widely in the horticultural sector due to the diverse nature of the crops grown. The vegetable growers have adopted more consistent growing techniques. This thesis concerns itself with the latter group because the greater homogeneity of the vegetable industry lends itself to a more formal analysis. The major factors in greenhouse vegetable production are: • Sunlight available to the plant • Indoor climate control. • Management techniques (ie. Materials handling, labour, etc.) • Cultivars grown. • Timing of Planting, harvesting, etc. Individual growers must coordinate these elements to achieve the highest possible annual yield. Most growers in B.C. have adopted hydroponic growing techniques. Plants are set out in bags of sawdust and are fed nutrient solution through a trickle irrigation 5 system. This cultural technique can easily double production and is less labour intensive than traditional soil bed cultivation. Virtually all B .C. greenhouses have automatic climate control systems. There is a trend toward microcomputer control which can cost-effectively monitor the outdoor environmental conditions and adjust indoor climate for optimum growth. Also roof vents can be opened in consideration of the wind direction for passive ventilation with no need for electric fans. These systems save energy, labour and time. They also aid in management by keeping historical records of climate and control settings. Another recent innovation is the use of carbon dioxide enrichment. This technique increases yield and produces larger more marketable fruits. N o single type of greenhouse dominates the industry, though there is a trend towards large ranges of medium sized subunits connected together at the gutters. The currently popular model is the Dutch Venlo type. Large fluctuations of energy use and productivity within each construction type occur because of variations in location and management techniques. A study showed no correlation between yield and energy use in 27 Dutch greenhouses (de Visser 1981). A recent survey of local greenhouse energy use supports this view. The typical greenhouse is usually made of glass, polyethylene, or fibreglass. Polyethylene houses normally have double air inflated roofs and fibreglass side walls. Generally, glass houses produce higher yields but use more energy than double layer polyethylene houses, although there are growers whose horticultural skills generate exceptions to this rule every year. Double poly houses are much cheaper but the plastic slowly deteriorates, becoming less transparent It must be replaced every three to four years. The average size of a B .C . vegetable greenhouse complex is approximately 5000 square meters (50,000 square feet) but individual installations can range from 500 to 15,000 square meters. 6 The two major crops are cucumbers and tomatoes. Cucumbers are warmer growing plants and require more energy than tomatoes. Currently, average industry costs for energy are approximately $ 8 / m 2 of planted area for tomatoes and $10/m 2 for cucumbers.1 Between 1975 and 1983 the fuel cost as a percentage of total cucumber production cost has risen from 12% to nearly 30% (Fig. 2). As fuel costs are expected to increase in the future, energy conservation is essential for greenhouse vegetables to remain competitively priced. The major source of competition in the B .C. vegetable market is field grown produce transported from California and Mexico. Despite transportation costs, the market prices of these products are almost always below those of locally grown hothouse crops.2 A 1977 study comparing energy requirements of the two options — homegrown versus imported — serves to illustrate the problem (Roberts 1981). In Table 1 energy inputs were normalized so that all of the energy required to produce fresh winter vegetables in the south and ship them to northern markets shows as 100 percent This study was conducted in Ohio, so energy costs for heating and cooling are probably somewhat higher than the Vancouver area. Nevertheless, it clearly indicates the large energy penalty that local growers must overcome in order to be truly competitive.3 Energy used for greenhouse heating would have to decrease by a factor of eight according to the Ohio study. Although great potential for energy conservation has been widely claimed in the greenhouse industry, it is doubtful whether it alone can effect this eight fold decrease. Perhaps changes in government policies (i.e. energy tax credits) or trends in international trade (i.e. falling dollar), wil l be required. Natural gas is the energy source used by most of the industry. However, growers on Vancouver Island still use oil and several farmers are experimenting with 1 Based on a 1984 B.C. Hydro natural gas price of $0.4049/Billing Uni t (100 MJ) . 2 For imported cucumbers, transportation currently works out to cost about 2 cents per cucumber. 3 M a n y B . C . growers would not be able to survive i f they were not receiving a subsidy from the B .C. Farm Insurance Program amounting to about 10 - 15 cents/lb. 7 • • • • • • • • • ' 1975 1976 1977 1978 1979 1980 1981 1982 1983 Figure 2: Production Costs of Cucumbers (Bryenton 1983). Table 1: Table of Relative Energy Consumption for Delivery of Fresh Winter Vegetables to a Northern U.S. Market E n e r g y I n p u t F i e l d G r o w n L o c a l G r e e n h o u s e C u l t u r a l E n e r g y 3 3 . 7 4 .0 P r o c e s s i n g a n d C o n t a i n e r s 1 1 . 9 9 . 9 T r a n s p o r t a t i o n 54.4 0 . 0 H e a t i n g a n d C o o l i n g 0 . 0 6 9 3 . 1 T O T A L S 1 0 0 . 0 7 0 7 . 0 wood as a fuel source. Typically heating plants are comprised of one or two hot water boilers. Water at 65 ° C is pumped to the growing area via 6 cm water pipes. Traditional pipe placement is along the sidewalls and above the plants at gutter 8 height Many energy saving techniques are being tried, however when compared to the potential energy savings, the present level of energy conservation in the greenhouse industry is relatively small (Bryenton 1983). Energy Saving Measures This thesis examines the costs and benefits of ten energy saving measures available to local growers (see Table 2). Each of these techniques relies on one of the three basic methods of reducing energy use in buildings. One is to improve the efficiency of the heating plant, heat distribution system and controls. A second scheme is to reduce heat losses by infiltration of cold air through cracks. Finally, the building envelope can be made more resistant to conductive and radiative losses. There are several other practices that can indirectly decrease energy consumption. For example, newly developed cool growing or early maturing cultivars can be planted. Propagation dates can thus be delayed or growing temperatures lowered. There are also techniques for storing solar heat under benches or below the ground. This can reduce the total energy demand. Dozens of methods for increasing heating system efficiency are available. The computer model which is the subject of this thesis, can simulate the effects of five: root zone heating, infra-red heating, stack heat recovery, microcomputer environmental control, and heat storage. Root zone heating is an attempt to distribute heat to the plants more effectively. To benefit from this form of heating, the traditional placement of heating pipes is changed. Hot water is delivered to the planted area via hundreds of smaller tubes that are placed along the floor of the greenhouse near the plants' roots. Heat is thereby brought to the plants where it may be needed most Infra-red radiant heating is accomplished by burning natural gas in such a way that the bulk of its energy is released as radiation in the long wave infra-red. These 9 Table 2: Energy Conservation Measures Included in this Study. E N E R G Y S A V I N G M E A S U R E I N S T A L L E D C O S T ($/m J ) A N N U A L E N E R G Y S A V E D P O S S I B L E S IDE E F F E C T S Root Zone Heating $19.00 15%2 Uneven heat, fruitset problems Stack Heat Recovery 12,000 ea.3 12%2 High mainL cost, low op. temp. Infra-red Heating 21.00 25%3 Less light, fruitset problems Microcomputer Control 20,000 ea.4 12%3 Difficult to master Sealing Glass Laps 8.501 20%' High humidity Second Glazing 8.001 40%' Less light, high humidity North Wall Insulation 4.005 10%' Less light Meter Height Insul. 4.003 10%' -Thermal Curtains 30.00' 35%3 lowlight, high humid., mainL probs. Heat Storage 17.005 25%3 High cost, maintenance unproven 'Badger 1979, 2 Blom 1982, 'Bryenton 1983, 4 Kowalski 1984, 5Staley 1984 burners are typically placed in the peak of a greenhouse and the radiant energy is reflected onto the plant canopy. In this way the plants themselves are heated directly without the intervening heat transfer medium such as water or air — a much more efficient process. A stack heat recovery unit is simply a gas-to- l iquid heat exchanger that reclaims some of the heat from the smokestack of the boiler. Energy savings resulting from the use of a microcomputer greenhouse environment management system can also be simulated by the model. These were discussed in the previous section. The fifth energy saving measure in the model is heat storage. This technique -is based on the results of experiments in Saanich, B .C. with the Japanese wet earth 10 heat storage system (Staley 1984). In this technique hot solar heated air inside the greenhouse is drawn through pipes buried beneath the floor, thereby storing heat in the earth under the building. A t night this heat can be reclaimed to help decrease energy requirements. Infiltration losses can be reduced by caulking between glass laps with silicone sealant or by covering the building surfaces with a second layer of glass or plastic. These two methods can be simulated by the model. Energy saving measures for decreasing conductive and radiative heat losses include thermal curtains and insulating some of the greenhouse walls. Typically the north wall is insulated with opaque polyurethane foam. In British Columbia the sun is almost always in the southern part of the sky so little direct sunlight is blocked using this technique. The model can simulate this, in addition to the installation of polyurethane insulation along inside walls to a height of one meter. This is the usual location of hot water heating pipes and is therefore an ideal place for effective insulation. A thermal curtain or blanket is a flexible material which is pulled across the roof from gutter to gutter and sometimes around the side walls of a greenhouse at night Materials range from light plastic film to dark heavy laminated fabric. These coverings reduce heat loss by convection, conduction, infiltration and radiation and have therefore come under considerable investigation recently. B . Previous Work Four main bodies of information are of interest for this study: the biophysics of plant growth, the physics of the greenhouse environment, the financial analysis of capital investment and the computer simulation of real systems. Plant growth is the realm of plant physiologists. A vast literature exists but few papers relating to greenhouse crops have been published. The physical behavior of greenhouses is studied 11 by agricultural and mechanical engineers and has recently been reviewed in the Applications Handbook of the American Society of Heating, Refrigeration, and Air-condit ioning Engineers ( A S H R A E 1982). Capital budgeting is a standard technique developed by financial analysts to predict the effects of capital expenditures on the value of a firm. Its sophistication has been enhanced by the advent of computers and it is now a standard chapter in all business management textbooks (Brigham 1983). Computer simulation is a recently developed problem solving tool that is very popular in resource management studies. Recent texts have reviewed the field (Roberts 1983). History of Greenhouse Research Greenhouses have been in use for approximately 300 years (perhaps beginning with the orangeries of Louis X I V ) but surprisingly little research has been done to understand how they work and to optimize performance. Since greenhouse crop culture is relatively intensive in its use of resources (energy, labour, water, space) it would seem that a great deal of literature on the subject would exist simply for reasons of economics. In addition, because of their special ability to control the plant environment one would think greenhouses would have come under considerable study by scientists. In fact the first comprehensive attempt to characterize the physics of the greenhouse environment was published in 1963. The 1981 A S H R A E Handbook of Fundamentals states, Present greenhouse systems. . . while appearing somewhat crude and inefficient at first look, have evolved from trial and error experiences. Although greenhouses are high energy consuming structures, they are still the preferred environment for growing plants throughout the year. The structures and technology have changed only slightly in the past 50 years. " U n t i l World War II, the research was limited to a single . . . experiment4. . . in 4 A demonstration of the "greenhouse effect" showing that convection rather than radiation was the dominant causative factor (Woods 1909). 12 1909 and collections of data at several places" (Businger 1963). Since the war and especially in the last 20 years a great deal of work has been published. Biophysical Studies Knowing exactly what factors affect the growth of a crop is necessary in order to predict the side effects of energy saving measures on the productivity of a greenhouse. One can turn to Biophysical studies to find suitable relationships between crop yield and the greenhouse environment The many variables that are involved in plant growth make predictions of crop yield very difficult (Horie 1979, Aldrich 1983, Seginer 1984). This is particularly true when only one or two factors (such as temperature and irradiation) are considered. In addition the problem is complicated by the variability of the farmers, themselves. A good grower can compensate for the lack of one plant growth factor with a variety of techniques. The widely differing expertise of growers is a difficult quantity to express in equations that predict crop yield. Some studies have reported no reduction in yield with changes in growing environment (cooler temperatures and tighter greenhouses) but these are rare (Van Steekelenburg 1981). The complexity of the plant/environment system means these empirical studies should be viewed with caution — in a relative rather than an absolute sense. A great number of questions arise as to the best technique for expressing crop yield. Should it be a measure of leaf formation rate, net photosynthesis, or fruit set? If the latter, should one utilize the time between sowing and harvest or the total weight of fruit? A l l the above methods have been used. Horie (1979) plotted net photosynthesis as a function of photosynthetically active radiation (PAR) at various humidity levels for greenhouse cucumbers. Challa (1980) prefered leaf formation rate as a function of temperature and P A R . This relationship was better suited to the real-time computer control system they developed for greenhouses. They used the 13 following empirically derived equation: dP/dt = fIT3 g{I] = (1-e ,(5.59-0.4T). >(6-e' ,(1.704-0.006391). ') [LI] where, P = leaves/plant (plastochron age of plant) fIT} = function of temperature on rate of leaf formation g{I} = function of P A R on rate of leaf formation T = temperature inside greenhouse I = P A R inside greenhouse W / m 2 t = time in days Others have used similar empirical relationships (Leibig 1981, Seginer 1981). A l l are usually based on linear regression analyses of data collected from a small number of greenhouses. This is understandable since a purely derived expression of plant growth would be complex. One major problem with evaluating crop yields is their sensitivity to timing. Crops planted at different times of the year require varying numbers of weeks to reach maturity. The cash value of a crop is often directly related to its earliness; the first fresh vegetables of the season commanding the highest price. In fig. 3 the price dynamics of the cucumber market and the rate of harvest are superimposed to illustrate this point Some researchers have developed analytical techniques to determine the value of earliness (Van de Vooren 1978, Verhaegh 1981, Grange 1983). Van de Vooren presented the equation: Y 141 - 3.38T - 0.0441 [1.2] where, Y earliness in days T 24 hour mean temperature I average daily irradiation in J / c m 2 /day 14 T I M E I N MONTHS Figure 3: Market Price and Crop Yie ld Dynamics of B .C. Greenhouse Cucumbers. (Source: B .C. Hothouse Products, 1983) This equation, like many others reported in the literature, has little use outside of the experiment performed. This is due to its empirical nature and the sketchy description of how the parameters were measured. The effect of specific greenhouse energy conservation measures on plant yields has been widely reported. One example is the use of thermal curtains (Stokes 1981, Verhaegh 1981, Grange 1983). Grange reported that while overall yield was increased by 9% (on a kg of fruit per plant basis) early yield decreased by 27%. This is presumably due to the slight reduction in available radiant energy (about 4%) due to the shading caused by the curtain apparatus during the day. Verhaegh showed that crops are particularly sensitive to light levels in their early growth stages. H e studied a number of greenhouses over three growing seasons and was able to show, with a high statistical correlation ( r 2 = 0.9) that a 10% decrease in light during the early part of the growing season resulted in a 20% reduction in yield. However, there was no correlation between light levels and yields later on in the growing season. 15 These results were confirmed by Stokes (1981) who also reported a 4% light loss due to thermal curtains and a corresponding yield reduction of 10 - 12%. Another common energy conservation technique that has the potential of reducing yields is the use of double glazing; in particular double poly (Baurle 1978, Verhaegh 1981, A S H R A E 1982). For a tomato greenhouse Baurle reported a light reduction of 18% and reduced yield of 7% for the spring crop and 11% for the fall crop. The higher yield in the spring season is the opposite of the light reduction effects reported by other workers and is not explained. From the preceding discussion it can be seen that attempts to demonstrate correlations between environmental parameters and plant growth can lead to conflicting results. A t best, experimental results conform to the same order of magnitude. The growing of plants is still very much an art which may explain why the bulk of the scientific studies reported in the literature do not consider plant yields. Many engineering studies do not even consider the crop. Agricultural Engineering Studies Agricultural engineers are predominantly concerned with the response of the greenhouse environment to changes in the building envelope structure or variations in outdoor climatic conditions. A knowledge of these relationships is necessary in order to develop the computer simulation of greenhouse energy fluxes. A great deal of work has been done in measuring the response of single parameters to a specific change. For example many studies have measured the effect of glazing types on available radiant energy flux (e.g. Stoffers 1981). Similar studies can be found that give equations expressing overall heat loss as a function of wind speed (e.g. Bot 1981). The first attempts to combine all this work into a comprehensive analysis of greenhouse behavior began in the 60's using abstract models. These are sets of 16 mathematical equations for' quantities in the plant/environment system. The solutions of the equations are used to predict the behavior of the system. Energy Balance The standard modelling approach has been the energy balance method, in which all heat gains are equated with all heat losses. " A n energy balance simply states that the flow of energy supplied to the greenhouse must either be lost, absorbed by the plant mass, or stored in a part of the greenhouse through an increase in temperature" (Silveston 1980). Businger (1963) is credited with being the first to study the greenhouse environment using this method. In recent years the analyses have become more complex but the basic technique and assumptions remain essentially the same. Businger's assumptions were: (a) with respect to radiation the greenhouse represents a horizontal surface equal to its floor area, (b) the convective heat transfer from the air to the greenhouse wall is proportional to the wall surface area and to the mean temperature difference between inside and outside air (AT) (c) the net radiation in the greenhouse is entirely absorbed at the ground surface, and (d) horizontal radiative transfer is negligible. It was also implicitiy assumed that no major system elements were responsible for significant heat storage. Businger's technique was to write energy balance equations for several locations in the greenhouse that were then solved simultaneously. While these early studies were based on six or seven unknowns, recent studies, attempting to be more realistic with the help of large computers, have solved up to 31 simultaneous equations (Takami 1977). A recent review of the energy balance method for greenhouses was done by Walker (1983). F ig . 4 illustrates the typical energy fluxes that are considered. The energy balance can be written, 17 Figure 4: Schematic Illustration of Typical Greenhouse Energy Fluxes. QT + + Q + Q F = (Q + Q ) + Q + Q. + Q. + Q . . . . [1.3] I e r f c g i t p where, Q i net solar input equipment heat Qr heat of respiration furnace heat = convective heat flux Q g = heat flux to ground % - ventilation heat loss 18 Q . = infiltration heat loss 1 Q t = thermal radiation to sky Qp = heat of photosynthesis Several terms represent a negligible energy flux. The heats of respiration and photosynthesis are often ignored, amounting to less than 3% of the incident solar radiation (Walker 1978). The heat flux down into the ground is also very small compared to upward convective, conductive, and radiative losses (Horiguchi 1979). Morris (1967) presented the following breakdown of the major upward heat losses: conduction/convection 42%, radiation 33%, and latent heat losses by evapotranspiration 25%. The heat from equipment, such as lights and fans, is small and is usually combined with Similarly Q y and Qj are quite often lumped together. Relationships can be developed for each of the remaining heat fluxes as functions of several environmental and physical factors. These expressions can be substituted into equation [1.3] to solve for Q^, the greenhouse heating requirement More detailed discussion of these individual relationships can be found in Chapter III:B. Businger's steady state analysis was adequate for determining greenhouse heating requirements. It could not, however, predict transitional changes in interior and exterior temperatures or the influence of thermal storage in the greenhouse structure, the ground, and the plants themselves. Throughout the 1970's much effort was devoted to developing dynamic models that would solve these problems. In particular, some researchers went to great lengths to model Q (Avissar 1982, Kimbal l 1973, Kindelan 1980). Takakura (1971) devised a very sophisticated dynamic model which was soon followed by many others. Kimbal l (1973) allowed for multiple glazings in his model. Takami (1977) added improved equations for crop dynamics. In 1980 Kindelan 19 addressed the problem of soil heat storage with a new model incorporating Fourier analysis. In one of the most recent dynamic models, Avissar (1982) added several new refinements. He considered variations in soil moisture and homogeneity, non-linear evapotranspiration and radiative fluxes from the crop, and humidity. His model also simulated the use of thermal curtains. It would appear that the complexity of greenhouse mathematical models is a function of the power of the computers used to solve them. Van de Braak (1981) expressed the fear that the overwhelming complexity of modern computer models would cause investigators to lose sight of basic principles. He presented a simple hand-calculated energy balance based on electrical network theory. Economically Optimizing Models Very little work has been published that includes economic or financial constraints in the greenhouse plant/environment model. Perhaps inclusion of these constraints wil l be the trend for the 1980's. Seginer (1980) proposed a model that optimizes indoor temperature for maximum economic return from the crop. His scheme was divided into two phases. In the first part he determined the economically optimal set of environmental conditions using linear programming techniques: It is seen that, in general, the economical optimum does not coincide with either maximum production or optimum temperature. The location of the optimum depends on the cost of energy and the outside conditions through their effect on iso-cost lines.5 In the second phase a microcomputer controller algorithm was developed for a specific greenhouse and crop that maintained the environmental conditions at the economically optimal levels. Seginer expressed the economically optimal conditions with the equation, d V / d T = C(dE/dT) [1.4] where, V = Market value of the crop ( $ / m 2 planted area) 5Seginer 1980. 20 E = Energy supplied by the heating system ( J / m 2 planted area) T = A i r temperature inside the greenhouse ( ° C ) C = Cost of energy ($/J) His objective was that equation [1.4] be satisfied by the end of every growing season. Similar work was done by Challa et. al. (1981). In his approach, the monetary value of the crop was also related to the onset of production (see equation 1.1). Heat loss was expressed simply as a function of windspeed. A n experiment was conducted whereby a cucumber greenhouse's temperature was controlled by a microcomputer using Challa's economically optimizing strategy. He reported . that the benefits of this technique were difficult to quantify in traditional greenhouse cultivation. Seginer (1981) published the results of experiments that used his system to control thermal curtains and vents for a carnation greenhouse in New York. The system would, for example, keep the curtains closed for some time after sunrise on cold mornings to balance the positive effect of increased light and the negative effect of increased heating costs. Computer control of greenhouse environments is becoming more cost effective and many units are being installed. Optimizing algorithms such as Seginer's and Challa's are desirable because they can be programmed for optimal return on investment, rather than maximum production, hence they greatly increase efficiency. Capital Budgeting In a Monsanto study to determine what reasons farmers gave for practicing soil conservation, it was reported: "Research indicates that farmers alter their tillage techniques for economic reasons, overwhelmingly over any other reason, including erosion control" (Collins 1982). Approximately 65% of those polled cited reasons of economics. Similarly with energy conservation, farmers may prefer financial analyses to engineers' physical analyses of energy saving measures. 21 A recent review of the energy conservation literature commissioned by the B .C. Greenhouse Vegetable Growers Association noted, "Few energy conservation reports have analysed the cost effectiveness of the measures, especially using actual fuel use data." (Bryenton eL al. 1983) A procedure was presented for determining annual "normalized" fuel costs for B .C . greenhouses with and without energy saving measures. However, there was no attempt to formally calculate the return on investment Unt i l very recently financial analysis of greenhouse energy saving measures has been very crude. Few energy use engineering studies consider it at all. Most of these publications focus on the physical properties of materials and the effect of energy saving measures on heat loss (White et. al. 1980). Studies that do consider financial aspects, conduct simple payback period analyses that do not consider the time value of money, the escalation rate of fuel prices, nor the true annual costs of owning, operating and maintaining energy saving measures. The preferred financial analysis technique used to evaluate investment decisions involving fixed assets is known as Capital Budgeting: The term capital refers to fixed assets used in production, while a budget is a plan detailing projected inflows and outflows during some future period. Thus the Capital Budget oudines the planned expenditure on fixed assets, and Capital Budgeting is the whole process of analysing projects and deciding whether they should be included in the capital budget 6 There are five conceptual steps that are used in capital budgeting: 1. Future cash flows due to the project are estimated. 2. Riskiness of the projected cash flows is estimated. 3. A n appropriate discount rate is chosen. 4. Future cash flows are discounted to net present value ( N P V ) over the lifetime of the project 5. The calculated N P V is compared to the cost of the project and i f the N P V exceeds the cost the project is accepted. 6 Brigham 1983 22 A considerable problem in capital budgeting is the correct estimation of the three major unknowns: cash flow, riskiness and discount rate. A great deal has been written about these subjects. Statistical techniques are often used to arrive at appropriate numbers. Probability functions of the known or expected distribution of values can be developed by a variety of techniques (Hillier 1969, Moore and Chen 1983, Saxena 1983). Some of these methods even allow for an expected learning curve in the estimation of future cash flows (Harpaz 1984). After it has been estimated, the riskiness of an investment can be incorporated into the discount rate. The choice of an appropriate discount rate for present value analysis is a subject under constant debate in the financial community. However, its accurate determination is not absolutely essential for a comparative analysis such as this thesis. A capital budgeting technique was presented by White et al. (1980) to evaluate the financial viability of energy conservation in greenhouses. White's investment model was designed specifically for thermal curtains. The cash flows included only the costs and benefits (after tax) that resulted from the use of the curtains. H e therefore described his technique as partial budgeting. The N P V was calculated for the life of the thermal curtain (10 years) under 100 randomly generated scenarios of varying fuel price, amount of energy saved, fuel escalation rate and crop yield changes. White reported positive N P V in every case, but this may be due to his choice of relatively low system cost ($21.50/m 2 ) and low discount rate (5%). In addition, because this was an American study, an allowable energy tax credit was taken for the installation cost of the thermal curtains. A similar discounting technique known as Life-Cycle Costing has been used in energy use studies of buildings (Marshall and Ruegg 1977, B .C. Hydro 23 1980). However, capital budgeting is more thorough and better suited to the many cost variables in greenhouse energy conservation analysis. It also lends itself to the interactive computer spreadsheet modelling technique that is developed in this thesis. Computer Simulation "Simulation is the process of designing a model of a real system and constructing experiments with this model for the purpose either of understanding the behavior of the system, or of evaluating various strategies for the operation of the system." (Shannon 1975) Most computer simulations of greenhouse systems have been of the former type. The large dynamic models developed by researchers such as Kindelan and Avissar usually are created to explain the behavior of one type of energy saving measure. The subject of this thesis is a model designed to compare 10 different strategies for energy conservation in greenhouses. As Bronson (1984) stated, simulation techniques are used "to provide structure and definition to imprecisely worded verbal descriptions, to identify key components, to quantify interactions, and then to code, experiment and recommend." The complex environmental effects and sometimes artfull nature of greenhouse growing can benefit from the structure and definition that simulation provides. N o model is perfect However, neither are the systems that are being simulated. Nevertheless, the excercise of formalizing the problem itself can lead to a better understanding of the system. Many computer simulation studies of energy use in buildings have been developed by A S H R A E and others as design tools for mechanical engineers. Programs such as B L A S T , D O E , and C A L - E R D A all originated from early work done in the 1960's at Canada's National Reseach Council and the U.S . National Bureau of Standards. Early versions tended to emphasize accurate calculations of the building 'skin' 24 losses while neglecting the inefficiencies of the heat extraction systems. These programs are aimed at the commercial building market They are too complex in areas of little relation to greenhouses and not sophisticated enough in dealing with solar heat gain, methods of controlling and storing solar heat, and simulating heat radiation back to the cloudless sky (Bycraft 1983). Several recentiy published simulations were created when the concept of solar heating became popular in the 1970's. Programs such as W A T S U N (Chandrashekar and Wylie 1981) and E N E R P A S S (Enermodal Engineering Ltd 1982) were designed to estimate passive solar heat contribution to buildings or for the sizing of mechanical solar heating systems. These programs were typically written in F O R T R A N for large mainframe computers. A recent greenhouse energy balance model using the G A S P - I V computer simulation language was designed to compare the 'degree-day' method with a more complicated hourly weather data heat loss analysis (Duncan et al. 1981). Some engineering design studies employ the 'degree-day' method to estimate the building heating load. In this technique a representative indoor temperature is chosen — usually 18 ° C . The differences between this and the average daily mean temperatures are summed for all the days of the year. The resulting number of degree-days is proportional to the annual heating load. This method is less appropriate for greenhouse studies because growing environment conditions are more variable than in ordinary buildings. Duncan reported greenhouse heating requirements 9% less than calculated by the conventional degree-day method. This was corroborated with experimental data from real greenhouses. His energy balance was simple and took advantage of G A S P ' s ability to simulate both discrete (e.g. thermostat settings) and continuous functions (e.g. evapotranspiration). H e also used the program to study the effect of thermal curtains on heating requirements and reported a 17% reduction. In summary he stated, " A 25 simulation model describing the greenhouse energy system offers the potential for evaluation of numerous conservation designs and management practices to determine the more feasible alternatives." Several heat loss simulation programs have been written for microcomputers, the most notable being the National Research Council's H O T C A N (Dumont eL al. 1982). This residential simulation model has been used successfully in public information programs touring across Canada to demonstrate the potential for energy conservation in the home. Many complicated greenhouse simulation studies have been developed on large computers but few simple models for use on microcomputers have been reported. Some companies offer microcomputer financial accounting packages specifically for the greenhouse industry (e.g. Ball Technical Service 1983) but these do not include heat loss calculations or capital budgeting analysis. Dempster (1983) described the successful use of VisiCalc microcomputer financial spreadsheet models as tools for advising English greenhouse growers. II. D E V E L O P M E N T O F A G R E E N H O U S E S I M U L A T I O N M O D E L "Probably the most important management fundamental that is being ignored today is staying close to the customer to satisfy his needs and anticipate his wants. In too many companies, the customer has become a bloody nuisance whose unpredictable behavior damages carefully made strategic plans, whose activities mess up computer operations, and who stubbornly insists that purchased products should work." 7 A. Introduction The project was undertaken in three stages. First a questionnaire was used to determine the state of energy conservation in B .C. greenhouses and how growers perceived the problem. Secondly, a computer program in the form of two Multiplan templates was created to model changes in heat loss and cash flow due to energy conservation in commercial greenhouses. The form and content of the model was strongly influenced by the preliminary fact finding stage. Finally the model was checked, corrected and verified using data from the questionnaires and B.C. Hydro records. The Questionnaire Early in the project development, a questionnaire was devised for use when interviewing greenhouse growers. A copy can be found in Appendix A . Initially the purpose of the questionnaire was to standardize data collection during informal interviews with commercial greenhouse growers. Ultimately it shaped not only the farmer's interview, but the form of the model's data-entry section. Also, data from some questionnaires was used to verify the heat loss section of the model. The systematic series of interviews with growers that was conducted in the summer of 1983 was very useful. By talking directly with growers, their needs and views on energy conservation became apparent O f the eighteen growers that were visited, fifteen questionnaires were completed. There were three flower growers, one 'Peters and Waterman, 1982. 26 27 forest seedling nursery, one lettuce grower, four tomato and six cucumber greenhouses. Early in the work it was decided that vegetable growers would receive more attention. The questionnaire concentrated on tomato and cucumber growers for several reasons. Growing these vegetables is more energy intensive than flowers, many of which have been bred for cool growth habit In addition the vegetable industry had recentiy put out a call for help with energy management problems (Mauza 1982). The vegetable growing industry is more standardized in cultural and marketing techniques, making it much more ammenable to computer modelling. Many of the answers to the questionnaire showed a high degree of variability for flower growers. Summary of Questionnaire Results Based on the questionnaire the average Fraser Valley greenhouse is about 4600 m 2 in size. The units are 2-3 meters high, connected at the gutters to form long and wide complexes. They are usually oriented with the roof ridges in a North/South direction. About half are made of single glass and half double polyethylene with single fibreglass walls. Most growers rate their greenhouses as being very airtight Approximately half are ventilated with large electric exhaust fans on one end wall, while the other half use natural ventilation through roof vents. The average growing temperature is 16.4+1.1 ° C at night and 2 0 . 3 ± 2 . 0 ° C during the day. In almost all cases the heating plant is one or a pair of gas fired hot water boilers. Although one or two operations ran all year round, most operated from mid January to the end of October, thus avoiding the coldest months of the heating season. Most growers expressed an interest in energy conservation but only a few had invested heavily in energy saving equipment They all felt that energy conservation might decrease yields and were critical of many North American studies that did not consider this important fact They were also concerned with the high cost of energy 28 conservation. Approximately one third of those interviewed had tried the low cost methods, such as insulating the North wall and putting a layer of polyethylene on the side walls. These growers were uniformly pleased with the results. One third had installed a microcomputer environmental control system partly for energy conservation, but also for reducing labour costs. These growers were also very happy with the new technology. Only one or two growers had installed specialized capital intensive energy conservation measures, such as thermal curtains, I.R. heating or stack-heat recovery units. They felt that their energy consumption was lower, but were vague about actual savings and pay-back periods. Conclusions Derived from the Questionnaire After visiting greenhouses and growers, the nature of the problem became clearer. Many studies had already shown the advantages of energy conservation in greenhouses. Although growers were aware of these facts, most had not taken advantage of the new available technology. They often stated that the greenhouse energy conservation studies had been done on research greenhouses that bore little resemblance to their own operations. Hence, they were skeptical of the results. Effects on crop yield repeatedly came into question. The other major problem area was the affordability of the investment in energy conservation. Growers wanted to know what would energy conservation cost them, roughly how much money could be saved in their greenhouse, and how much could they safely invest Thus the problem was not to show yet again that in general energy conservation in greenhouses was possible. Instead a method was needed that would encourage an individual to invest in energy conservation measures in his own operation. This finding was to shape the nature of the computer hardware and software chosen 29 for the project. The questionnaire exercise also helped in determining the essential data requirements for simulation purposes. B . Project Software and Hardware Microcomputers and Simulation Models In the last five years microcomputers have become ubiquitous analytical tools in universities, banks, businesses and government offices. Their ever increasing computational power and steadily decreasing price will ensure their proliferation. These trends have made microcomputers the most accessible computing tool available today. A n often neglected design criterion is the accessibility of a computer simulation model. Most greenhouse models reported to date have been created on large mainframe computers in universities or governments. They typically cannot be used on other machines and must be run by their designers (Bycraft 1983). In addition they are usually created specifically for one situation. (See for example White et. al. 1980, Kindelan 1980, Avissar and Mahrer 1982, Seginer 1981, Davis et al. 1981). This inaccessibility and lack of generality seriously limits the value of most existing greenhouse computer simulation programs. It has often been stated that models should be more transparent, simple to use, and widely available (Manheim 1981, Albright 1983, Dempster 1983). Dempster put it this way: Model building can be extremely complex or very simple. For accuracy, complexity is necessary yet very often the forecasts that are being made are based on such uncertain data that a simpler approach, although technically imprecise will still provide results of adequate precision. Therefore in any model building. . . simplicity is most important A simple model can be readily understood both by the model builder and by others. The logic within the model can be followed readily and the assumptions made remembered. One very good way to keep a simulation program both simple and accessible is by 30 the use of an electronic spreadsheet on a microcomputer. A Victor 9000 microcomputer was used in this project It is based on the Intel 8088 16-bit microprocessor and has 128K R A M (Random Access Memory). It was equipped with M S - D O S (Microsoft Disk Operating System), version 1.05. Microcomputer Spreadsheet Modelling Programs The single most influencial and widely used microcomputer program written to date is VisiCalc. It was created in 1978 by a frustrated M . B . A . student tired of recalculating financial scratch sheets by hand. Since then many improved "electronic spreadsheet" programs have appeared for use on microcomputers. A typical spreadsheet program is 64 columns wide and 256 rows long. Any row/column coordinate (or cell) can be referred to by any other cell arithmetically or trigonometrically (see fig. 5). Once the relationships between the individual cells of the spreadsheet have been established in the model, a change in any value which affects other values will instantly be updated. This gives the computer operator the ability to examine instantly the ramifications of many "what- i f?" situations by changing any value in the matrix. The arithmetical and logical relations between cells are hidden from the user, but the results are displayed in a very easy to read and flexible format The spreadsheet software chosen for this project is Multiplan version 1.05.8 It has emerged as the second most popular spreadsheet program in the world after Lotus 1-2-3 (Cobb et al. 1983). Versions are available for virtually any microcomputer from the Commodore 64 to the Apple Macintosh and the I B M - P C . It is very well designed and can be used by anyone with or without a programming background. Many universities, banks, businesses and government offices are already using Multiplan on their microcomputers. Copywrited by Microsoft, Bellevue, Washington, 1982. 31 WHAT GOES ON BEHIND THE SCENES Figure 5: How Spreadsheet Programs Work. The Multiplan greenhouse simulation model developed here could therefore be run on any of the millions of microcomputers that are now in use. The interactive "what- i f?" nature of Multiplan imparts a high degree of adaptability to the model. This allows individual growers to get answers specific to their own greenhouses. III. T H E S P R E A D S H E E T M O D E L A . Introduction Electronic spreadsheets like Multiplan do not actually do anything all by themselves. In this respect they are similar to programming languages like F O R T R A N or B A S I C . Commands must be entered in a systematic way to create a specific program. Spreadsheet programs are called models or templates. The resulting model can be stored on floppy disk and loaded by Mult iplan on any machine. The greenhouse spreadsheet model in this thesis is really two models: a data entry model, G R E E N D A T , and the analysis of heat-loss and cash-flow, G R E E N S I M . What would otherwise have been a very large model is broken into two smaller ones. Multiplan's model- l inking feature allows values to be transferred from G R E E N D A T to G R E E N S I M whenever G R E E N S I M is loaded. Program Overview Fig . 6 shows the organization and logical flow of G R E E N S I M . A l l of the data and many of the program's assumptions originate in G R E E N D A T and internal look-up tables. The basis of the heat-loss simulation is an energy balance. Simply stated, it is a summation of all the ways in which heat can escape from the greenhouse throughout the year. The calculations are based on data from the user and weather data, etc., from internal tables. The energy consumption of a simulated reference greenhouse serves as a baseline when evaluating energy management schemes. This is accomplished by calculating the greenhouse energy consumption twice: once in its original reference state as defined by the user, and again with the effects of proposed energy conservation included. These effects are simulated by applying adjustment factors based on observations reported in the literature. 32 33 COST ESTIMATOR • REFERENCE GREENHOUSE ENERGY BALANCE PARAMETER WEIGHTING ENERGY SAVING GREENHOUSE ENERGY BALANCE CASH FLOW DISPLAY Figure 6: Logical Flow of the G R E E N S I M Program. The cash-flow section of the model uses cost and yield estimates in a partial budgeting analysis. The N P V after 15 years and the break-even year for the investment are both calculated. The key results from the Financial and heat-loss calculations as well as the primary program variables are all displayed in one video screen. Because of the interactive nature of Mult iplan more than 20 variables can be changed by the user. O n recalculation, the results are immediately visible on the same screen. For this reason the "display" part of the model communicates bidirectionally with other program sections. In the following pages, details will be presented for each section of the program as described above and in Figure 6. 34 B. Input Variables G R E E N S I M receives data from two sources: the greenhouse grower via G R E E N D A T and from internal look-up tables. The user initially interacts with the simulation model through G R E E N D A T , a self-prompting annotated computerized questionnaire (see Appendix B). This method was chosen for its simplicity and ease of use. Concepts are explained as questions are encountered, requiring minimal back-up documentation. A l l the questions initially contain default answers, so the user need not answer every one. Multiplan is very user friendly and wi l l accept answers in free format numbers or characters. Because of its interactive nature it is very easy to go back to review or change answers to previous questions. Look-up Tables The nature of a comparative analysis requires that different values be assigned to many program variables for each scenario that is simulated. Multiplan has a look-up function that is ideally suited for this purpose. G R E E N S I M has a total of six tables (see Appendix C, line 160). These tables contain such information as heat and light transmission coefficients of greenhouse materials; mean monthly weather data for the Fraser Valley, B .C . area; A S H R A E design values for air changes in greenhouses; and costs of fuel, crops, and energy saving measures. In addition there is a table that summarizes the published effects of energy conservation measures on heat loss, crop yield, and light transmission. The use of look-up tables gives the program two levels of adaptability. The first allows individual growers to simulate their own type of greenhouse. For example there are eight types of greenhouse glazings and ten different energy saving measures to choose from. 35 The principle of information hiding is also employed by the use of look-up tables. A great deal of information about the weather and physical properties of materials is hidden from the user. He need not concern himself with countless bothersome details. In this way the task of modelling appears less daunting, and the program becomes more "friendly" and usable. However, this does not imply that the model has built in obsolescence or error. O n the contrary. The nature of look-up tables allows a second level of adaptability. A n experienced user can easily update and customize the program to changing needs. For example, by replacing the numbers in the weather table with similar values for another location, the model can simulate greenhouses in different climatic zones. Similarly, prices of materials can be updated to reflect different economic areas or future trends. Even the adjustment factors used to simulate the effects of energy saving measures may be updated should future research yield more accurate results. C . The Greenhouse Energy Balance The computer model in this project was adapted from the work of Walker et al. (1983) and Blom et al. (1982). A modified A S H R A E steady-state building heat loss model was used. It is calculated monthly using average weather data and published heat transfer coefficients for greenhouse materials. Parsons (1983) found monthly weather data adequate for the prediction of greenhouse heating loads. His results varied within 3% of hourly studies. In order to attain a level of complexity that would allow the model to be accurate yet widely usable, many simplifying assumptions were required. However, it should be noted that some of the same assumptions are employed by the most complicated greenhouse models (e.g. Kimbal l 1973, Takami and Uchij ima 1977, Avissar and Mahrer 1982). 36 Basic Assumptions The major assumptions are: 1. Wi th respect to radiation, the greenhouse presents a horizontal surface equal to its floor area. 2. The convective heat transfer through the greenhouse wall is proportional to the wall surface area and the mean temperature difference between inside and outside air (AT). 3. The net radiation in the greenhouse is entirely absorbed at the ground surface. 4. A l l masses and materials are horizontally and vertically homogeneous. 5. Optical properties of components do not vary during the simulation. 6. Heat storage in plants, internal air, and structural materials is negligible. The Energy Fluxes and Balance Equations The energy balance equation used in the model is a simplified version of Walker et_ al. (1983) given in Chapter I (see equation [1.3] and Fig. 4): Q I + Q f = Q g + Q v + Q i + Q t + Q c where all the Q's are in units of M J / m o . The equation is solved for Q f to determine the monthly heating load. Separate calculations for the daytime and nighttime losses (or solar gains) are calculated for each month. Convective and Thermal Losses Losses through the building skin, Q , were divided into three components: Q c c roof Q c walls a n d Q c northwair T h i s w a s t 0 a l l o w f o r wall and roof materials, installation of north wall insulation, and the use of a thermal blanket for the roof or wall only. A n example calculation for daytime heat-loss through the roof of the greenhouse for one month is: Q , . = A U ( A T , ) ( H . / 2 4 ) 2.628 [3.2] roof day r r day 7 v d ' 1 J 37 where, Q c r o Q j . ^ = daytime heat loss through the roof (MJ/month) 2 A - roof area (m ) r v ' U r = overall coefficient of heat transfer for the roof ( W / m ° C ) A T d a y = m e a n a v e r a 8 e monthly temperature difference of inside and outside air during the day ( ° C ) H ^ = average hours of daylight (hr) The constant 2.628 converts watts to MJ/month . This equation can be found on line 64 of the spreadsheet model (Appendix D) . It is repeated for all twelve months. The same formula was used for the other building surfaces with U and A values corresponding to those surfaces. The formula was also repeated to calculate nighttime losses using A T ^ and ( 2 4 - H d ) / 2 4 in place of their daytime counterparts. The program assigns U values from a look-up table of various glazing materials. These U values were taken from a recent A S H R A E Applications Handbook and include long wave thermal radiation losses ( A S H R A E 1982). Thus is included in the calculation of Q . ^ c Conduction Losses Conduction losses to the ground, Q , were approximated by a formula for perimeter heat loss at the foundation wall. Qg day = 2 " 6 2 8 P U p A T d a y ( H d / 2 4 ) [ 1 3 ] where, P = perimeter (m) U p = perimeter heat loss coefficient ( W / m ° C ) The perimeter U value was 1.39 W / m ° C i f perimeter insulation was used and 2.77 W / m ° C if not (from Blom et al. 1982). It was assumed that soil temperature near the surface was approximated by the air temperature. This equation was also calculated 38 for the night component in the same manner as convective losses. Vertical conduction fluxes into the ground were neglected as they were assumed to average out over the year. Infiltration and Ventilation Losses Ventilation heat loss was ignored due to the difficulty of estimating periods of simultaneous ventilation and heating. In any case these periods would be relatively rare and would represent a very small portion of total heat loss. Ventilation with no heating would not affect total heat requirements. Infiltration losses represent unavoidable air leakage through structural cracks, and around closed doors and vents. Q. was divided into sensible and latent components as well as the usual monthly and day/night partitioning. Daytime sensible heat loss was calculated by the following formula: Q i sensible day = 0 3 3 3 V S A T d a y 2-628(^/24) [3.4] where, Q. . . . , = daytime sensible heat loss by infiltration (MJ/mo) I sensible day J v ' V = greenhouse volume (m 3) S = number of airchanges/hour ( h r 1 ) The constant 0.333 is the product of the heat capacity and density of air multiplied by conversion factors for h r 1 to sec - 1 and kilojoules to joules.' A i r changes are based on the age of the greenhouse, the type of glazing material and the user's estimate of leakiness. For example with an old glass greenhouse, S would vary from 2 to 4. For a new double polyethylene greenhouse S would range from 0.2 to 1 airchanges/hour ( A S H R A E 1982). A n S value from the appropriate range is interpolated using the farmer's estimate of leakiness. 9 Assuming an average greenhouse air density of 1.19 k g / m 3 and an average specific heat for greenhouse air of 1.009 k J / k g ° C . 39 Latent heat losses were divided into day and night components. It was assumed that daytime latent heat losses could be approximated by the evapotranspiration of the crop. Walker et. al. (1983) cited several studies that showed a strong correlation between the energy associated with evapotranspiration and radiation impinging upon the crop. His suggested ratio was 0.5. Thus, Q . , . , . = 0.5 Q [3.5] ^ i latent day ^ i L J where, Qj l a t e n t d a y = latent daytime heat loss (MJ/mo) A t night, when there is no solar input, latent heat loss is based on average vapour density difference between inside and outside air. Using Tetens' formula to approximate the psychrometric curve, [7.5T/(T+237.3)] V T * = (1322/(T+273.2)) • 1( where, T - 10 [3.6] Wj* = saturation vapour density at temperature T (g/m 3 ) T = average night temperature ( ° C ) Saturation vapour densities were calculated for both inside and outside conditions. Latent heat loss was then determined from published monthly average relative humidity values outside (Environment Canada) and inside the greenhouse (Mastalerz 1977), as follows: Q . , t t . . = 1.789 V S(V *. R H . - V * R H Y 2 4 - H ,)/24 . . [3.7] ^ i latent night T in in T out out / v d ' L J where, ^ i latent night = m 8 n t u m e latent heat losses (MJ/mo) V - p * ^ = saturation vapour density inside greenhouse (g/m 3 ) ^ T * o u t = s a t u r a u o n v a p o u r density outside greenhouse (g/m 3 ) R H - n = relative humidity inside greenhouse R H Q u t = relative humidity outside greenhouse 40 The constant 1.789 is the latent heat of vaporization of water multiplied by conversion factors for watts to M J / m o and grams to kilograms. As a check on Walker's coefficient of 0.5 in equation [3.5] Tetens' formula was also used to approximate daytime vapour densities inside and outside of the greenhouse as in equation [3.7]. The results are discussed in chapter IV, section A . Net Solar Radiation The monthly average mean daily solar radiation on a horizontal surface was found in published tables (Duffie and Beckman 1980). The following formula was used, Q j = 30.4 A Q H t f [3.8] where, A = floor area (m 2) Qpj = monthly average mean daily solar radiation on a horizontal surface ( M J / m 2 day) t = short wave transmissivity of the glazing f = shading factor for framing members and reflection, etc. (Businger 1963). The f factor in the program is set at 0.7. The constant 30.4 represents the average number of days in a month. The organization of the above equations on the spreadsheet is given in Appendix C , the program output listing. Appendix D is a detailed listing of all the underlying mathematical and logical relationships in the model. The model totals the heat loss or gain for every month, but for months when the daily solar energy input is greater than daytime losses, the daytime total Q^. is set to zero. This is typical of months like July and August During hot periods commercial greenhouses vent excess heat There is little or no heat storage, so it is not counted in the model. 41 The user can specify the heating season as beginning in one month (e.g. February) and ending in another (e.g. October). Only the monthly heat losses for the specified heating season are totalled. The results are presented in several ways: 1. Heat loss per unit area ( M J / m 2 ) 2. Annual heating costs (heat load * fuel cost/boiler efficiency) 3. Heating costs per unit area ($ /m 2 and $/ft 2). Energy Saving Greenhouse Simulation G R E E N S I M calculates two energy balances: one for the reference greenhouse, and one for the same greenhouse with simulated energy saving measures. Table 3 indicates how energy conservation measures affect certain program parameters during the second calculation. Some weighting factors were derived from the published properties of materials (overall U values and transmissivity). Others were averages taken from published empirical studies (heating efficiency, yield, infiltration rate). This was necessary because to date no satisfactory theoretical relationships have been developed for these factors. In cases where more than one energy saving measure affects the same parameter the effects are cumulative. For example the U w a ^ value in a greenhouse with both 1 meter height wall insulation and polyethylene over glass would change by: 1 - [(1-0.26)(l-0.32)] = .497 or 50% not 58%. D. Economic Analysis Crop Yield and Value Crop yield per unit area and market price of the crop are stored in a l o o k - u p table. They are historical averages based on data from the local Greenhouse Vegetable Marketing Board. The annual gross revenue of the greenhouse is estimated from these values based on its floor area. Due to the nature of spreadsheet software, 42 Table 3: Effects of Energy Saving Measures on Energy Balance Variables. ENERGY SAVING INFILTRATION MEASURES RATE U walls U roof U north wall NET RADIATION HEATING EFFICIENCY YIELD Rootzone heating — - — — — + .15 + .02 e Stack Heat Recovery - - - - — + .12 -Infra-red Heat - - - - -.05 e + .25 -.03 e Microcomputer + .12 + .05 1 controls Sealing Glass Laps -0.50 e - - -- - -Poly on Class -0.60 e -0.32 -0.32 - -.14 ~ -.10 2 Nwall Insulation -0.07 e - - -0. .83 -.05 e - -.005 e One meter wall lnsul -0.02 e -0.26 - - -.01 e -Thermal Curtains -0.10 e -0.53 -0.53 -0. .53 -.04" -.05 3 (effects only at night) Heat Storage - - - - - + .25 + .05 Kowalskl 1984 e denotes estimated value 2 Baurle 1978 Average of Stokes 1981, Verhaegh 1981, Grange 1983 " Stokes 1981 the user can correct this estimate, replacing it with actual yield or revenue from personal experience. Capital Budgeting The cost-benefit analysis uses a capital budgeting technique that examines both the positive and negative effects of the energy conservation project on future cash flow. The incremental annual project cash flows are those costs and benefits that the greenhouse grower will have left after deduction of al l Financing costs and taxes: A C F t = ( 1 - T x ) (AS t - A K t - A C { - A y + T x (ACCAp - P t [3.9] where, A C F t = net change in annual cash flow for year L T x = federal and provincial tax rate. A S t = change in annual sales revenue due to energy conservation. 43 A K t = change in annual expenditure on energy A C t = change in annual cost of maintenance due to energy conservation AIj. = change in annual interest payments due to loan for energy conservation equipment A C C A j . = change in Capital Cost Allowance due to energy conservation equipment P = annual portion of principal on loan for energy conservation equipment A l l variables are in dollars except the tax rate. Typically A C , A l , P and A C C A have positive or zero values, while A K is negative. A S may be positive or negative. O n the spreadsheet the cash flow is calculated in a table starting at line 140. A typical cash-flow is shown in Table 4. The component values in the cash flow analysis can be thought of as means taken from statistical distributions. For example A K , the change in annual energy costs, may be quite high or very low depending on dozens of factors. However, it would tend to follow a probability distribution that peaks at the mean value. By determining the relative "tightness" of a variable's probability curve, one can obtain an indication of its riskiness. Large computer simulation programs are available that can simulate distribution curves for each cash flow component and produce a probability curve for the resulting N P V . A much simpler method is to let the discount rate in the N P V calculation reflect the project's riskiness: the higher the discount rate, the riskier the project The elevated discount rate effectively devalues the future cash-flow of the project These reduced future returns reflect the probability that something will go wrong with the system, making it less efficient or more costiy than planned. By handling risk in this fasion the farmer can see it as cash out of pocket This is easier to understand than probability distributions. 44 Table 4: Sample Cash Flow Analysis. E. $'s saved/year : $13,715 Loan Payment: $2,907 annually Change in Sales/yr: -$1,207 TEAR •/- SALES ENERGY $'S SAVED MA I NT LOAN BAL INTEREST PRINCIPAL CCA CASHFLOW DI SCOUNTED TEAR NUMBER -$17,786 -$17,786 1984 -$845 $9,601 $260 $17,000 $1,785 $357 $1,043 $7,397 $7,397 1 1985 -921 10,561 283 16,642 1 ,747 410 939 8,137 7,201 2 1986 -1,004 11,617 309 16,231 1 ,704 472 845 8,972 7,026 Break-Even 1987 -1,094 12,779 336 15,759 1 ,654 543 760 9,910 6,868 4 1988 -1,193 14,057 367 15,215 1,597 624 684 10,958 6,721 5 1989 -1,300 15,462 400 14,591 1,532 718 616 12,127 6,582 6 1990 -1,417 17,009 436 13,872 1,456 826 554 13,426 6,449 7 1991 -1,545 18,709 475 13,045 1,369 950 499 14,868 6,319 8 1992 -1,684 20,580 518 12,095 1,270 1 ,092 449 t6,464 6,193 9 1993 -1 ,835 22,638 564 11,002 1,155 1 ,256 404 18,230 6,068 10 1994 -2,000 24,902 615 9,745 1,023 1 ,445 363 20,181 5,945 11 1995 -2,180 27,393 671 8,300 871 1 ,662 327 22,334 5,822 12 1996 -2,377 30,132 731 6,638 696 1 ,911 294 24,709 5,700 13 1997 -2,591 33, 145 797 4,726 496 2, 198 265 27,327 5,579 14 1998 -2,824 36,460 869 2,528 265 2,528 238 30,211 5,458 15 brk even yr: 3 Net Present Value: $77,548 Provisions for new equipment financing were included in the model because this is a true cost of energy conservation that is overlooked in most studies. One of the program's input parameters is the amount of money the user expects to borrow. The total installed cost of the energy conservation measure is estimated by the program on a square meter basis. Thus the grower's contribution is the difference between the installed cost and the loan amount The annual loan payment is based on a 15 year term with interest rate set by the user. Several of the annual cash flow components are inflated in future years. The values for AS and AC increase at the expected inflation rate, while AK escalates at an escalation rate typically 1 or 2% above the inflation rate. The analysis covers a span of 15 years which is roughly equivalent to the expected life of the energy conservation measures. 45 Calculation of Net Present Value Net present value of the project after 15 years is calculated from the cash flows using the formula: I s 1 ; A C F N P V = 22 ~. ~ IC [3.10] t = l <1 + V where, ^ = appropriate risk adjusted discount rate A C F t = net annual change in cash flow for year t IC = grower's contribution to the installed cost The discount rate used must reflect the uncertainty of the future cash flows from the energy conservation project One economic model often used in Financial risk analysis to estimate the appropriate risk adjusted discount rate is the Capital Asset Pricing Model ( C A P M ) (see for example Brigham et al. 1983). The risk adjusted discount rate in the C A P M is: r = r , + P(T - r f ) [3.11] p f v m f where, Tj. = riskless discount rate r m = expected market return j3 = beta coefficient of risk The riskless discount rate, which is equivalent to the one year government securities rate (=12% in 1984), reflects the minimum return on any investment 1 0 The second term, ^ ( r m _ r f ) . * s a risk premium designed to adjust the discount rate upward for riskier projects. The market price of risk, ( r m~rp> reflects the overall level of risk aversion in the economy and has a historical average value of =8%. The beta coefficient of risk is a weighting factor that reflects the relative riskiness of a See page 8 of the User's Manual in Appendix E for a more detailed explanation. 46 particular venture compared to an investment in a well diversified securities portfolio. A B value of 1 would be assigned to a project that had average risk. Values less than 1 reflect low risk while a B of 0 would imply no risk. Similarly, a B value of 2 would indicate a project was twice as risky as the market average. A n attempt was made to determine a B value for energy conservation investments. A market index 1 1 is published for ten companies on the New York Stock Exchange that specialize in conservation and solar energy technologies. It was felt that investments in these companies would have levels of risk similar to investment in greenhouse energy saving technology. The B value is defined by the following regression equation, r g - r f = a + / 3 ( r m - r f ) [3.12] where, r g = % monthly return of solar stocks Tj. = % monthly riskless rate of return on 90 day treasury bills TM - % monthly return of a well diversified market portfolio The market rate of return, r , was calculated from the Standard and Poor's 500 m market index, (r - r r ) was regressed against (T-TX The B coefficient of risk for s t a ° v m t conservation technologies is the slope of the resulting line. For a sample of 23 months (Apr i l 1982 to March 1984) the calculated B was 1.46 but statistically different from zero at only the 10% level. 1 2 When B cannot be determined objectively by statistical methods, the best alternative is the subjective estimate of the farmer. The risk adjusted discount rate would probably range from r f up to =*r f +1.5(r m -r f ) (12 to 24% in 1984). This discount rate seems rather high because unlike others, it includes the extra risks associated with energy saving technology. This is often a concern to greenhouse growers 1 1 The Lynch Solar Index, prepared by J. Peter Lynch, White Plains, N . Y . , published in Renewable Energy News, Washington, D . C . 1 2 F test = 2.84, T test = 1.69, n = 23 47 and is therefore included in the analysis. Again, due to the interactive nature of Multiplan, the discount rate can be set by the user to any value to reflect the perceived risk of the energy conservation measures in a particular greenhouse. Some energy saving measures such as sealing glass laps or meter height insulation are inherently less risky than complicated and expensive devices like stack-heat recovery units. Nominal rates have been employed throughout the analysis for the sake of convenience. It is expected that nominal interest rates would be more comprehensible to the average greenhouse operator because they are used in everyday conversation. Nominal rates were also necessary because the after-tax cash flow is generated with two separate inflation rates: one for fuel cost and one for all other inflatable costs. These rates are set by the user and would be difficult to estimate in real terms. In addition to the N P V , the economic analysis indicates the break-even year of the investment. This is the point in time at which the cumulative discounted future project cash flow is equal to the initial investment In practice most energy conservation projects are rejected i f their break-even point is greater than three years. The conservatively high discount rates suggested for use in the model, which cause generally lower values of N P V , would not influence decisions favouring quick pay-back projects. IV. V E R I F I C A T I O N A N D T E S T I N G O F T H E M O D E L There were two main objectives in testing the G R E E N S I M model. One was to determine how accurately it could model specific greenhouses. The other was to discover how the program would be received by the target users: extension workers, farmers, bankers, etc. A . Test of Model Accuracy Heat Loss Predictions Using data from the 1983 greenhouse survey questionnaires, seven greenhouses were simulated by G R E E N S I M . Each had a different set of input variables, some of which are summarized in Table 5. The weather table was loaded with meteorological data for 1983. Actual energy consumption of the seven farms for the 1983 heating season were obtained from B.C. Hydro records. These are compared with the computer simulated energy use in Table 6. Although error ranged from -30% to +28% the average error was essentially zero with standard deviation of 21.8%. To check the model's estimate of daytime latent heat loss a special simulation was done using vapour density differences instead of Walker's simple approximation (equation [3.5]). The outside relative humidity was obtained from Environment Canada tables but the inside average daytime humidity had to be estimated. Two simulation runs with inside humidity set at 63% and 77% were conducted using the reference greenhouse as described in Table 7. With Walker's formula, annual latent heat loss (day and night) was estimated to be 4,830,000 M J and total annual losses per m 2 were 2460 M J . Using the vapour density difference method with greenhouse humidity set at 77%, annual latent heat loss fell to 1,973,000 M J but total annual losses per m 2 were 2415 M J , a difference of only 1.8%. If the inside humidity was set at 63% (perhaps more realistic) latent losses fell to 1,075,000 M J / y r and total losses were 2356 48 49 Table 5: Characteristics of Commercial Greenhouses used for Program Verification. C O D E E N E R G Y S A V I N G M E A S U R E S U S E D F L O O R A R E A R O O F (m 2 ) G L A Z I N G C R O P T Y P E D A Y / N I G H T T E M P ° C 1 1 meter insul, Computer 3846 Glass + poly Cucumber 23/18 2 1 meter insulation 2542 Double poly Cucumber 20/16.5 3 North Wal l Insulation 2956 Double poly Tomato 20/17 4 Poly-on-glass, computer, reduced leakage 4701 Glass Cucumber 22.5/17 5 Poly-on-glass, computer 4108 Glass + poly Cucumber 22/18 6 None 2492 Double poly Cucumber 23/20 7 None 1003 Glass Tomato 20/17.5 Table 6: Comparison of Actual and Simulated Heat Loss. C O D E A C T U A L * P R E D I C T E D * E R R O R 1 1386 1369 -1.2% 2 1568 1687 7.1% 3 2475 1932 -28.1% 4 2054 2158 4.8% 5 1606 2216 27.5% 6 2184 1677 -30.2% 7 1767 2140 17.4% Average Error: Standard Deviation: -0.4% 21.8% *A11 values in M J / m 2 / y r . 50 M J / m V y r , a change of 4.2%. In the model, Walker's estimate works as well as the more complicated vapour density calculation to estimate total heat loss. Although in terms of the latent portion of total heat losses, there appear to be large differences, it must be remembered that during hot summer months the model sets all daytime heat losses equal to zero i f incident solar energy is greater than daytime losses. It is during these months that the largest difference in calculated latent heat loss occurs so they are effectively ignored by the model. Financial Analysis Every greenhouse has a distinctive pattern of energy use. Farmers know in general that certain energy saving measures are better than others, but they still like to see which ones would be the best investment in their unique situation." To demonstrate how G R E E N S I M accomplishes this, a series of simulations was done showing the effect of size, structure, and cultural practice on N P V and break-even point of the investment. Table 7 summarizes the results. Most of the program's variables were held constant throughout all the simulations. These are listed at the base of table 7. The baseline or reference greenhouse was 4700 m 2 in size, fourteen years old, made of 3 mm glass throughout and was kept at 22° C by day and 18° C at night For the three other simulations these variables were kept the same except as follows: To simulate structural effects, the building skin was changed to a one year old double polyethylene roof and corrugated fibreglass walls. For cultural effects, nothing was changed from the reference case except for the day and night temperature setpoints. They became 20° C and 15° C , respectively. To model size difference a greenhouse ten times smaller than the reference case was simulated. 51 Table 7: Comparison of Energy Saving Investments in Four Types of Greenhouses. R E F E R E N C E 1 S T R U C T U R E 2 C U L T U R E 3 SIZE 4 H E A T L O S S ( M J / m J y r ) E N E R G Y C O S T ($/ft 2) 2683 1.00 1542 0.58 1915 0.72 3579 1.34 E N E R G Y S A V I N G $ N P V B / E 5 $ N P V B / E $ N P V B / E $ N P V B / E M E A S U R E S Rootzone Heat -25,759 N 6 -43,836 N -37,921 N -1,163 N Stack-Heat Recovery 23,848 4 8,999 7 13,857 5 -6,388 N I.R. Heating -49,905 N -76,588 N -67,686 N -2,839 N Computer Control 54,665 3 39,817 4 44,675 3 -10,101 N Reduced Leak 52,968 2 14,546 6 33,954 3 5,818 2 P o l y - o n - G l a s s -26,009 N -101,504 N -70,976 N -1,150 N N . Wal l Insulation 8,216 1 3,400 2 4,564 1 1,000 1 One meter Insulation 6,930 1 5,459 2 4,880 2 2,793 2 Thermal Curtain 35,285 7 3,900 14 9,058 12 6,948 5 Heat Storage -5,060 N -32,778 N -23,709 N 1,427 11 P R O G R A M V A R I A B L E S SET A T : Discount Rate: 18% Fuel Escalation: 9% Crop Type : Tomato Crop Yie ld : 2.45 cases/m 2 Fuel Price : .004049 $ / M J Leakiness (1 to 10): 5 Wal l Height: 8 feet Inflation Rate: 7% Tax Rate : 30% Crop Price: $11.54/case Fuel Type: Natural Gas Heating Season: February - October Orientation: North/South Perimeter Insulation: N o 1. "Reference" greenhouse was 4700 m 2 , 14 years old, made of 3 mm glass throughout, moderately leaky, and kept at 22° C by day and 18° C at night 2. "Structural" changes were air inflated double poly roof, corrugated fibreglass side walls, one year old. 3. "Cultural" changes: daytemp set at 20° C by day and 15° C at night 4. "Size" changes: Small greenhouse was 474 m J in size. 5. " B / E " means: Break Even year of investment 6. " N " means the investment never breaks even. 52 Depending on an individual's priorities, one may choose energy saving measures that maximize N P V or those that minimize the break-even time. In every case north-wall insulation and one-meter-height insulation had the quickest payback, but usually the lowest return on investment Note that it is more profitable to insulate the north wall of a large greenhouse than a small one. However, i f one has a reasonably energy efficient greenhouse, the effects of north-wall insulation are lessened and take longer to pay back. Computer-controls, reduced leakage (caulking glass laps), and stack-heat recovery often seemed like the best options but not in every case. For example, reduced-leakage is very good for the standard old glasshouse, but would be second choice in the cool greenhouse. Stack-heat recovery has a very good return on investment for an old warm glasshouse, is less attractive in the cooler and more efficient greenhouses and would be rejected by the small grower. These results compare very favorably with current patterns of investment in the greenhouse industry. This exercise serves to demonstrate an important lesson in energy management Each energy consuming system is unique and requires special treatment to optimize energy savings and financial returns. For example, in Table 7 the same discount rate was used for each energy saving measure. In reality every investment would have its own unique level of risk. This could be simulated by a user who could apply a separate discount rate for each energy saving system. Sensitivity Analysis Nineteen program variables were examined to see how sensitive the model was to their variation. Each variable was set at appropriate values below and above their levels in the so called reference case as described in Table 7. For each setting all other variables remained constant and the results in terms of N P V and break-even year were noted (see Table 8). 53 Table 8: Results of Sensitivity Analysis on Selected Program Variables. V A R I A B L E N P V ($) B - E 1 SENSITIVITY SENSITIVITY ( N P V ) ( B - E ) Discount rate 12% 68,075 6 18% 35,285 7 H i g h Moderate 24% 15,231 9 Fuel Escalation rate 5% 18,000 9 7% 26,196 8 Moderate Moderate 9% 35,285 7 Inflation 5% 36,285 7 7% 35,285 7 Low Low 9% 34,136 7 Tax rate 20% 45,000 7 30% 35,285 7 Low Low 40% 25,524 8 Fuel Price ($ /MJ) .003239 16,154 10 .00404907 35,285 7 High High .004858 54,390 6 Crop Yie ld (case/m 2) 1.96 36,821 7 2.45 35,285 7 Low Low 2.80 33,749 7 Crop price ($/case) 9.23 36,823 7 11.54 35,285 7 Low Low 13.85 33,748 7 Yield Effect Weighting Factor Rootzone 0.00 -41,119 N 2 + .02 -25,759 N Moderate Low + .04 -10,399 N Infra-red Heating - .05 -71,231 N - .03 -49,905 N H i g h Low + .01 -19,184 N Computer control 0.00 16,264 6 + .05 54,663 3 H i g h High + .07 70,026 2 Poly-on-glass - .05 12,515 12 - .10 -26,009 N H i g h High - .15 -64,410 N North Wal l Insulation 0.00 12,057 1 -.005 8,216 1 H i g h Low - .01 4,377 2 -.015 536 10 54 Table 8 : continued... V A R I A B L E N P V ($) B - E 1 SENSITIVITY SENSITIVITY ( N P V ) ( B - E ) Thermal Curtains - .01 35,285 7 - .05 4,564 14 High High - .10 -33,837 N Infiltration Effect Weighting Factor Sealing Glass Laps - .70 79,320 2 - .50 52,968 2 High Moderate - .30 26,617 4 Poly-on-glass - .70 -26,009 N - .40 -65,536 N High Low North W a l l Insulation - .05 1,627 4 - .07 8,216 1 High High - .09 15,137 1 One-meter Insulation 0.00 4,296 - .02 6,930 1 Moderate Moderate - .04 9,566 1 Thermal Curtains - .05 29,653 - .10 35,285 7 Moderate Low - .15 40,918 7 N O T E S : 1. B - E indicates break-even year. 2. N indicates project never breaks even. With thermal curtains being simulated (the first seven variables in Table 8) the model was very sensitive to discount rate, fuel escalation rate and fuel price changes in the reference case. However, it should be noted that the sensitivity of the model would be different under alternate simulation scenarios. For example, energy saving measures with lower capital costs such as north wall insulation V o u l d not be affected as much by these economic indicators. In the reference case the model showed low sensitivity to inflation rate, crop yield and crop price fluctuations of ± 20%. However, had the yield effect variable been set higher, as in other possible scenarios, the latter parameters would have shown 55 greater sensitivity. The yield effect variable found in the energy saving measure look-up table (Table 3) was studied for the six energy saving measures that it affected. In cases where the investment had a positive N P V the model was very sensitive to changes in the yield effect This fact underlines the importance of getting good estimates for the yield effects of energy saving measures and explains why greenhouse operators are so concerned about crop yield effects. The infiltration weighting factor in Table 3 was also investigated. The model was only moderately sensitive to variations as high as 100% in the infiltration weighting factor variable. This is probably due to the relatively small contribution of infiltration losses to total annual heat loss. Again, sensitivity would be different under other simulation scenarios. For example, in leakier greenhouses, infiltration would become more significant This analysis illustrates the way the model can be used to determine the relative sensitivity of all the variables in a particular scenario. B. User Testing of the Model As a result of a presentation of this work given in August 1984 at the Canadian Society of Agricultural Engineering in Winnipeg, both federal and provincial government extension workers expressed considerable interest in obtaining a copy of the program for use on their own microcomputers. A diskette containing G R E E N S I M and G R E E N D A T as well as a printed user's manual (Appendix E) were given to an economist in the B .C. Ministry of Agriculture and Food. He was working on a Multiplan greenhouse enterprise model at the time. He and the departmental greenhouse specialist were both favourably impressed with the G R E E N S I M model. They were able to use it with minimal training and only three phone calls for technical support 56 In addition, the government economist had no trouble adapting the model so that the results of the energy balance were incorporated into his own greenhouse enterprise model under development In fact the G R E E N S I M model stimulated sufficient interest to encourage the ministry to propose the allocation of funds to further develop the model as an extension tool. The plan is to use the model on a portable I B M personal computer at trade fairs, branch offices and in the field. The object is to motivate growers to make use of more energy conservation measures and to educate them in energy management techniques. The above experience demonstrated three objectives of this project: 1. Supporting software (Multiplan) and skilled personel who need such "micro" models, exist 2. The model is easy to use. 3. The model is adaptable and transportable. The program was developed on a Victor 9000 computer, but the end-user ran the model on an I B M - P C . This proved the cross compatibility and machine independence of the model. C . Test of the Model with Greenhouse Operators Although the computer model in this project is designed predominantly for use by agricultural support workers, it is farmers themselves who must ultimately benefit from its use. Therefore an attempt was made to determine its adequacy among greenhouse growers. After the model was completed three Fraser Valley growers were interviewed in order to get input data for G R E E N D A T . Their greenhouses were later simulated at U . B . C . using G R E E N S I M , and the results were mailed back to the growers with an explanatory covering letter. After several weeks the three farmers were telephoned for their reactions. 57 The results were varied. One grower simply could not be bothered looking over the figures. The other two farmers were very pleased with the analysis. This was despite the fact that compared to actual energy consumption, G R E E N S I M predicted 30% less energy use in one case and 27.5% too much for the other greenhouse. Whereas it appeared that G R E E N S I M was gravely in error, surprisingly, both farmers justified the discrepancies easily. For example, an inefficient boiler, heated irrigation water, and an unusually strong northeast wind was used to explain the underestimated heat loss in the first case. The other extremely efficient grower revealed that he was using a radically low night temperature setback that worked in stages throughout the growing season. He noted that B .C. Hydro had sent people out to investigate whether he had been tampering with his gas meter. The growers found the computer printout overwhelmingly technical and difficult to comprehend. However, they both felt it would be very useful to have such information. One grower was convinced to install north wall insulation based on the economic analysis in G R E E N S I M . In November, 1984, the greenhouse simulation model was demonstrated to a greenhouse workshop in Prince George, B . C . Both the computer hardware and software ( I B M - P C and Multiplan) were obtained locally. G R E E N S I M was pre-loaded with Prince George weather and price data before the "hands-on" demonstration took place. A t first farmers were reluctant to volunteer their greenhouses for modelling purposes, but during coffee break a crowd formed around the microcomputer as a simulation got underway. The results were relatively accurate for the Prince George area and the growers were pleased to carry away printouts of simulations for later study. The most significant result of all the user testing was the way the modelling program acted as a kind of catalyst for communication. Individual growers were much more willing to reveal their cultural and management techniques once the simulation printout for their own greenhouse was in hand. It stimulated their thinking and 58 increased their confidence. In the workshop setting the model served as a focal point for discussion and learning. The relative merits of various energy saving measures were discussed as well as markets, fuel prices, interest rates, risk, and discounted cash flow analysis. Because of the model's interactive nature, these concepts could be demonstrated on the computer immediately, making them easier to understand. V . C O N C L U S I O N The advent of powerful popular microcomputers and their novel programs such as spreadsheets and newer integrated packages is a profound revolution in science and society. It has yielded a new level of capability for the individual to handle vast amounts of information. In a trade that requires a blend of art and science like agriculture, spreadsheets can, be a valuable productivity tool. Through their use, one can maintain a degree of practicality and objectivity while artfully combining and comparing a host of alternatives. G R E E N S I M is meant to be a decision making tool. It keeps track of a great deal of information. It makes it easy to access key variables and indicators in the greenhouse business; and it emphasizes energy saving measures. Energy use is reaching ever increasing levels in almost all sectors of the economy. As demand for energy increases, so will demand for energy conservation technologies.1 3 Resource management specialists must be able to communicate the potential application of new technologies to the end user. This project has demonstrated how microcomputer spreadsheet programs can be used in this process. A . Energy Use and Yield in B . C . Greenhouses A n attempt was made to repeat deVisser's (1981) study of Dutch greenhouses showing no correlation between energy use and crop yield. Thirteen Fraser Valley vegetable greenhouses were included. Y ie ld data was obtained from the Marketing Board and energy use data came from B.C. Hydro records. Figure 7 shows a plot of energy use versus yield. The correlation coefficient (r) was 0.52 indicating virtually no relationship between yield and energy use. This corroborates the Dutch study and indicates that there are many more factors influencing plant growth. The important observation, however, is that for any given level of crop yield, there are growers able to maintain productivity with greatly 1 3 Sales of energy management systems are expected to grow at the rate of 20% per year (Gessert 1983). 59 60 e 3 <—»•© CJ — PJ o \ 55 § CO s O o w 2 W 8 L o o " I 1 1 1 r • PLASTIC © GLASS O i 1 1 — i — i 1 1—g~ o J I I I I I I i L I i ' l _ 0.0 1.0 2.0 S.0 4.0 6.0 0.0 YIELD (CASES/M2/YR) 7.0 a.o Figure 7: Energy Use Versus Y i e l d in B .C. Greenhouses. reduced energy consumption. Conserving energy does not necessarily imply reduced yields. F ig . 7 therefore indicates the potential for energy conservation in many B.C. greenhouses. B . Value and Limitations of the Study The accuracy of the G R E E N S I M greenhouse simulation is largely dependent on the precision of the numbers in the look-up tables. Installation costs and crop values may change. In addition, continuing research wil l probably yield better values for the weighting factors in Table 3. A t present some of these values are estimates. Fortunately, due to the interactive nature of spreadsheet software, these numbers can be easily updated by any user. 61 The monthly basis of the energy balance is another limitation. This level of resolution is necessary to provide a quick interactive programming environment Unfortunately it means that i f a grower begins or ends his heating season in the middle of a month, it cannot be accurately simulated by G R E E N S I M . This is particularly important because the first and last weeks of the growing season are the coldest, representing a significant portion of the annual heating load. The model's monthly resolution also limits the scope of topics for investigation. Daily variations such as humidity, condensation, and ventilation cannot be easily studied. However other specialized spreadsheet models could be constructed to examine these phenomena on an hourly basis. G R E E N S I M was designed to present cash-flow for a fifteen year period, so hourly variations could not be included. The emphasis on energy conservation measures is another restriction of the G R E E N S I M model. In trials with farmers questions were raised about the optimum size of the greenhouse, best planting or harvesting time, boiler sizing, heating of irrigation water and C 0 2 enrichment With skill , one can make the model reveal more, but it simply cannot answer all these questions. Farmers are expressing a need for a more general greenhouse model that has broader scope. Another possible problem was the growers' impression that the model was too technical. They were hesitant to use it without plenty of guidance and explanation. Since G R E E N S I M is designed for agricultural support workers who are already skilled in the use of electronic spreadsheets, perhaps with appropriate documentation this wil l not be a major barrier. Future versions could use integrated software packages that present the results graphically. The 21.8% standard deviation of the heat loss simulation is reasonable, considering that the simulated greenhouses were not under experimental control. In some cases, for example, farm residential energy use may have been included in Hydro's energy consumption figures. Also effects of local microclimate and variations in 62 individual grower's management techniques could not be included in the simulation. In heat transfer problems it is not uncommon to have uncertainties of 20% (Holman 1981). The acceptable level of error under various input scenarios indicates this is a robust model that could be easily adapted to many research and extension applications. Just as N R C ' s microcomputer program, H O T C A N , was used to promote energy conservation in the home, so G R E E N S I M could be used in the agricultural sector. In addition, bank financing for energy conservation technology is difficult to obtain (Roberts and Mears 1981). The detailed cash flow projections produced by this model can be used to help secure such financing. C . Future Use of Greenhouse Spreadsheet Models In the coming years microcomputers will become more powerful. Programs like G R E E N S I M wil l be more accurate and allow more refined analysis. There may be expert system spreadsheets especially for greenhouses. They will find use in government and university extension offices where they can be used to advise and educate the public. In addition they may find their way into marketing co-ops, seed companies, agricultural supply houses, engineering and economic consulting firms. It is possible that G R E E N S I M will be used by B.C. Hydro or the B .C . Ministry of Agriculture and Food. They are expected to conduct an energy audit of commercial greenhouses in B .C . similar to one reported by Rynk et al. (1982) in the United States. Spreadsheet models may also become part of government decision making. For example the farm insurance program could use such a model to calculate annual premiums or benefits. Hilborn et al. (1984) recently described his experiences with spreadsheet models in federal fisheries policy making. He felt that microcomputer spreadsheet models were a "significant breakthrough in getting decision-makers to use 63 quantitative tools." Micros were cheaper and quicker to yield results than large mainframes. The spreadsheet model acted as a repository of data and concepts. Over a four year period it helped the changing personnel maintain continuity in the policy-making process. Banks also are beginning to use spreadsheet models of various "enterprises". For farmers they have developed models of swine barns, wheat fields, dairy herds, etc. These are used to assist the farmer and to determine the status of his venture for credit purposes. Spreadsheet models of greenhouses will probably be used for educational purposes as well. The physics of the greenhouse environment can be studied in addition to management techniques, economics, crop scheduling and nutritional requirements. It is possible that the future power and portability of microcomputers will make tomorrow's electronic spreadsheets as common as pocket calculators are today. References Albright, L . D . , Personal communication, May 1983 Aldrich, R . A . , Downs, R.S., et al. 1983 The effect of environment on plant growth, in Ventilation £ f Agricultural Structures. Ed . M . A . Hellickson, A S A E monograph #6, St Joseph, Michigan, p. 217 A S H R A E 1982, American Society of Heating, Refrigeration and Airconditioning Engineers Applications Handbook, Chapter 21.7 Avissar, R. ; Mahrer, Y . 1982, Verification study of a numerical greenhouse microclimate model, Trans, of Hie. A S A E . p. 1711 Badger, P . C ; Poole, H . A . 1979, Conserving Energy i n Ohio Greenhouses. Ohio Department of Energy Bauerle, W . L . ; Short T . H . 1978, Energy conservation and plant growth by using double plastic on glass greenhouses, Ada H o r t #76, p. 305 B.C. Hydro. 1980, A method for calculating optimum levels of insulation, Energy Use Engineering Department Energy Conservation Division, B .C. Hydro Data Sheet #H205 Blom, T.J.; Ingratta, F.J . ; Hughes, J. 1982, Eneigy Conservation i n OjrJaiiQ Greenhouses. Publication #65, Ontario Ministry of Agriculture and Food B o t G . P . A . 1981, Heating load of a glasshouse from the physical point of view, Acia Hon, #115, p. 335 Brigham, E.F. ; K a h l , A . L . ; Rentz, W.F . 1983, Canadian Financial Management: Theory and Practice. H o l t Rinehart and Winston of Canada, p. 340 Bronson, R. 1984, Computer simulation, Bvte. March, p. 95 Bryenton, R .W. ; Johnson, G . ; Schmalz, W . 1983, Greenhouse energy conservation project report British Columbia Greenhouse Vegetable Growers Research Committee Businger, J .A. 1963, The greenhouse climate, in Physics o f Plant Environment 2nd Edition, E d . W . R . Van Wijk, North Holland Publishing Co. , Amsterdam, p. 277 Bycraft, R.S., Chief, Computer Aided Design Centre, N . R . C . , Personal communication, March, 1983 Challa, H . ; Van de Vooren, J. 1980, A strategy for climate control in greenhouses in early winter production, Acta H o r t #106, p. 159 Challa, H . ; Bakker, J .C. ; B o t G . P . A . ; Udink ten Gate, A . J . ; Van de Vooren, J. 1981, Economical optimization of energy consumption in an early cuke crop, Acta F i e r i #118, p. 191 Chandrashekar, M . ; Wylie , R . H . 1981, W A T S U N - 3 : Solar heating simulation and economic evaluation program, The Energy Resource Group, Department of Mechanical Engineering, U . of Waterloo, Ontario 64 65 Cobb, D .F . ; Cobb, G .B . ; Henderson, T.B. 1983, Mult ipla i l Models fjor Business, Que Publishing, Indianapolis, Indiana Collins, D . M . 1982, Achieving cost effective conservation, 1 jaf S_oj] and Water  Conservation, vol. 37, no. 5, sept-oct Davis, B.; Swan, D . ; Jeffers, K . 1981, Design study of energy efficient greenhouses for intensive horticultural production, Nova Energy Ltd. , Dartmouth, Nova Scotia, A g . Can. contract #34SZ-01799-0-0550 Dempster, J . H . 1983, Financial computer models for glasshouse crops on microcomputers, Ada Hon... #135, p. 93 de Visser, A. J . 1981, Economic aspects of cucumber growing in the Netherlands, Ada H o r t #118, p. 11 Duffie , J .A.; Beckman, W . A . 1980, Solar Engineering £>f Thermal Processes. John Wiley and Sons, New York, p. 749 Dumont, R.S.; Lux, M . E . ; Orr , H . W . 1982, H O T C A N : A computer program for estimating the space heating requirement of residences, National Research Council of Canada, Computer Program # CP49, Ottawa, September Duncan, G . A . ; Loewer Jr., O.J. ; Colliver, D . G . 1981, Simulation of energy flows in a greenhouse: magnitudes and conservation potential, Trans, of Hie A S A E . vol. 24, no. 4, p. 1014 Enermodal Engineering Limited. 1982, E N E R P A S S , Enermodel Engineering Limited, Waterloo, Ontario Gessert, S. 1983, The race to sell energy saving systems, Business Week. May 23, p. 100 Grange, R.I.; Hurd , R . G . 1983, Thermal screens: environmental plant studies, Scientia U Q I L , vol. 19, no. 3-4, A p r i l , p. 201 Harpaz, G . ; Thomadakis, S.B. 1984, Project valuation with imperfect information, Engineering Economist, vol. 29, no. 2, p. 101 Hi lborn , R. ; Walters, C.J. ; Peterman, R .P . ; Staley, M . J . 1984, Models and fisheries: a case study in implementation, North American 1 £ f Fisheries Management 4:9-14 Hil l ier , F.S. 1969, J h £ Evaluation o j Risky Interrelated Investments, North Holland Publishing Co. , Amsterdam Holman, J.P. 1981, IJsat J j a n s f i a , 5th Edition, M c G r a w - H i l l , p. 79 Horie, T. 1979, A simulation model of cucumber growth to form bases for managing the plant environment system, Ada H Q I L #87, p. 215 Horiguchi, I. 1979, The variation of heating load coefficient for the greenhouse, Ada H o r t #87, p.95 Jackson, B. 1983, The nature of investment problems in energy conservation, Energy 66 World. December, p. 9 Kimbal l , B . A . 1973, Simulation of the energy balance of a greenhouse, Agric.  MeteoroL vol. 11, p. 243 Kindelan, M . 1980, Dynamic modelling of greenhouse environment, Trans, .of J&fi ASAE, vol. 23, p. 1232 Kowalski, R. 1984, Priva computer marketing manager, B.C. , Personal communication Liebig, H .P . 1981, A growth model to predict yield and economical figures of the cucumber crop, Acta Hort. #118, p. 165 Manheim, M . L . 1981, Ethical issues in environmental impact assessment, Environmental Impact Assessment Review, vol. 2, no. 4, p. 822 Marshall, H . E . ; Ruegg, R.T. 1977, Energy conservation through life-cycle costing, 1 of Architectural Ed. , vol. 30, no. 3, p. 42 Mastalerz, J .W. 1977, Jhs Greenhouse Environment John Wiley and Sons, New York Mauza, B. 1982, Solicitation for proposals to research B.C. greenhouse energy use, British Columbia Greenhouse Vegetable Growers Research Committee, Vancouver, B.C. Moore, W.T. ; Chen, S. 1983, The value of perfect information in capital budgeting decisions with unknown cash flow parameters, Engineering Economist vol. 29, no. 1. P- 41 Morris, L . G . ; Winspear, J. 1967, The control of temperature and humidity in glasshouses by heating and ventilation, Proc. Agric. Eng, Symp.. Silsoe, England Nehring, R.; Van Driest E.R. 1981, The discovery of significant oil and gas fields in the U.S. , Rand Corp. 1981, as reported by Bi l l S t John, Exploration and Economics Hie Petroleum Jjiajjstry, vol. 20, p. 1 Parsons, B . K . 1983, The simulation and design of building attached sunspaces, Masters Thesis, U . of Wisconsin, Madison Peters, T.J.; Waterman, R . H . Jr. 1982, ID Sfiaicli £f Excellence. Harper and Row, New York Roberts, N . D . , Anderson et al. 1983, Introduction I D Computer Simulation. Addison-Wesley, Reading, M A . Roberts, W.J . ; Mears, D .R . 1981, Energy use in greenhouses—how low can we go?, Acta H Q I L , #115, p. 143. Rynk, R ; Schrader, R ; L ight P . G . 1982, A n energy audit for commercial greenhouse growers, ASAE Paper m 82-4535. Winter Meeting, Chicago Saxena, U . 1983, Investment analysis under uncertainty, Eng. Econ., vol. 29, no. 1, p. 33. Scott, G . 1984, Managing Director, B .C . Hothouse Products, Personal communication 67 Seginer, I. 1980, Optimizing greenhouse operation for best aerial environment, Acta JJQ IL #106, p. 169-178 Seginer, I. 1981, Economic greenhouse temperatures, Ada Hort.. #115, p. 439 Seginer, I. 1984, Personal communication Shannon, R.E. 1975, Systems Simulation: Ine Alt and Science, Prentice-Hall , Englewood Cliffs, New Jersey Silveston, P.L. ; Costigane, W . D . ; Tiessen, H . ; Hudgins, R .R. 1980, Energy conservation through control of greenhouse humidity, Can. Agric. Eng.. V o l . 22, No . 2, p. 125 Staley, L . M . ; Monk, G. J . 1984, A comparative study of solar energy capture, use and conservation in conventional, solar shed, rock storage and wet earth thermal storage units, A g . Can. contract #08SB.01843-1-ER04 Stoffers, J .A.; van den Kieboom, A . M . 1981, Energy fluxes in greenhouses, Acta Hort.. #115, p.151 Stokes, D . A . ; Tinley, G . H . 1981, Cucumber: thermal screen and growing media investigations, Ada Hort.. #118, p. 135 Takakura, T.; Jordan, K..A.; Boyd, L . L . 1971, Dynamic simulation of plant growth and environment in the greenhouse, Trans, of ihe A S A E . 14(5), p. 964 Takami, S.; Uchij ima, Z . 1977, A model for the greenhouse environment as affected by the mass and energy exchange of a crop, L Agric. Meteorology. 33(3), p. 117 Tutton, M . 1984, Pulp and paper: the need to modernize in Canada's largest industry, Renewable Energy News. Vol . 6, N o . 11, p. C3 Van de Braak, N J . 1981, Thermal problem solving by handcalculations, an application of network theory, Acta H o r t . #115, p. 365 Van de Vooren, J.; Challa, H . 1978, Influence of varying night temperatures on a cucumber crop, Ada Hort,. #87, p. 249 Van Steekelenburg, N . A . M . ; Van de Vooren, J. 1981, Influence of the glasshouse climate on development of diseases in a cucumber. crop with special reference to stem and fruit rot caused by didymella bryoniae, Ada Hon,, #118, p. 45 Verhaegh, A .P . 1981, The influence of insulation techniques on crop production and profitability in the Dutch glasshouse industry, Acta BssL, #115, p. 453 Walker, J . N . ; Duncan, G . A . 1978, Engineering considerations of energy problems in protected cultivation, Acta Hon,, #76, p. 67 Walker, J . N . ; Aldrich, R . A . ; Short, T . H . 1983, Quantity of air flow for greenhouse structures, in Hellickson, M . A . , Ed . , Ventilation & Agricultural Structures. A S A E Monograph #6, St. Joseph, Michigan, p. 257 White, G .B . ; Former, G .R . ; Albright, L D . 1980, The economics of movable interior blankets for greenhouses, in Agricultural Energy, A S A E National Energy Symposium, V o l . 2, p. 562 Wilmer, D .B . 1982, Energy supply and demand in the 1980's, Exploration and Economics jas Petroleum industry, vol. 20, p. 173 Woods, R . W . 1909, Note on the theory of the greenhouse, Phi l . Mag. , p. 319 A P P E N D I X A : G R E E N H O U S E Q U E S T I O N N A I R E 69 70 1983 GREENHOUSE QUESTIONAIRE Name: Address: " Date: 1 . Crop: Spec ies : Spec ia l Care Requirements: Y ie ld :_ Costs and Gross Sales ( e s t . ) : 2. S t r u c t u r e type Dimensions: A D Number o f Un i t s ( G u t t e r - c o n n e c t e d ) (Gable) (Arched) (Quonset) B: C: G: H: F: E: Number o f Growing A r e a s ^ Age: G l a z i n g : 1 A 1' 1 t E B For each house i n d i c a t e t y p e , l a y e r s , age. Single gable greenhouse. . A A 1-\ E B Gutter-connected gable greenhouses. •D-I n s u l a t i o n : A I n f i l t r a t i o n : -B-Gutter-connected, curved-roof greenhouse. •D-O r i e n t a t i o n : Ouonset-style house. 71 Page 2 1983 GREENHOUSE QUESTIONAIRE 3. Envi ronmental Con t ro l V e n t i l a t i o n : ( f a n , vent l o c a t i o n ) H e a t i n g : F u e l : B o i l e r : Set P o i n t s : N i g h t : Day: N i g h t : Day: N i g h t : Day: Water and Humid i ty c o n t r o l : Rel H u m i d i t y : C o n t r o l s : 4. Costs : May we use B.C. Hydro 's energy consumption f i g u r e s ? Other c o s t s : ( l a b o u r , g r o w i n g , c h e m i c a l s , w a t e r , main tenance, e t c . ) Is greenhouse on separa te meter? 5. Time Growing season: S t a r t : End: Days: Day l e n g t h ( h o u r s ) : 72 Page 3 GREENHOUSE QUESTIONAIRE 6. ENERGY SAVING MEASURE SUMMARY TABLE ITEM DATE COST MAI NT COMMENTS pro con .... 7. A d d i t i o n a l Comments A P P E N D I X B : G R E E N D A T L I S T I N G 73 74 ***************** GREENHOUSE ECONOMIC ANALYSIS ***************** Copywrited by Barry S h e l l Sept 1984 Data Entry Spreadsheet *GREENDAT* version 1.1 This spreadsheet contains a l l the questions and blanks which when answered w i l l be used to perform the simulation analysis on another spreadsheet. A l l the input values are indicated by dashed l i n e s . By pressing the <FUNCTION 4> key, you w i l l automatically move to the next input point. Default values are presented at a l l input points and may be l e f t the same or changed to suit the simulation. To begin use <F4> to move to the f i r s t input value: your name. Press the <Return> key once then type in your name (15 l e t t e r s only). Now use the <F4> key to move to the next input point. Type in the appropriate words or numbers and simply use <F4> to move to the next input point. The arrow keys can also be used to move around on the screen. ****************************************************************** YOUR NAME: John Q. Farmer DIMENSIONS: Wall height A: House width B: House length C: Choose units, type in 'feet' or 'meters' feet 8 200 54 TEMPERATURE SET POINTS: / / 7 / / / c Day (C) : 20 HUMIDITY SET AT: 85% Night (C) : 17 (% Relative Humidity) WALL GLAZING TYPE; Glass (1) Double glass (2) Polyethylene (3) Double poly (4) Poly + glass (5) Fibreglass (6) Fibreglass (7) A c r y l i c SDP (9) ROOF GLAZING: FUEL TYPE: Enter a number which best describes your type of wall glazing. (See manual for d e t a i l e d d e s c r i p t i o n of types.) 1 ( f l a t ) (Corrugated) Enter a number from the l i s t above: O i l (1) Gas (2) E l e c t r i c i t y (3) YEAR BUILT: 1 949 CURRENT YEAR: 1 984 75 ESTIMATED LEAKINESS: Rate the 'tightness' of your greenhouse on a scale of 1 to 10 with 10 being very leaky and 1 very t i g h t . Leakiness: 5 ORIENTATION: Type in a (1) i f the roof l i n e i s North/South. Type (2) i f the r o o f l i n e i s aligned East/West. North/South (1): East/West (2): 2 Enter the s t a r t i n g and ending month (i n c l u s i v e ) of your growing season, (e.g. from February to November would be 2 to 11. 2 to Ending month 10 ***************** ENERGY CONSERVATION MEASURES ******************* In t h i s section you may describe the kinds of energy saving techniques used, or those you would l i k e to evaluate. INSULATION: Type 'yes' i f you have perimeter s o i l i n s u l a t i o n i n s t a l l e d . Type 'no' i f perimeter footing walls are uninsulated. yes or no ? no Here are the 10 energy saving measures that can be simulated by the GRNHEAT program. Typing a '1' to the right of a name turns on that technique. Typing a '0' turns i t o f f . You can s t a r t off by switching on the energy conservation measures you now use. Later you can try adding others to see the e f f e c t i t w i l l have on energy consumption. NAME ON/OFF 1 Rootzone heating 0 2 Stackheat recovery 0 3 InfraRed heating 0 4 Computer control 0 5 Reduced leakage 1 6 Poly-on-glass 0 7 North Wall Insulation 0 8 Onemeter perim i n s u l a t i o n 1 9 Thermal Curtains 0 10 Thermal Storage 0 ******************* ECONOMIC VARIABLES ************************** In t h i s section you are asked to enter estimates about future trends in major economic parameters. Indicate what you expect the average values to be for the next 10 or 15 years. Interest Rate: 18% This should be the long term government bond rate + 13% risk factor, (approx. 24%) I n f l a t i o n Rate: 7% This i s the expected i n f l a t i o n rate for the next 10 - 15 years, (currently about 8%) Fuel E s c a l l a t i o n : 9% This i s the rate at which the price of fuel HEATING SEASON: Starting month: 76 i s expected to increase for the next 10 -15 years. Usually about 2% above i n f l a t i o n . Tax Rate: 30% This should be the tax rate you expect to pay for the next 10 - 15 years. Loan Amount: $0.00 If you w i l l borrow money to pay for energy conservation equipment, enter the amount you expect to borrow. Otherwise enter 0. LoanInterestRate: 15% This i s the inte r e s t rate you expect to pay for the above loan. * * * * * * * * * * * * * * * * * * * C R O P VARIABLES ************************** In t h i s section you describe your crop, expected y i e l d and average market p r i c e you w i l l receive over the growing season. Type the number corresponding to the main crop grown: Cucumber (1): Tomato (2): 2 Expected annual y i e l d in cases/square meter 2.45 (e.g. 2.45 for tomatoes, 4.12 for cukes) Expected market price per case: $11.54 (e.g. $11.53 for tomatoes & $10 for cukes) ************************************************************************* This ends the data entry portion of the program. Next you w i l l load the Greenhouse Simulation Spreadsheet and try various combinations of energy saving measures. To change spreadsheets type: <F1> (T)ransfer (S)ave <Return> (Y) (T)ransfer (L)oad GREENSIM <Return> After a couple of minutes you w i l l see a s c r e e n f u l l of v a r i a b l e s . These var i a b l e s show the current scenario being simulated and give the state of the simulation model. The 'Indicators' show the predicted outcome of the use of various energy conservation measures. You may change the values of the variables to try out the e f f e c t s of various combinations of energy conservation techniques. Type a '1' to switch on an energy conservation measure or a '0' to turn i t o f f . To see how the r e s u l t s change with d i f f e r e n t economic futures, you can change the various i n t e r e s t rates, prices or y i e l d values. ************************************************************************* A P P E N D I X C : G R E E N S I M L I S T I N G 77 1 2 3 4 5 ************************ GREENHOUSE ECONOMIC ANALYSIS ******************** Copywrited by B a r r y S h e l l Sept 1984 •GREENSIM* v e r s i o n 1.1 GETTING AROUND: To see a p a r t of the program type (G)o (N)ame and the f i r s t l e t t e r of the s e c t i o n : 1 2 3 4 5 6 7 8 For example to see the F i n a n c i a l 9 10 COMPUTER GREENHOUSE ENERGY SAVING SIMULATION FOR 11 ** ENERGY SAVING MEASURES ** (E) nergy b a l a n c e ( V ) a r i a b l e s (F) i n a n c i a l anal Crop ( Y ) i e l d (L)ookup t a b l e s (I ) n s t r u c t i o n s a n a l y s i s , you would type: G N F <return> 12 r o o t z o n e 13 s t a c k h e a t 14 IRheat 15 computer 16 r e d u c e d l e a k 17 p o l y o n g l a s s 18 N w a l 1 i n s u l 19 onemeterinsu1 20 therma1 c u r t a i n s 21 h e a t s t o r a g e 22 23 INDICATORS AND RESULTS ** 24 Annual Loan Payment: 25 Change i n S a l e s / y r : 26 Annual Energy Sa v i n g 27 Net P r e s e n t V alue 28 Break-Even Year 29 H e a t i n g from month 30 31 CALC OF AREAS ETC 32 S u r f a c e a r e a : 33 Volume: 34 P e r i m e t e r : 35 F l o o r a r e a : 36 N wal1 a r e a : 37 Roof areaT Area and Age 0 G l a z i n g Made Of 0 G l a z i n g on Roof 0 Fuel Type Used 0 Crop Grown 1 I n t e r e s t Rate 0 Fuel e s c a l a t i o n 0 I n f l a t i o n Rate 1 Tax Rate 0 Loan Amount 0 Loan I n t e r e s t C r o p P r i c e $/case Y i e l d ( c a s e s / y r ) $0.00 I n s t a l l e d Cost : $0.00 Maintenance $/yr $2,519.71 Fuel Cost $/MJ : $12,681.21 HEATLOSS SUMMARY 2 Heat Cost $ / f t 2 John Q. Farmer 1984 1,003.32 m2 35 y e a r s o l d 2 to month 10:Tot Losses MJ/m2 Gl a s s 3 mm G l a s s 3 mm Nat u r a l Gas Tomato 18% 9% 7% 30% $0.00 15% $1 1 .54 2,458.1 $3,474.78 $63.37 0.00404907 Old House $1 .03 2,736 1 1 2 2 Night Temp. 17 Day Temp. 20 ADJUSTMENTS % L i g h t 0.00% % Fuel 0.00% % Y i e l d 0.00% With E Save $0.79 2,115 ********* VARIABLES TRANSFERRED FROM GR 1,510.93 square meters 2,689.63 c u b i c meters 154.94 meters 1,003.32 square meters 40.13 square meters 1,103.65 square meters FOR REFERENCE GREENHOUSE Old 6.45 W/sq m * C 6.45 W/sq m * C 3 volumes/hour offmonth=0 1 0=new 1=old 1 38 39 INTERMEDIATE CALCS 4 0  41 Wal1 U v a l u e : 42 Roof U v a l u e : 43 44 Greenhouse Age: 45 A i r c h a n g e s : 46 47 I n f i l t r a t i o n l o s s e s 48 49 Vapour D e n s i t y C a l c : T e t e n s Vap* 50 i n Nightime Vap i n s i d e 51 52 CALCULATION OF NET ANNUAL HEATING LOAD OF REFERENCE GREENHOUSE 53 54 | Jan Feb Mar 2,686.94 W/deg C ( s e n s i b l e heat o n l y ) 14.45 g / c u b i c M 11.13 g / c u b i c M U n i t s of l e n g t h Houselength Housew i d th Wal1 h e i g h t Day temp. C Night temp. C L e a k i n e s s e s t . Or i e n t a t i on Fuel Cost Crop Type Marketpr i ce Y i e l d Startmonth Endmonth $/MJ f e e t 54 200 8 20 17 5 2 .00404907 2 $11.54 2.45 2 10 Apr May Jun J u l c c 1 2 3 4 5 6 7 8 OO 56 Avg Oaylight Hours 9 10 12 14 15.5 16 15.5 57 Outside Day Temp C 5.29 7.56 9.65 13.98 16.83 19.60 22 .09 58 Outside Nite Temp C -0.27 0.96 2.30 4.83 7.84 10. 74 12.47 59 DeltaT (day) deg C 14.71 12.44 10.35 6 .02 3. 17 0.40 -2 .09 60 DeltaT (night) C 17.27 16.04 14.70 12. 17 9. 16 6 . 26 4 . 53 61 DeltaVap (N1te)g/M3 7.33 7.15 7.11 6. 16 5. 16 4 .05 3.13 62 DAYTIME LOSSES 63 0 radiation (MJ/mo) -59,785 -81,805 -147,096 -269,804 -368,124 -379,153 0 64 0 roof (MJ/month) 103,196 96,968 96.812 65,695 38,300 4,989 0 65 0 North wal1 3.753 3,526 3,520 2,389 1 , 393 181 0 66 0 E,W,S walIs 34,329 32,257 32,205 21 ,854 12,741 1 ,660 0 67 0 perimeter 6,222 5,846 5,837 3,961 2,309 301 0 68 Qi nf11trat ion(sens) 38,952 36,601 36,542 24,797 14,457 1 ,883 0 69 Q i n f l t r a t i o n ( l a t n t ) 29,893 40,903 73,548 134,902 184,062 189,577 0 70 NIGHTTIME LOSSES 71 0 roof (MJ/month) 201,925 175,040 137,501 94,863 60,690 39,036 30,014 72 0 North wal1 7 , 343 6,365 5,000 3,450 2,207 1 ,420 1 ,091 73 0 E,W,S walIs 67,172 58,229 45,741 31,557 20,189 12,986 9, 984 74 0 perimeter 12,174 10,553 8,290 5,719 3,659 2, 354 1,810 75 O i n f i l t r a t i o n ( s e n s ) 76,218 66,070 51,900 35,807 22,908 14,735 11,329 76 "7 V Qi nf1trat ion(1atnt) 66,096 60,166 51 ,276 37,051 26,384 19,467 15,98 1 / / 78 7 Q Monthly t o t a l s 587,486 510,719 401,077 208,447 136,037 89,997 70,209 / 23 80 Fuel Required (MJ) 0 729,599 572,967 297,781 194,339 128,567 100,299 81 Cost of Fuel $0.00 $2,954.20 $2,319.98 $1,205.74 $786.89 $520.57 $406.12 82 83 Ototal MJ/sq m yr 2,735.72 84 85 Costs @ $0.405/100MJ $11.08 per sq m $1.03 per sq f t 8 6  87 INTERMEDIATE CALC FOR ENERGY SAVING GREENHOUSE 88 i f reduceleak i f polyonglass i f Imeterinsul 89 ESG E, W,& S wall U: 6.321 6.321 4.67754 W/sq m * C 90 ESG Roof U value: 6.45 W/sq m * C 91 ESG N wall U value: 6.321 W/sq m * C 92 93 i f reduceleak 1f polyonglass i f Nwallinsul If Imeterlns 94 ESG Airchanges: 1.5 1.5 1.5 1.47 volumes/hr 95 I n f i l t r a t i o n losses: 1,316.60 W/deg C (sensible heat only) 96 97 i f rootzone i f stackheat if IRheat i f computer i f heatstorage 98 ESG B o i l e r E f f i c i e n c y 0.7 0.7 0.7 0.7 0.7 99 i f IRheating i f Polyonglass if Nwallinsul i f thrmlcurtn 100 ESG radiation loss: 1.000 1.000 1.000 1.000 f r a c t i o n of light transmitted with ener 101 102 CALCULATION OF NET ANNUAL HEATING LOAD OF ENERGY SAVING GREENHOUSE 103 104 Jan Feb Mar Apr May Jun Jul 105 106 DeltaT (day) deg C I 14.71 12.44 10.35 6.02 3.17 0.40 -2.09 107 DeltaT (night) C | 17.27 16.04 14.70 12.17 9.16 6.26 4.53 108 DAYTIME LOSSES 109 1 10 1 1 1 1 12 1 13 1 14 115 1 16 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 1 0 ra d i a t i o n (MJ/mo) 0 roof (MJ/month) 0 North wal1 0 E,W,S walIs 0 perimeter Olnfi1tration(sens) Oinf1trat ion(latnt) NIGHTTIME LOSSES 0 roof (MJ/month) Q North wal1 Q E,W,S walIs 0 perimeter O i n f i l t r a t i o n ( s e n s ) Qi nf1trat ion(1atnt) 158 159 160 161 162 Monthly t o t a l s Fuel Cost Requ i red of Fuel (MJ) 2 3 4 5 6 7 8 -59,785 -81,805 -147,096 -269,804 -368, 124 -379,153 0 103,196 96,968 96,812 65,695 38,300 4,989 0 3,678 3,456 3,450 2,341 1 ,365 178 0 24,895 23,393 23,355 15,849 9,240 1 ,203 0 6, 222 5,846 5,837 3,961 2,309 301 0 19,086 17,934 17,906 12,150 7,084 923 0 29,893 40,903 73,548 134,902 184,062 189,577 O 201,925 175,040 137,501 94,863 60,690 39,036 30.014 7, 196 6,238 4,900 3,381 2, 163 1 , 391 1 .070 48,713 42,228 33,171 22,885 14,641 9,417 7,241 12,174 10,553 8,290 5,719 3,659 2,354 1,810 37,347 32,374 25,431 17,545 1 1 ,225 7,220 5.551 32,387 29,481 25,125 18,155 12,928 9,539 7 , 831 466,926 402,609 308,230 162,549 105,306 68,957 53,516 0 575,156 440,329 232,212 150,438 98,510 76,451 $0.00 $2,328.84 $1,782.92 $940.24 $609.13 $398.87 $309.56 Qtotal MJ/sq m yr 2,115.48 Costs 9 $0.405/100MJ *********************** $8.57 per sq m CROP YIELD ANALYSIS ***** Annual Y i e l d (cases) 2,458.1 Price per case: $11.54 ****************** ECONOMIC ANALYSIS Crop Value: $0.79 per sq f t ****************** $28,366.87 ********************** E . $'s Change saved/year : in Sales/yr: YEAR $2, 519. $0. 71 00 +/- SALES Loan Payment: E $'s SAVED $0.00 annual 1y MA I NT LOAN BAL INTEREST PRINCIPAL CCA 143 1984 $0 00 $1,763 80 $44 36 $0 00 $0 00 $0 00 $104 24 144 1984 0 00 1 ,922 54 47 47 $0 00 $0 00 $0 00 93 82 145 1984 0 00 2,095 56 50 79 $0 00 $0 00 $0 00 84 44 146 1984 0 00 2,284 17 54 34 $0 00 $0 00 $0 00 75 99 147 1984 0 00 2,489 74 58 15 $0 00 $0 00 $0 00 68 39 148 1984 0 00 2,713 82 62 22 $0 00 $0 00 $0 00 61 55 149 1984 0 00 2,958 06 66 57 $o 00 $0 00 $0 00 55 40 150 1984 0 00 3,224 29 71 24 $0 00 $0 00 $0 00 49 86 151 1984 0 00 3,514 47 76 22 $0 00 $0 00 $0 00 44 87 152 1984 0 00 3,830 77 81 56 $0 00 $0 00 $0 00 40 39 153 1984 0 00 4, 175 54 87 27 $0 00 $0 00 $0 00 36 35 154 1984 0 00 4,551 34 93 37 $0 00 $0 00 $0 00 32 71 155 1984 0 00 4,960 96 99 91 $0 00 $0 00 $0 00 29 44 156 1984 0 00 5,407 45 106 90 $0 00 $0 00 $0 00 26 50 157 1984 0 00 5,894 12 114 39 $0 00 $0 00 $0 00 23 85 Net Present Value: ***************** LOOKUP TABLES BEGIN HERE ****************** GLAZING TABLE GLAZING NUMBER U VALUE Short T co o 1 . 2 3 4 163 1 Glass 3 mm 6 45 0.88 164 2 double glass 3 70 0. 75 165 3 polyethylene 6 53 0.89 166 4 double poly 4 25 0.76 167 5 poly + glass 4 00 0. 75 168 6 f i b r g l a s f l a t 5 68 0. 78 169 7 f i b r g l a s —>—•—•—< 6 53 0.78 170 8 1 in.styrfoam 0 97 0 171 9 Double acryl 3 12 0.85 172 173 U values are overall heat loss (W/m2 O based on 25 km/hr 174 outside and no wind inside. Taken from Blom 1982. 175 176 WEATHER TABLE Average weather data for the Vancouver area. 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 ESM TABLE 201 202 ESM 203 204 205 206 207 208 209 210 211 212 213 1983 Relat i ve MONTH Number Avg Low Avg High Rad1at i on Humidi ty MO/ scj m January 1 -0.270 5 . 290 3 . 182 80% February 2 0.960 7 .560 4.354 77% March 3 2 . 300 9 .650 7.829 71% Apr i 1 4 4.830 13 .980 14.360 74% May 5 7 .840 16 830 19.593 73% June 6 10.740 19 600 20.180 72% Jul y 7 12.470 22 090 22.817 73% August 8 12.430 21 .740 16.663 75% September 9 9 . 900 18 . 470 10.550 79% October 10 6.460 13 740 6 .699 81% November 1 1 2 . 900 9 060 3.978 82% December 12 1 . 240 6 610 2 . 345 84% •- AIRCHANGE TABLE --- Values in greenhouse ai rchanges/hr AGEcode Airchange range Low new glass 0 0.75 0 75 old glass 1 2 .00 2 00 new doublepoly 2 0.80 0 20 old doublepoly 3 1 .50 0 50 Adjustment factors for ESM used: f r a c t i o n of 1 or $/m2 Root Zone Heating Stack Heat Recovery I.R. Heat i ng Computer Control Reduced Leakage Poly on Glass N Wal1 Insulat ion 1 meter Insulation Thermal Curtains Heat Storage -Per square me 214 215 216 TYPEcode TYPE er | Uwa11va1 Uroofval Rtrans | A i rchange Inst Cost 1 1 00 1 .00 1 .00 1 00 $19 OO 2 1 00 1 .00 1 .00 1 00 $12,000 00 3 1 00 1 .00 0.95 1 00 $2 1 00 4 1 00 1 .00 1 .00 1 00 $20,000 00 5 0 98 0.98 1 .00 0 50 $3 00 6 0 68 0.68 0. 86 0 30 $10 00 7 1 00 1 .00 0.95 0 93 $3 00 8 0 74 1 .00 0.93 0 98 $3 00 9 0 47 0.47 0.96 0 90 $12 00 10 1 00 1 .00 1 .00 1 00 $17 00 12 0 00 0.00 0.00 0 00 $0 00 TABLE -- -- ($/MJ) 1983 COST $/MJ 1 2 3 4 5 217 1 O i l 0.00817132 @ 1.45 $/imp.gal 218 2 N a t u r a l Gas 0.00404907 @ .405 $/therm 219 3 E l e c t r i c i t y 0.014598 @ .05 $/KWhr 220 221 CROP DATA TABLE From Rick Wallace (B.C. Hothouse P r o d u c t s ) 1982 222 YIELD 223 NUMBER CROP CASE/SO M MKT PRICE/CASE 224 1 Cucumber 4.12 $10.00 225 2 Tomato 2.45 $11.53 22G 3 227 4 00 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 S C R A T C H P A D 10 I n s t a l c o s t Maintenance Y l d f a c t o r 11 12 EENDATA ************* 1 $0 .00 $0 .00 0. .000 13 G l a z i n g type 1 2 0 .00 0 .00 0. .000 14 Fuel type : 2 3 0 .00 0 .00 0, .000 15 Ro o f G l a z i ng: 1 4 0 .00 0 .00 0. .000 16 INSULATION 5 3,009 .96 40 . 13 0. .000 17 P e r i m e t e r : no 6 0 .00 0 .00 0. .000 18 7 0 .00 O. .00 0. .000 19 Age 35 8 464 .82 23 , 24 0. .000 20 9 0. ,00 0. .00 0. .000 21 Year bu i11 1949 10 0. .00 0. .00 0. .000 22 I n t e r e s t R a t e : 0. 18 23 I n f l a t i o n : 0.07 TOTALS: $3,474. . 78 $63 . 37 0. .000 24 E s c a l a t i o n : 0.09 25 Tax Rate: 0. 30 26 Loan Amount: $0.00 27 LoanIntRate: 0. 15 28 r o o t z o n e 0 29 s t a c k h e a t 0 30 IRheat 0 31 computer 0 32 r e d u c e d l e a k 1 33 p o l y o n g l a s s 0 34 Nw a l 1 i n s u l 0 35 o n e m e t e r i n s u l 1 36 t h e r m a l c u r t a i 0 37 h e a t s t o r a g e 0 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 Aug Sep Oct Nov Dec Year t o t a l 10 11 12 13 14 55 --. 56 14 12 10 9 8 57 21 . 74 18.47 13.74 9.06 6.61 58 12.43 9.90 6.46 2.90 1 . 24 59 -1 .74 1 .53 6.26 10.94 13.39 60 4 . 57 7 . 10 10.54 14.10 15 . 76 61 2.93 3 . 76 5.08 6.29 6.70 62 63 0 -198,219 -125,865 -74,741 -44,059 -1,748,652 64 0 14,311 48,796 76,748 83,498 629,311 65 0 520 1 ,774 2,791 '3,036 22,884 66 0 4,761 16,232 25,531 27,776 209,346 67 0 863 2,942 4,627 5,034 37,942 68 0 5,402 18,418 28,969 31,517 237,537 69 0 99, 1 10 62,932 37,370 22,030 874,326 70 71 35,622 66,412 115,020 164,860 196,554 1,317,539 72 1 ,295 2,415 4 , 183 5,995 7, 147 47,910 73 1 1 ,850 22,093 38,263 54,842 65,386 438,292 74 2, 148 4,004 6,935 9,940 11,850 79,436 75 13,446 25,068 43,415 62,228 74,191 497,313 76 17,606 27,136 42,734 56,743 . 64,471 485,110 78 81,967 147,127 275,779 455,903 548,431 3,513,180 79 80 117,096 210,181 393,970 0 0 2,744,800 81 $474 . 13 $851.04 $1,595.21 $0.00 $0.00 $11,113.89 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 gy conservation 101 102 103 104 105 -106 107 108 Aug Sep Oct Nov Dec Year total -1 .74 4.57 1 . 53 7 . 10 6.26 10. 54 10.94 14. 10 13.39 15.76 9 10 11 12 13 14 109 0 -198,219 -125,865 -74,741 -44,059 -1 ,748,652 1 10 0 14,311 48,796 76,748 83,498 629,311 1 1 1 0 510 1 ,739 2,735 2,976 22,426 112 0 3,453 1 1 ,772 18,515 20,143 151,818 1 13 0 863 2,942 4,627 5,034 37,942 1 14 0 2,647 9,025 14,195 15,443 116,393 1 15 0 99, 1 10 62,932 37,370 22,030 874,326 1 16 1 17 35,622 66,412 115,020 164,860 196,554 1 ,317,539 1 18 1 , 269 2, 367 4 ,099 5,875 7,004 46,952 119 8, 594 16,022 27,748 39,772 47,418 317,850 120 2, 148 4,004 6,935 9,940 1 1 ,850 79,436 121 6, 588 12,283 21,273 30,492 36,353 243,683 122 8,627 13,297 20,939 27,804 31 ,591 237,704 123 124 62,849 114,384 207,355 358,192 435,836 2,746,708 125 126 89,784 163,405 296,222 0 0 2,122,507 127 $363 . 54 $661.64 $1 ,199.42 $0.00 $0.00 $8,594. 18 128 129 130 131 132 133 134 135 136 137 138 139 140 141 CASHFLOW DISCOUNTED YEAR NUMBER 142 I [$3,474 .78)($3,474.78) 143 $1,823 .68 $1,823.68 1 144 $1,968 .89 1,968.89 Break-Even 145 $2,129 .21 2,129.21 3 146 $2,305 .81 2,305.81 4 147 $2,499 .99 2,499.99 5 148 $2,713 . 15 2,713. 15 6 149 $2,946 .89 2,946.89 7 150 $3,202 .91 3,202.91 8 151 $3,483 . 12 3,483. 12 9 152 $3,789 .60 3,789.60 10 153 $4,124 .63 4,124.63 1 1 154 $4,490 .68 4,490.68 12 155 $4,890 . 49 4,890.49 13 156 $5,327 .04 5,327.04 14 157 $5,803 . 58 5,803.58 15 158 brk even y r : 2 159 $12,681 .21 160 161 162 9 10 11 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 t e r Change i n H e a t i n g 202 Maintenance Y i e l d (%) E f f i c i e n c y 203 --204 $0, . 20 0. .020 1 . 15 205 $100 .00 0. .000 1 . 12 206 $0, . 13 -0. .030 1 .25 207 $200. .00 0. .050 1 . 12 208 $0, .04 0, ,000 209 $2. .00 -0. . 100 210 $0. . 15 -0. .005 211 $0 . 15 0. .000 212 $0, . 12 -0. .010 213 $50. .00 0, ,000 1 .25 214 $0. .00 0. .000 215 216 CO CM O r - o o o O - ^ C M C O ' a - m i D r -' - i - ' - C M C M C M C M C M C M C M C N C M C M C M C M C M C M C M C M C M C M C M APPENDIX D: GREENSIM EQUATIONS j n************************ GREENHOUSE EC ONOMIC ANALYSIS ********************" 4 "GETTING AROUND: To see a p a r t of the p rogram t y p e (G)o (N)ame and t h e " 5 " f i r s t l e t t e r of the s e c t i o n : " 6 7 8 "For example to see the F i n a n c i a l a n a l y s i s , you would t y p e : G N F <return>" 9 10 "COMPUTER GREENHOUSE ENERGY SAVING SIMU LATION FOR:" 11 "** ENERGY SAVING MEASURES **" 12 " r o o t z o n e " "Copywrited by B a r r y Shel 1 Sept 1984" g r e e n d a t a . r o o t z o n e " *GREENSIM* v e r s i o n 1.1' "( E ) n e r g y b a l a n c e " " ( F ) i n a n c i a l a n a l y s i s " "(L)ookup t a b l e s " "Area and Age :" " G l a z i n g Made Of : " 13 " s t a c k h e a t " g r e e n d a t a . s t a c k h e a t " G l a z i n g on Roof: 14 "IRheat" g r e e n d a t a . I R h e a t "Fuel Type Used 15 "computer" greendata.computer "Crop Grown 16 " r e d u c e d l e a k " g r e e n d a t a . r e d u c e d l e a k " I n t e r e s t Rate 17 " p o l y o n g l a s s " g r e e n d a t a . p o l y o n g l a s s "Fuel e s c a l a t i o n : 18 " N w a l l i n s u l " g r e e n d a t a . N w a l 1 i n s u l ' Inf1 at ion Rate : " 19 " o n e m e t e r i n s u l " greendata.onemeterinsu1 "Tax Rate 20 " t h e r m a l c u r t a i n s " g r e e n d a t a . t h e r m a l c u r t a i n s "Loan Amount 21 " h e a t s t o r a g e " g r e e n d a t a . h e a t s t o r a g e "Loan I n t e r e s t 22 23 "** INDICATORS AND RESULTS **' 24 "Annual Loan Payment:" 25 "Change i n S a l e s / y r :" 26 "Annual Energy S a v i n g : " 1oanpayment d e l t a S a l e s y e a r 1ysav i ng " C r o p P r i c e $/case" " Y i e l d ( c a s e s / y r ) " " I n s t a l l e d Cost : " "Maintenance $/yr "Fuel Cost $/MJ :" 00 1 27 "Net P r e s e n t V a l u e :" 28 "Break-Even Year : " 29 " H e a t i n g from month "&FIXED(startmonth, 0)&" to month "&FIXED(endmonth,0)&":" 30 31 "CALC OF AREAS ETC" 32 " S u r f a c e a r e a . " 33 "Volume:" 34 "Per i m e t e r : " 35 "F1oor a r e a : ' 35 "N wal1 a r e a : 37 "Roof a r e a : " 38 39 "INTERMEDIATE CALCS" NPV breakeven "HEATLOSS SUMMARY" "Heat Cost $ / f t 2 " "Tot Losses Mu/m2" (houselength*wa11 h e i g h t * 2 "square meters' +housewidth*wallheight*1. 1*2+houselength*housewidt h*1 . 1 ) * I F ( L E N ( u n i t s ) = 4 , 0 . 0929,1) (housewidth*wa11 h e i g h t * 1. " c u b i c meters" 1*houselength)*IF(LEN(uni ts)=4,0.0283,1) (2*housewidth+2*houseleng "meters" t h ) * I F ( L E N ( u n i t s ) = 4 , 0 . 3 0 5 , 1 ) housewidth*houselength*IF "square meters'* (LEN(units)=4,0.0929,1) ( I F ( o r i e n t a t i o n = 1 , h o u s e w i "square meters" dth*wal1 h e i g h t , h o u s e l e n g t h * w a l l h e i g h t ) ) * I F ( L E N ( u n i ts)=4,0.0929,1) 1.1*R[-2]C "square meters" "FOR REFERENCE GREENHOUSE 40 " 41 "Wal 1 U v a l u e : " 42 "Roof U v a l u e : " 43 44 "Greenhouse Age:" 45 "A i r c h a n g e s : " 46 47 " I n f i l t r a t i o n l o s s e s : 48 49 "Vapour D e n s i t y C a l c : LOOKUP(g1az i ng,Uva1ue) "W/sq m * C" LOOKUP(roofglazing,Uvalue "W/sq m * C" ) IF(age=0,"New","01d") I F ( ( ( c u r r e n t y e a r ) - y e a r b u i1t)>9, 1,0) 0.1*leakiness*L00KUP((age "volumes/hour" +IF(0R(glaz=2,glaz=4,glaz = 5 ) , 2 , 0 ) ) , a i rchangerange) +L00KUP((age+IF(0R(g1az=2 , g l a z = 4,glaz = 5 ) , 2 , 0 ) ) , a i r changelow) 0.333*volume*airchanges "W/deg C ( s e n s i b l e heat o n l y ) " "Tetens Vap* =" 50 " i n Nighti m e " 51 52 "CALCULATION OF NET ANNUAL HEATING LOAD OF REFERENCE GREENHOUSE" "Vap i n s i d e (1322/(nighttemp+273.2))*(10a(( ni ghttemp*7.5)/(ni ghttemp+237 . 3 ))) 0.77*vapstarInNight o 53 1 2 3 54 " I" "Jan" 55 " •Feb" 56 "Avg D a y l i g h t Hours I" 57 " O u t s i d e Day Temp C |" 58 " O u t s i d e N i t e Temp C|" 59 "D e l t a T (day) deg C " 60 " D e l t a T ( n i g h t ) C 61 "DeltaVap (Nite)g/M3 " 62 "DAYTIME LOSSES" 63 "0 r a d i a t i o n (MJ/mo)|" 64 "0 r o o f (MJ/month) |" 65 "Q N o r t h wal1 | 66 "0 E.W,S w a l I s | " 67 "0 p e r i m e t e r | " 68 " O i n f i 1 t r a t i o n ( s e n s ) | " 69 " O i n f 1 t r a t i o n ( l a t n t ) | " 70 "NIGHTTIME LOSSES" 71 "0 r o o f (MJ/month) |" 9 L00KUP(C0LUMN()-1.Hightem P) L00KUP(C0LUMN()-1,Lowtemp ) daytemp-R[-2]C nighttemp-R[-2]C v a p l n N i ght-(LOOKUP(COLUMN ( ) - 1 , h u m i d i t y t a b l e ) * ( ( 1 3 2 2/(R[-3]C+273.2))*(10a((R t-3]C*7.5)/(R[-3]C+237.3) )))) I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly R a d i a t ion)*LOOKUP(roofgla z i n g , S h o r t w a v e t r a n s ) * 0 . 7 * 30.4) IF(R[-5]C<0,0,roofarea*Ur oof*R[-5]C*(2.628*R[-8]C/ 24)) IF(R[-6]C<0,0,Northwallar ea*Uwal1s*R[-6]C*(2.628*R t-9]C/24)) I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a-(Northwal1area+roofarea ))*Uwalls*R[-7]C*(2.628*R [-10]C/24)) IF(R[-8]C<0,0,perimeter*I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 .39,2.77)*R[-8]C*2.628*R[ -11JC/24) I F ( R [ - 9 ] C < 0 , 0 , O i n f i I t r a t i on*R[-9]C*(2.628*R[-12]C/ 24)) I F ( R [ - 1 0 ] C < 0 , 0 , f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly R a d i a t 1 o n ) * L 0 0 K U P ( r o o f g l a z i ng,Shortwavetrans)*0.5* 0.7*30.4) I F ( R [ - 11]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R [-15]C)/24)) 10 L00KUP(C0LUMN()-1.Hightemp) L00KUP(C0LUMN()-1,Lowtemp) daytemp-R[-2]C nighttemp-R[-2]C vapInNight-(L00KUP(C0LUMN()-1,h umi d i t y t a b l e ) * ( ( 1 3 2 2 / ( R [ - 3 ] C + 2 7 3.2) )*(10a((R[-3]C*7.5)/(R[-3]C +237.3))))) IF(R[-4]C<0,0,-floorArea*LOOKUP (COLUMN()-1,MonthlyRadiation)*L 00KUP(roofglaz i ng.Shortwavetran s)*0.7*30.4) IF(R[-5]C<0,0,roofarea*Uroof*R[ -5]C*(2.628*R[-8]C/24)) IF(R[-6]C<0,0,Northwal larea*Uwa 1ls*R[-6]C*(2.628*R[-9]C/24)) IF(R[-7]C<0,0.(surfacearea-(Nor thwal1area+roofarea))*Uwal 1 s*R[ -7]C*(2.628*R[-10]C/24)) IF(R[-8]C<0,0,perimeter*IF(LEN( per imeter i nsul)=3, 1.39,2.77)*R[ -8]C*2.628*R[-11]C/24) IF(R[-9]C<0,0,Qinf i 1 t r a t ion*R[-9]C*(2.628*R[-12]C/24)) IF(R[-10]C<0,0,floorArea*LOOKUP (C0LUMN()-1,MonthlyRadiation)*L 0 0 K U P ( r o o f g l a z i ng,Shortwavetran s)*0.5*0.7*30.4) IF(R[-11]C<0,0,roofarea*Uroof*R [-11 ]C*(2.628*(24-R[-15]C)/24)) 1 72 "Q N o r t h wal1 | " 73 "Q E,W,S w a l I s | " 74 "0 p e r i m e t e r | " 75 " Q i n f i 1 t r a t i o n ( s e n s ) | " 76 " Q i n f 1 t r a t i o n ( l a t n t ) | " 7 7 n 2 IF(R(-12]C<0,0,Northwalla rea*Uwal1s*R[- 12]C*(2.628 *(24-R[-16]C)/24)) I F ( R [ - 1 3 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a))*Uwalls*R[-13]C*2.628* (24-R[-17]C)/24) IF(R[-14]C<0,0,perimeter* I F ( L E N ( p e r i m e t e r i nsu1) = 3, 1 .39,2.77)*R[-14]C*2.628* (24-R[- 18]C)/24) I F ( R [ - 1 5 ] C < 0 , 0 , Q i n f i 1 t r a t ion*R[-15]C*2.628*(24-R[-19]C)/24) IF(R[-15]C<0,0,(3600*24*3 0.4167*((24-R[-20]C)/24)) * 2 . 4 5 * ( a i rchanges/3600)*v olume*(R[-15]C/1000)) 3 IF(R[-12]C<0,0,Northwallarea*Uw alls*R[-12]C*(2.628*(24-R[-16]C )/24)) IF(R[-13]C<0,0,(surfacearea-(No rthwal1area+roofarea))*Uwal1s*R [-13]C*2.628*(24-R[-17]C)/24) IF(R[-14]C<0,0,perimeter* IF(LEN (per imeter i n s u l ) = 3,1.39,2.77)*R [-14]C*2.628*(24-R[-18]C)/24) IF(R[-15]C<0',0,Q1nf i 1 t r a t i o n * R [ -15]C*2.628*(24-R[-19]C)/24) IF(R[-15]C<0,0,(3600*24*30.4167 * ( ( 2 4 - R [ - 2 0 ] C ) / 2 4 ) ) * 2 . 4 5 * ( a i r c h anges/3600)*volume*(R[-15]C/100 0)) 78 "Monthly t o t a l s IF(-R[-15]C>SUM(R[-14]C:R I F ( - R [ - 15]C>SUM(R[- 14]C:R[-9]C) [-9]C),SUM(R[-7]C:R[-2]C) ,SUM(R[-7]C:R[-2]C)\SUM(R[- 15]C ,SUM(R[-15]C:R[-2]C)) :R[-2]C)) 79 80 "Fuel R e q u i r e d (MJ) |" IF(AND(C0LUMN()>=startmon th+1,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth+1),R[ -2]C/0.7,0) 81 "Cost of Fuel |" R [ - 1 ] C * f u e l c o s t 82 83 " Q t o t a l MJ/sq m y r " R [ - 3 ] C [ + 1 2 ] / f l o o r A r e a 84 85 "Costs @ $0.405/100MJ:" R [ - 2 ] C * f u e l c o s t 86 " I  87 "INTERMEDIATE CALC FOR ENERGY SAVING GR EENHOUSE " 88 " " i f reduce leak" n 89 "ESG E, W,& S w a l l U:" IF(reducedleak=1,LOOKUP ( 5 .ESMUwalIval)*Uwalls.Uwal i s ) IF(polyonglass=1,L00KUP(6 ,ESMUroofval)*Uroof,Uroof ) IF(Nwal1insul=1,L00KUP(8, Uvalue),R[-2]C) 90 "ESG Roof U v a l u e : 91 "ESG N wal1 U v a l u e : 1 IF(AND(C0LUMN()> = startmonth+1 ,C 0LUMN()<=endmonth+1,C0LUMN()<>o ffmonth+1),R[-2]C/0.7,0) R[-1 ] C * f u e l c o s t "per sq m" " i f p o l y o n g l a s s " IF(polyonglass=1,LOOKUP(6,ESMUw a l 1 v a l )*RC[-1],RC[-1]) "W/sq m * C" 92 93 ' i f r e d u c e l e a k " "W/sq m * C" i f p o l y o n g l a s s " 94 "ESG A i r c h a n g e s : 95 " I n f i l t r a t i o n l o s s e s : " 96 97 98 "ESG B o i l e r E f f i c i e n c y : ' 2 3 I F ( r e d u c e d l e a k = 1 ,LOOKUP(5 IF(polyonglass=1,LOOKUP(6,ESMai ,ESMai r c h a n g e ) * a i rchanges rchange)*RC[- 1],RC[- 1]) , a i r c h a n g e s ) O.333*volume*ESGairchange "W/deg C ( s e n s i b l e heat o n l y ) " s " i f r o o t z o n e " " i f s t a c k h e a t " IF(rootzone=1,LOOKUP(1,ES IF(stackheat=1,LOOKUP(2,ESMheat M h e a t e f f i c i e n c y ) * 0 . 7 , 0 . 7 ) e f f i c i e n c y ) * R C [ - 1] ,RC[-1 ] ) 99 100 "ESG r a d i a t i o n l o s s : " " i f I R h e a t i n g " " i f P o l y o n g l a s s " IF(IRheat=1,LOOKUP(3,ESMR IF(polyonglass=1,LOOKUP(6,ESMRt t r a n s ) . 1 ) r a n s ) * R C [ - 1],RC[-1]) 101 102 "CALCULATION OF NET ANNUAL HEATING LOAD OF ENERGY SAVING GREENHOUSE" 103 " 104 "Jan" 105 " "Feb" 106 "D e l t a T (day) deg C 107 "D e l t a T ( n i g h t ) C 108 "DAYTIME LOSSES" 109 "Q r a d i a t i o n (MJ/mo) 110 "0 r o o f (MJ/month) 111 "0 N o r t h wal1 112 "Q E.W.S w a l I s 113 "0 p e r i m e t e r 114 " Q i n f i 1 t r a t 1 o n ( s e n s ) daytemp-L00KUP(C0LUMN()-1 .Hightemp) nighttemp-L00KUP(C0LUMN( ) - 1 ,Lowtemp) I F ( R [ - 3 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly R a d i a t ion)*LOOKUP(roofgla z i n g , Shortwavetrans)*0.7* 30.4*ESGradiat i o n l o s s ) IF(R[-4]C<0,0,roofarea*ES GUroof*R[-4]C*(2.628*R[-5 4]C/24)) IF(R[-5]C<0,0,Northwallar ea*ESGUnorthwal1*R[-5]C*( 2.628*R[-55]C/24)) I F ( R [ - 6 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ) )*ESGUwalls*R[-6]C*(2.62 8*R[-56]C/24) ) IF(R[-7]C<0,0.perimeter*I F(LEN(perimeterinsu1)=3,1 .39,2.77)*R[-7]C*(2.628*R [-57]C/24) ) I F ( R [ - 8 ] C < 0 , 0 , E S G O i n f i I t r ation*R[-8]C*(2.628*R[-58 daytemp-LOOKUP(COLUMN()-1,Hight emp) nighttemp-LOOKUP(COLUMN()-1,Low temp) IF(R[-3]C<0.0,-floorArea*L00KUP (C0LUMN()-1,Month1yRadiation)*L 00KUP(roofglaz i ng,Shortwavetran s ) * 0 . 7 * 3 0 . 4 * E S G r a d i a t i o n l o s s ) IF(R[-4]C<0,0,roofarea*ESGUroof *R[-4]C*(2.628*R[-54]C/24)) IF(R[-5]C<0,0,Northwallarea*ESG Unorthwal1*R[-5]C*(2.628*R[-55] C/24)) IF(R[-6]C<0,0,(surfacearea-(Nor thwallarea+roofarea))*ESGUwalIs *R[-6]C*(2.628*R[-56]C/24)) IF(R[-7]C<0,0,perimeter*IF(LEN( perimeterinsul)=3,1.39,2.77)*R[ -7]C*(2.628*R[-57]C/24)) IF(R[-8]C<0,0,ESGOinfi1trat i o n * R[-8]C*(2 .628*R[-58]C/24)) 115 " Q i n f 1 t r a t i o n ( l a t n t ) | 2 3 I F ( R [ - 9 ] C < 0 , 0 , f l o o r A r e a * L IF(R[-9]C<0,0,f1oorArea*LOOKUP( 00KUP(C0LUMN()-1.MonthlyR COLUMN()-1,MonthlyRadiation)*L0 a d i a t i o n ) * L O O K U P ( r o o f g l a z OKUP(roofglazing,Shortwavetrans ing,Shortwavetrans)*ESGra )*ESGradiationloss*0.7*30.4*0.5 d i a t i o n l o s s * 0 . 7 * 3 0 . 4 * 0 . 5 ) ) 116 "NIGHTTIME LOSSES" 117 "0 r o o f (MJ/month) |" 118 "0 N o r t h wal1 119 "0 E,W,S w a l l s 120 "0 p e r i m e t e r I" 121 " O i n f i 1 t r a t i o n ( s e n s ) | 122 " O i n f 1 t r a t i o n ( l a t n t ) | IF(R[-10]C<0,0,roofarea*E SGUroof*IF(thermal c u r t a i n =1,LOOKUP(9,ESMUroofval), 1)*R[-10]C*(2.628*(24-R[-S1]C)/24)) IF(R[-11]C<0,0,Northwalla rea*ESGUnorthwal1*IF(ther m a l c u r t a i n=1,L00KUP(9,ESM Uwal1val),1)*R[-11]C*(2.6 28*(24-R[-62]C)/24) ) I F ( R [ - 1 2 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a))*ESGUwal1s*IF(therma1c urtain=1,LOOKUP(9,ESMUwal 1val ) , 1 )*R[-12]C*(2.628*( 24-R[-63]C)/24)) IF(R[-10]C<0,0,roofarea*ESGUroo f*IF(thermalcurtain=1,L00KUP(9, ESMUroofval),1)*R[-10]C*(2.628* (24-R[-61]C)/24)) IF(R[-11]C<0,0,Northwallarea*ES GUnorthwal1*IF(thermalcurtain=1 ,LOOKUP(9,ESMUwal1val ) , 1)*R [ - 1 1 ]C*(2.628*(24-R[-62]C)/24)) IF(R[-12]C<0,0,(surfacearea-(No rthwal1area+roofarea))*ESGUwal 1 s*IF(thermal curtain=1,L00KUP(9, ESMUwal1val),1)*R[-12]C*(2.628* (24-R[-63]C)/24)) IF(R[-13]C<0,0,perimeter* I F ( R [ - 13]C<0,0,perimeter*IF(LEN I F ( L E N ( p e r i m e t e r i n s u l ) = 3, (perimeterinsu1)=3,1.39,2.77)*R 1.39,2.77)*R[-13]C*(2.628 [- 13]C*(2.628*(24-R[-64]C)/24)) *(24-R[-64]C)/24)) IF(R[-14]C<0,0,ESGO i nf i11 r a t i o n * R [ - 14]C*IF(thermal curtain=1,LOOKUP(9,ESMair change),1) *(2.628*(24-R[-65]C)/24)) IF(R[-61]C<0,0,(3600*24*3 0.4167*((24-R[-66]C)/24)) *2.45*(ESGairchanges/3600 )*IF(thermalcurtain=1,LOO KUP(9,ESMairchange),1)*vo lume*(R[-61]C/1000)) I F ( R [ - 14]C<0,0,ESGOinfi1trat ion *R[-14]C*IF(thermalcurtain=1,LO 0KUP(9,ESMairchange),1)*(2.628* (24-R[-65]C)/24) ) IF(R[-61]C<0,0,(3600*24*30.4167 *((24-R[-66]C)/24))*2.45*(ESGai r c h a n g e s / 3 6 0 0 ) * I F ( t h e r m a l c u r t a i n=1,LOOKUP(9,ESMairchange),1)*v o1ume*(R[-61]C/1000)) 123 "-124 "Monthly t o t a l s |" IF(-R[-15]C>SUM(R[-14]C:R I F ( - R [ - 15 ]C>SUM(R[- 14]C:R[-9]C) [-9]C),SUM(R[-7]C:R[-2]C) ,SUM(R[-7]C:R[-2]C),SUM(R[-15]C ,SUM(R[-15]C:R[-2]C)) :R[-2]C)) 125 126 "Fuel R e q u i r e d (MJ) |" 2 3 IF(AND(COLUMN()>=startmon IF(AND(COLUMN() > = startmonth+1,C th+1,COLUMN()<=endmonth+1 OLUMN()<=endmonth+1,COLUMN()<>o ,COLUMN()<>offmonth),R[-2 f f m o n t h ) , R [ - 2 ] C / h e a t i n g e f f i c i e n ] C / h e a t i n g e f f i c i e n c y , 0 ) cy,0) 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 "Cost of Fuel | " " Q t o t a l MJ/sq m y r " "Costs @ $0.405/100MJ:" n * * * * * * * * * * * * * * * * * * * * * * * CROP YIELD ANA LYSIS ***********************" "Annual Y i e l d ( c a s e s ) : " " P r i c e per c a s e : " ..****************** ECONOMIC ANALYSIS * * * * * * * * * * * * * * * * * * * * * * I  "E. $'s saved/year :" "Change i n S a l e s / y r : " "YEAR" ( c u r r e n t y e a r ) R [ - 1 ] C * f u e l c o s t R[-3]C[ + -12]/f l o o r A r e a R [ - 2 ] C * f u e l c o s t R[-1 ] C * f u e l c o s t "per sq m" g r e e n d a t a . y i e l d * f 1 o o r A r e a "Crop Value:" greendata.marketpr i c e ( r e f E c o s t - E S G E c o s t ) y i e l d f a c t o r * y e a r l y S a l e s "+/- SALES" ( 1 - t a x ) * d e l t a S a l e s "Loan Payment:" "E $'s SAVED" (1 - t a x ) * y e a r 1 y s a v i n g 144 ( c u r r e n t y e a r ) 145 ( c u r r e n t y e a r ) 146 ( c u r r e n t y e a r ) 147 ( c u r r e n t y e a r ) 148 ( c u r r e n t y e a r ) 149 ( c u r r e n t y e a r ) 150 ( c u r r e n t y e a r ) 151 ( c u r r e n t y e a r ) R [ - 1 ] C * ( 1 + i n f l a t i o n ) R[-1]C*(1+esc) R [ - 1 ] C * ( 1 + i n f l a t i o n ) R[-1]C* R [ - 1 ] C * ( 1 + i n f l a t i o n ) R[-1]C* R [ - 1 ] C * ( 1 + i n f l a t i o n ) R[-1]C*(1 + i n f l a t i o n ) R[-1 ]C*(1 + i n f l a t i o n ) R[- 1]C*(1 + i n f l a t ion) R[-1 ]C*(1 + i n f l a t i o n ) R[-1 ]C* R[-1 ]C* R[-1 ]C* R[-1 ]C* R[-1 ]C* 1+esc) 1+esc) 1+esc) 1+esc) 1+esc) 1+esc) 1+esc) 152 ( c u r r e n t y e a r ) R[-1 ]C*(1 + i n f l a t i o n ) R [ - 1 ]C* 1+esc) 1 153 ( c u r r e n t y e a r ) 2 R[-1 ]C*(1 + i n f l a t i o n ) 154 ( c u r r e n t y e a r ) 155 ( c u r r e n t y e a r ) 156 ( c u r r e n t y e a r ) 157 ( c u r r e n t y e a r ) R[-1 ] C * ( 1 + i n f l a t i o n ) R[-1 ] C * ( 1 + i n f l a t i o n ) R [ - 1 ] C * ( 1 + i n f l a t i o n ) R [ - 1 ]C*(1 + i n f 1 at ion) 158 159 160 "***************** LOOKUP TABLES BEGIN HERE ******************** "GLAZING TABLE 161 162 "GLAZING" 163 1 2 3 4 5 6 7 164 165 166 167 168 169 170 8 171 9 172 173 "U v a l u e s a r e o v e r a l l heat l o s s (W/m2 C ) based on 25 km/hr wind" 174 " o u t s i d e and no wind i n s i d e . Taken f r o m Blom 1982. " 175 176 "WEATHER TABLE Average weather d a t a f o r t h e Vancouver a r e a . " 177 » "NUMBER" "Gl a s s 3 mm " "double g l a s s " " p o l y e t h y 1 e n e " "double p o l y " " p o l y + g l a s s " " f i b r g l a s f l a t " " f i b r g l a s ' "1 i n . s t y r f o a m " "Double a c r y l " 178 179 "MONTH" "Number" 180 "January 1 181 " F e b r u a r y 2 182 "March 3 183 "Apri1 4 184 "May 5 185 "June 6 186 " J u l y 7 R[ -1 ]C*(1+esc) R[- 1 ]C*(1+esc) R[- 1 ]C*(1+esc) R[-1 ]C*(1+esc) R[-1 ]C*(1+esc) "U VALUE " 6 . 45 3.7 6.53 4.25 4 5.68 6.53 0.97 3. 12 "Avg Low" -0. 27 0.96 2 . 3 4 .83 7.84 10. 74 12.47 187 188 189 190 191 192 193 194 195 196 197 198 199 "August "September "October "November "December AIRCHANGE TABLE i n greenhouse a i r c h a n g e s / h r " V a l u e s "new g l a s s " " o l d g l a s s " "new d o u b l e p o l y " " o l d d o u b l e p o l y " 8 9 10 1 1 12 "AGEcode" 0 1 2 3 200 " ESM TABLE Adjustment f a c t o r s f o r ESM used: f r a c t i o n of 1 or $/m2" 201 " 202 203 'ESM 204 II 205 II 206 11 207 " 208 II 209 " 210 21 1 t l 212 It 213 214 215 " 216 11 217 1 218 2 219 3 220 221 II 222 223 " 224 1 225 2 226 3 227 4 Root Zone H e a t i n g " S t a c k Heat Recovery" I.R. H e a t i n g " Computer C o n t r o l " Reduced Leakage" P o l y on G l a s s " N Wal1 I n s u l a t i o n " 1 meter I n s u l a t i o n " Thermal C u r t a i n s " Heat S t o r a g e " FUEL COST TABLE ($/Mu)" CROP DATA TABLE From R i c k ' "Number 1 2 3 4 5 6 7 8 9 10 12 "TYPE " " O i l " "Natural Gas " E l e c t r i c i ty "CROP" "Cucumber" "Tomato" 12.43 9.9 6.46 2.9 1 . 24 "Airchange range" 0.75 2 0.8 1 .5 "Uwal1val" 98 68 74 47 "COST $/MJ" 0.00817132 0.00404907 0.014598 "ck Wallace (B.C. Hothouse Prod u c t s ) 1982" "YIELD" "CASE/SO M" 4.12 2.45 4 6 " ( V ) a r i a b l e s " "Crop ( Y ) i e l d " " ( I ) n s t r u c t i o n s " 9 10 11 12 13 [ g r e e n d a t a name] [ g r e e n d a t a c u r r e n t y e a r ] FIXED(floorArea,2)8." m2" FIXED(greendata.age,0) &" y e a r s o l d " L00 K U P ( g l a z i n g , g l a z i n g t a b l e g r e e n d a t a . g l a z i ng ) L O O K U P ( r o o f g l a z i n g , g l a z i n g t g r e e n d a t a . r o o f g l a z i n g a b l e ) 14 L00KUP(fuel t y p e , f u e l t a b l e ) g r e e n d a t a . f u e l type "********* VARIABLES TRAN SFERRED FROM GREENDATA ** ************ Uni t s of l e n g t h : " Houselength 15 L 0 0 K U P ( c r o p , c r o p t a b l e ) g r e e n d a t a . c r o p Housew i d t h 16 g r e e n d a t a . i n t "Night Temp." Wal1 h e i g h t 17 g r e e n d a t a . e s c g r e e n d a t a . n i g h t t e m p Day temp. C 18 g r e e n d a t a . i n f 1 a t i o n "Day Temp." Night temp. C 19 g r e e n d a t a . t a x greendata.daytemp 20 g r eendata.1oan "ADJUSTMENTS" Lea k i n e s s e s t . 21 g r e e n d a t a . L o a n l n t R a t e 7. L i g h t ' Or i e n t a t i on 22 g r e e n d a t a . m a r k e t p r i c e 23 c r o p y i e l d . 24 i n s t a l c o s t . 25 m a i n t c o s t . 26 f u e l c o s t . - ( 1 - E S G r a d i a t i o n l o s s ) "% F u e l " - ( h e a t i n g e f f i c i e n c y - 0 . 7 ) / " 0.7 "% Y i e l d " y i e l d f a c t o r " Fuel Cost $/MJ :" Crop Type :" M a r k e t p r i c e :" Y i e l d :" 27 "Old House" 28 r e f U n i t E c o s t 29 Qref 4 "With E Save ESGUni t E c o s t O t o t a l 5 6 Startmonth Endmonth 7 30 31 32 33 34 35 36 37 38 39 40 43 44 " 0=new 1=old" 45 r o o f g l a z i n g 46 47 48 49 " g / c u b i c M" 50 " g / c u b i c M" 51 vo 52 «3 41 "offmonth= IF(startmonth-endmonth>0, startmonth-1,0) 42 53 54 55 "Mar" "Apr 1 "May" " d u n " 56 12 57 LOOKUP(COLUMN()-1.Hightemp) 58 LOOKUP(COLUMN()-1,Lowtemp) 59 daytemp-R[-2]C 60 nighttemp-R[-2]C 61 vapInNight-(LOOKUP(COLUMN() - 1 , h u m i d i t y t a b l e ) * ( ( 1322/(R [-3]C+273.2))*(10a((R[-3]C* 7 . 5 ) / ( R [ - 3 ] C + 2 3 7 . 3 ) ) ) ) ) 62 63 I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * L O 0KUP(C0LUMN()-1,MonthlyRadi at i o n ) * L O O K U P ( r o o f g l a z i ng,S nort w a v e t r a n s ) * 0 . 7 * 3 0 . 4 ) 64 I F ( R [ - 5 ] C < 0 , 0 , r o o f a r e a * U r o o f*R[-5]C*(2.628*R[-8]C/24)) 65 I F ( R [ - 6 ] C < 0 , 0 , N o r t h w a l l a r e a *Uwalls*R[-6]C*(2.628*R[-9] C/24)) 66 I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a -( N o r t h w a l 1 a r e a + r o o f a r e a ) ) * U w a l l s * R [ - 7 ] C * ( 2 . 6 2 8 * R [ - 1 0 ] C /24)) 67 I F ( R [ - 8 ] C < 0 , 0 , p e r i m e t e r * I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 . 3 9 , 2.77)*R[-8]C*2.628*R[-11]C/ 24) 68 I F ( R [ - 9 ] C < O , O , 0 i n f i l t r a t i o n *R[-9]C*(2.628*R[-12]C/24) ) 69 I F ( R [ - 1 0 ] C < 0 , 0 , f l o o r A r e a * L 0 0KUP(C0LUMN()-1.MonthlyRadi at 1 o n ) * LOOKUP(roofglaz i ng,S hortwavetrans)*0.5*0.7*30.4 ) 70 71 I F ( R [ - 1 1 ] C < 0 , 0 , r o o f a r e a * U r o of*R[-11]C*(2.628*(24-R[-15 ] C ) / 2 4 ) ) 14 LOOKUP(C0LUMN()-1.Hightem P) L00KUP(C0LUMN()-1,Lowtemp ) daytemp-R[-2]C nighttemp-R[-2 ]C vapInNight-(LOOKUP(COLUMN ()-1,humidi t y t a b l e ) * ( ( 1 3 2 2/(R[-3]C+273.2))*(10a((R [-3]C*7 . 5)/(R[-3]C+237.3) ) ) ) ) I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly R a d i a t ion)*LOOKUP(roofgla z i n g ,Shortwavetrans)*0.7* 30.4) IF(R[-5]C<0,0, r o o f a r e a * U r oof *R[.-5]C*(2 .628*R[-8]C/ 24)) IF(R[-6]C<0,0,Northwallar ea*Uwal1s*R[-6]C*(2.628*R t-9]C/24)) I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ) )*Uwalls*R[-7]C*(2.628*R [-10]C/24)) IF(R[-8]C<0,0,perimeter* I F ( L E N ( p e r i m e t e r 1nsu1) = 3,1 .39,2.77)*R[-8]C*2.628*R[ -11]C/24) I F ( R [ - 9 ] C < O , O , 0 i n f i l t r a t i on*R[-9]C*(2.628*R[-12]C/ 24)) I F ( R [ - 1 0 ] C < 0 , 0 , f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Rad i a t i on) * LOOKUP(roofg1 a z i n g ,Shortwavetrans)*0.5* 0.7*30.4) IF(R[-11]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R [-15]C)/24)) 15.5 LOOKUP(C0LUMN()-1.Hightem P) LOOKUP(C0LUMN()-1.Lowtemp ) daytemp-R[-2]C nighttemp-R[-2]C vapInNight-(LOOKUP(COLUMN ( ) - 1 , h u m i d i t y t a b l e ) * ( ( 1 3 2 2/(R[-3]C+273.2))*(1Oct((R [-3]C*7.5)/(R[-3]C+237.3) )))) I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Radiat ion)*LOOKUP(roofgla z i ng,Shortwavetrans)*0.7* 30.4) IF(R[-5]C<0,0,roofarea*Ur oof*R[-5]C*(2.628*R[-8]C/ 24)) IF(R[-6]C<0,0,Northwallar ea*Uwal1s*R[-6]C*(2.628*R [ - 9 1 C / 2 4 ) ) I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a-(Northwal1area+roofarea ))*Uwalls*R[-7]C*(2.628*R t-10]C/24)) IF(R[-8]C<0,0,perimeter*I F ( L E N ( p e r i m e t e r i n s u l ) = 3, 1 .39,2.77)*R[-8]C*2.628*R[ -11 ]C/24) I F ( R [ - 9 ] C < 0 , 0 , Q i n f i l t r a t i on*R[-9]C*(2.628*R[-12]C/ 24)) I F ( R [ - 1 0 ] C < 0 , 0 , f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Rad1 at i on)* LOOKUP(roofgl a z i ng,Shortwavetrans)*0.5* 0.7*30.4) IF(R[-11]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R [-15]C)/24)) 16 L00KUP(C0LUMN()-1.Hightem P) L00KUP(C0LUMN()-1.Lowtemp ) daytemp-R[-2]C nighttemp-R[-2]C vaplnN i ght-(LOOKUP(COLUMN ( ) - 1 , h u m i d i t y t a b l e ) * ( ( 1 3 2 2/(R[-3]C+273.2))*(10a((R [-3]C*7.5)/(R[-3]C+237.3) )))) I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Radi at i on)*LOOKUP(roofgla z i ng,Shortwavetrans)*0.7* 30.4) IF(R[-5]C<0,0,roofarea*Ur oof*R[-5]C*(2.628*R[-8]C/ 24)) IF(R[-6]C<0,0,Northwal l a r ea*Uwal1s*R[-6]C*(2.628*R [-91C/24)) IF( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a-(Northwa11area+roofarea ))*Uwalls*R[-7]C*(2.628*R [-10]C/24)) IF(R[-8]C<0,0,perimeter*I F ( L E N ( p e r i m e t e r i n s u 1 ) = 3, 1 .39,2.77)*R[-8]C*2.628*R[ -11 ]C/24) I F ( R [ - 9 ] C < 0 , 0 , O i n f i 1 t r a t i on*R[-9]C*(2.628*R[-12]C/ 24)) IF( R [ - 1 0 ] C < 0 , 0 , f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Rad i at i o n )* LOOKUP(roofgla z i ng,Shortwavetrans)*0.5* 0.7*30.4) I F ( R [ - 11]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R [-15]C)/24)) 72 I F ( R [ - 1 2 ] C < 0 , 0 , N o r t h w a l l a r e a*Uwalls*R[-12]C*(2.628*(24 - R [ - 1 6 ] C ) / 2 4 ) ) 73 I F ( R [ - 1 3 ] C < 0 , 0 . ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ) ) * Uwalls*R[-13]C*2.628*(24-R[ -17]C)/24) 74 I F ( R [ - 1 4 ] C < 0 , 0 , p e r i m e t e r * I F ( LEN(per i m e t e r i nsul)=3,1.39 ,2.77)*R[-14]C*2.628*(24-R[ -18]C)/24) 75 I F ( R [ - 1 5 ] C < 0 , 0 , Q i n f i l t r a t i o n*R[-15]C*2.628*(24-R[-19]C )/24) 76 IF(R[-15]C<0,0,(3600*24*30. 4167*((24-R[-20]C)/24))*2.4 5 * ( a i rchanges/3600)*volume* (R[-15]C/1000)) 77 IF(R[-12]C<0,0,Northwalla rea*Uwal1s*R[- 12]C*(2.628 *(24-R[-16]C)/24)) I F ( R [ - 1 3 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a))*Uwalls*R[-13]C*2.628* (24-R[-17]C)/24) IF(R[-14]C<0,0,peri meter* I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1.39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) I F ( R [ - 1 5 ] C < O , O , 0 i n f i l t r a t ion*R[-15]C*2.628*(24-R[-19]C)/24) IF(R[-15]C<0,0,(3600*24*3 0.4167*((24-R[-20]C)/24)) * 2 . 4 5 * ( a i rchanges/3600)*v olume*(R[-15]C/1000)) IF(R[-12]C<0,0,Northwal l a rea*Uwal1 s*R[- 12]C*(2.628 *(24-R[-16]C)/24)) IF(R[-13]C<0,0,(surfacear ea-(Northwal1area+roofare a))*Uwalls*R[-13]C*2.628* (24-R[-17]C)/24) IF(R[-14]C<0,0,perimeter* IF(LEN(per1meterinsul)=3, 1 .39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) I F ( R [ - 1 5 ] C < 0 , 0 , O i n f i l t r a t ion*R[-15]C*2.628*(24-R[-19]C)/24) IF(R[-15]C<0,0,(3600*24*3 0.4167*((24-R[-20]C)/24)) *2.45*(a i rchanges/3600)*v olume*(R[-15]C/1000)) IF(R[-12]C<0,0,Northwalla rea*Uwal1s*R[- 12]C*(2.628 *(24-R[-16]C)/24)) IF(R[-13]C<0,0,(surfacear ea-(Northwal1area+roofare a) )*Uwalls*R[-13]C*2.628* (24-R[-17]C)/24) IF(R[-14]C<0,0,perimeter* I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 .39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) I F ( R t - 15]C<0,0,Oinf11trat ion*R[-15]C*2.628*(24-R[-19]C)/24) IF(R[-15]C<0,0,(3600*24*3 0.4167*((24-R[-20]C)/24)) *2 . 45*(a i rchanges/3600)*v olume*(R[-15]C/1000)) 78 IF(-R[-15]C>SUM(R[-14]C:R[- I F ( - R [ - 15]OSUM(R[- 14]C:R I F ( - R [ - 15]OSUM(R[- 14]C:R I F ( - R [ - 15]C>SUM(R[- 14]C:R 9]C),SUM(R[-7]C:R[-2]C),SUM [-9]C),SUM(R[-7]C:R[-2]C) [-9 ]C),SUM(R[-7]C:R[-2]C) [-9]C) ,SUM(R[-7]C:R[-2]C) ( R [ - 1 5 ] C : R [ - 2 ] C ) ) ,SUM(R[- 15]C:R[-2]C)) ,SUM(R[- 15]C:R[-2]C)) , SUM(R[- 15]C:R[-2]C)) 79 80 81 82 83 84 85 86 IF(AND(C0LUMN()>=startmonth +1,C0LUMN()<=endmonth+1,C0L UMN()<>offmonth+1),R[-2]C/0 .7.0) R[-1 ] C * f u e l c o s t RC[-2]/10.8 IF(AND(C0LUMN()>=startmon th+1,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth+1),R[ -2]C/0.7,0) R t - 1 ] C * f u e l c o s t "per sq f t " IF(AND(COLUMN()>=startmon th+1,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth+1),R[ -2]C/0.7,0) R[- 1 ] C * f u e l c o s t IF(AND(COLUMN()>=startmon th+1,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth+1),R[ -2JC/0.7.0) R[-1 ] C * f u e l c o s t 87 88 " i f I m e t e r i n s u l " 89 IF(onemeterinsul=1,L00KUP(8 "W/sq m * C" ,ESMUwal1val)*RC[-1],RC[-1 } ) 90 91 92 93 " i f N w a l 1 i n s u l " " i f I m e t e r l n s " 4 5 94 IF(Nwal1insul=1,L00KUP(7,ES IF(onemeter1nsu1=1,LOOKUP Mairchange)*RC[-1],RC[-1 ] ) (8,ESMa i r change)*RC[- 1],R C [ - 1 ] ) 95 " volumes/hr" 96 97 98 99 100 101 102 " i f IRheat" " i f computer" " i f h e a t s t o r a g e " IF( IRheat=1,LOOKUP(3,ESMhea IF(computer=1,LOOKUP(4,ES IF(heatstorage=1,LOOKUP(1 t e f f i c i e n c y ) * R C [ - 1 ],RC[- 1 ] ) M h e a t e f f i c i e n c y )*RC[- 1 ],R 0, ESMheateff i c i e n c y ) * R C [ -C [ - 1 ] ) 1].RC[-1]) " i f N w a l l i n s u l " " i f t h r m l c u r t n " I F ( N w a l 1 i n s u l = 1,L00KUP(7,ES IF(therma1 curtain=1,LOOKU " f r a c t i o n of l i g h t transm MRtrans)*RC[- 1 ],RC[-1 ]) P(9,ESMRtrans)*RC[- 1],RC[ i t t e d w i t h energy conserv -1]) a t i o n " 103 104 "Mar" 105 "Apr" "May" " Jun" 106 daytemp-L00KUP(C0LUMN()-1,H ightemp) 107 nighttemp-LO0KUP(COLUMN()-1 , Lowtemp) 108 109 I F ( R [ - 3 ] C < 0 , 0 , - f l o o r A r e a * L O OKUP(COLUMN()-1.MonthlyRadi a t i o n ) * L O O K U P ( r o o f g l a z i n g , S hortwavetrans)*0.7*30.4*ESG r a d 1 a t i o n l o s s ) 110 IF(R[-4]C<0,0,roofarea*ESGU r o o f * R [ - 4 ] C * ( 2 . 6 2 8 * R [ - 5 4 ] C / 24)) 111 I F ( R [ - 5 ] C < 0 , O , N o r t h w a l l a r e a *ESGUnorthwal1*Rt-5]C*(2.62 8*R[-55]C/24) ) 112 I F ( R [ - 6 ] C < 0 , 0 , ( s u r f a c e a r e a -( N o r t h w a 1 1 a r e a + r o o f a r e a ) ) * E SGUwalls*R[-6]C*(2.628*R[-5 6]C/24)) 113 I F ( R [ - 7 ] C < 0 , 0 , p e r i m e t e r * I F ( LEN(per imeter i nsul)=3,1.39, 2.77)*R[-7]C*(2.628*R[-57]C /24)) 114 I F ( R [ - 8 ] C < 0 , 0 , E S G Q i n f i 1 t r a t ion*R[-8]C*(2.628*R[-58]C/2 4 ) ) daytemp-LOOKUP(COLUMN()-1 daytemp-LOOKUP(COLUMN()-1 .Hightemp) .Hightemp) nighttemp-LOOKUP(COLUMN() nighttemp-LOOKUP(COLUMN() -1,Lowtemp) -1,Lowtemp) I F ( R [ - 3 ] C < 0 , 0 , - f l o o r A r e a * LOOKUP(COLUMN()-1.Monthly R a d i a t i o n ) * L O O K U P ( r o o f g l a z i n g , S h o r t w a v e t r a n s ) * 0 . 7 * 3 0 . 4 * E S G r a d i a t i o n l o s s ) IF(R[-4]C<0,0,roofarea*ES GUroof*R[-4]C*(2.628*R[-5 4]C/24)) I F ( R [ - 5 ] C < 0 , 0 , N o r t h w a l l a r ea*ESGUnorthwal1*R[-5]C*( 2.628*R[-55]C/24)) I F ( R [ - 6 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ))*ESGUwalls*R[-6]C*(2.62 8*R[-56]C/24)) IF(R[-7]C<0,0,perimeter*I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 .39,2.77)*R[-7]C*(2.628*R [-57]C/24)) I F ( R [ - 8 ] C < 0 , 0 , E S G O i n f i 1 t r ation*R[-8]C*(2.628*R[-58 ]C/24)) I F ( R [ - 3 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Rad i a t i o n )*L00KUP(roofgla z i ng,Shortwavetrans)*0.7* 3 0 . 4 * E S G r a d i a t i o n l o s s ) IF(R[-4]C<0,0,roofarea*ES GUroof*R[-4]C*(2.628*R[-5 4]C/24)) IF(R[-5]C<0,0,Northwal l a r ea*ESGUnorthwal1*R[-5]C*( 2.628*R[-55]C/24)) IF ( R [ - 6 ] C < 0 , 0 , ( s u r f a c e a r e a-(Northwal1area+roofarea ) )*ESGUwal1s*R[-6]C*(2.62 8*R[-56]C/24)) IF(R[-7]C<0,0,perimeter*I F ( L E N ( p e r i m e t e r i n s u l ) = 3, 1 .39,2.77)*R[-7]C*(2.628*R [-57]C/24)) I F ( R [ - 8 ] C < 0 , 0 , E S G Q i n f i l t r ation*R[-8]C*(2.628*R[-58 ]C/24)) daytemp-L00KUP(C0LUMN( )-1 ,Hightemp) nighttemp-L00KUP(C0LUMN() - 1,Lowtemp) IF ( R [ - 3 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Radi at i on)*LOOKUP(roofgla z i n g , S h o r t w a v e t r a n s ) * 0 . 7 * 30.4*ESGradi at i o n l o s s ) IF(R[-4]C<0,0,roofarea*ES GUroof*R[-4]C*(2.628*R[-5 4]C/24)) IF(R[-5]C<0,0,Northwallar ea*ESGUnorthwal1*R[-5]C*( 2.628*R[-55]C/24)) IF ( R [ - 6 ] C < 0 , 0 , ( s u r f a c e a r e a-(Northwa11area+roofarea ))*ESGUwalls*R[-6]C*(2.62 8*R[-56]C/24)) IF(R[-7]C<0,0,perimeter*I F(LEN(per i m e t e r i n s u l )=3, 1 .39,2.77)*R[-7]C*(2.628*R [-57]C/24)) IF(Rt-8]C<0,0,ESGQinfi 1 t r ation*R[-8]C*(2.628*R[-58 ]C/24)) O 115 IF(R[-9]C<0,0,f1oorArea*L00 IF(R[-9]C<0,0,f1oorArea*L IF(R[-9]C<0,0,f1oorArea*L IF(R[-9]C<0,0,floorArea*L KUP(C0LUMN( )-1.MonthlyRadia 00KUP(C0LUMN()-1.MonthlyR 00KUP(C0LUMN()-1,MonthlyR OOKUP(C0LUMN()-1.MonthlyR t i o n ) * L O O K U P ( r o o f g l a z i ng,Sh a d i a t i on)*LOOKUP(roofglaz a d i a t ion)*LOOKUP(roofgl az adi at ion)*LOOKUP(roofgl az o r t w a v e t r a n s ) * E S G r a d i a t i o n l i n g , S h o r t w a v e t r a n s ) * E S G r a ing,Shortwavetrans)*ESGra ing,Shortwavetrans)*ESGra oss*0.7*30.4*0. 5) d i a t i o n l o s s * 0 . 7 * 3 0 . 4 * 0 . 5 ) d i a t i o n l o s s * 0 . 7 * 3 0 . 4 * 0 . 5 ) d i a t i o n l o s s * 0 . 7 * 3 0 . 4 * 0 . 5 ) 1 16 117 IF(R[-10]C<0,0,roofarea*ESG I F ( R [ - 10]C<0,0,roofarea*E IF(R[-10]C<0,0,roofarea*E I F ( R [ - 10]C<0,0,roofarea*E U r o o f * I F (thermal c u r t a i n= 1 , L SGUroof * IF( t h e r m a l c u r t a i n SGUroof*IF(thermal c u r t a i n SGUroof* IF(thermal c u r t a i n 00KUP(9,ESMUroofval ) ,1)*R[- =1 , LOOKUP(9,ESMUroofva 1 ) , =1 , LOOKUP(9,ESMUroofval ) , =1 , LOOKUP(9,ESMUroofval ) , 10]C*(2 .628*(24-R[-61]C)/24 1)*R[- 10]C*(2.628*(24-R[- 1 ) *R[- 10]C*(2.628*(24-R[- 1 ) *R[- 10]C*(2.628*(24-R[-)) 6 1 ] C ) / 2 4 ) ) 61]C)/24)) 61]C)/24)) 118 I F ( R [ - 1 1 ] C < 0 , 0 , N o r t h w a l l a r e IF ( R[- 11]C<0.0.Northwa11 a I F ( R [ - 11]C<0.0,Northwa11 a I F ( R [ - 11]C<0,0,Northwa 1 1 a a*ESGUnorthwal1*IF(thermal c r e a * E S G U n o r t h w a l 1 * I F ( t h e r rea*ESGUnorthwa11*IF(ther rea*ESGUnorthwal1*IF( t h e r urtain=1,LOOKUP(9.ESMUwa11v malcurtain=1,LOOKUP(9,ESM malcurtain=1.L00KUP(9,ESM malcurtain=1.LOOKUP(9 , ESM a l ) , 1 ) * R [ - 1 1 ] C * ( 2 . 6 2 8 * ( 2 4 - R U w a 1 l v a l ) , 1 ) * R [ - 1 1 ] C * ( 2 . 6 Uwal1va1), 1 ) *R[-11]C*(2.6 Uwa11va1) , 1 ) *R[- 11]C*(2.6 [-62]C)/24)) 28*(24-R[-62]C)/24) ) 28*(24-R[-62]C)/24)) 28*(24-R[-62]C)/24)) 119 I F ( R [ - 1 2 ] C < 0 , 0 , ( s u r f a c e a r e a I F ( R [ - 12]C<0,0,(surfacear IF(R[-12]C<0,0,(surfacear IF(R[-12]C<0,0,(surfacear - ( N o r t h w a l 1 a r e a + r o o f a r e a ) )* e a - ( N o r t h w a l 1 a r e a + r o o f a r e ea-(Northwal1area+roofare ea-(Northwal1area+roofare ESGUwal 1 s * I F ( thermal c u r t a i n a))*ESGUwal1s*IF(therma1c a) )*ESGUwal1s*IF(therma1c a) )*ESGUwal1s*IF(thermalc = 1 , LOOKUP(9,ESMUwa11val ), 1 ) urtain=1,LOOKUP(9,ESMUwa1 urtain=1,LOOKUP(9,ESMUwa 1 urtain=1,LOOKUP(9, ESMUwa 1 *R[-12]C*(2.628*(24-R[-63]C 1val ) , 1 ) *R[- 12]C*(2.628*( 1 v a l ) , 1 ) *R[-12]C*(2.628* ( 1 val ) , 1 ) *R[- 12]C*(2.628*( )/24)) 24-R[-63]C)/24)) 24-R[-63]C)/24)) 24-R[-63]C)/24)) 120 I F ( R [ - 1 3 ] C < 0 , 0 , p e r i m e t e r * I F I F ( R [ - 13]C<0,0.perimeter* I F ( R [ - 13]C<0,0,perimeter* I F ( R [ - 1 3]C<0.0,perimeter* (LEN( per imeter in s u 1 ) = 3,1.39 I F ( L E N ( p e r imeter insu1) = 3, I F ( L E N ( p e r i m e t e r i nsul ) = 3, IF(LEN(per imeter i nsul )=3, ,2.77)*R[-13]C*(2.628*(24-R 1 . 39,2 . 77 ) *R[- 13 ] C*(2.628 1 . 39 , 2 . 77)*R[- 13]C*(2.628 1 . 39 , 2 . 77)*R[- 13]C* ( 2.628 [-64]C)/24)) *(24-R[-64]C)/24)) *(24-R[-64]C)/24)) *(24-R[-64]C)/24)) 121 I F ( R [ - 1 4 ] C < 0 , 0 , E S G 0 1 n f i l t r a I F ( R [ - 14]C<0,0,ESGQinf111 I F ( R [ - 14]C<0.0,ESGOinfi 11 I F ( R [ - 14]C<0,0,ESGOinf1 It t ion*R[ - 1 4 ] C * I F ( t h e r m a l c u r t r a t i o n * R [ - 14]C*IF(thermal r a t i o n * R [ - 14]C*IF(therma1 r a t i o n * R [ - 14]C*IF(therma1 ain=1 , LOOKUP(9,ESMairchange curtain=1,LOOKUP(9,ESMa i r curtain=1,LOOKUP(9,ESMair curtain=1,LOOKUP(9,ESMai r ), 1 )*(2 .628*(24-R[-65]C)/24 change),1 )*(2.628*(24-R[- change),1 )*(2.628*(24-R[- change),1 )*(2.628*(24-R[-)) 6 5 ] C ) / 2 4 ) ) 65]C)/24)) 65]C)/24)) 122 IF(R[-61]C<0,0,(3600*24*30. IF(R[-61]C<0,0,(3600*24*3 IF(R[-61]C<0,0,(3600*24*3 IF(R[-61]C<0,0.(3600*24*3 4 1 6 7 * ( ( 2 4 - R [ - 6 6 ] C ) / 2 4 ) ) * 2 .4 0.4167*((24-R[-66]C )/24)) 0 . 4 167*((24-R[-66]C)/24)) 0. 4 167*((24-Rt-66]C)/24)) 5 * ( E S G a i r c h a n g e s / 3 6 0 0 ) * I F ( t *2.45*(ESGairchanges/3600 *2.45*(ESGai rchanges/3600 *2.45*(ESGai rchanges/3600 hernial curtain=1,LOOKUP(9,ES )* IF (thermal c u r t a i n=1,LOO )* IF(thermal curtain=1,LOO )* IF(therma1 curtain=1,LOO Mai r c h a n g e ) , 1 ) * v o l u m e * ( R [ - 6 KUP(9,ESMai rchange),1)*vo KUP(9,ESMa i rchange), 1)*vo KUP(9,ESMa i rchange), 1)*vo 1]C/1000)) lume*(R[-61]C/1000)) 1ume*(R[-61]C/1000)) 1ume*(R[-61]C/1000)) 123 124 IF(-R[-15]C>SUM(R[-14]C:R[- I F ( - R [ - 15 ] OSUM(R[- 14]C:R I F ( - R [ - 15]C>SUM(R[- 14]C : R I F ( - R [ - 15]C>SUM(R[- 14]C:R 9]C),SUM(R[-7]C:R[-2]C),SUM t~9]C),.SUM(R[-7]C:R[-2]C) [-9]C) .SUM(R[-7]C:R[-2]C) [-9]C) ,SUM(R[-7]C:R[ -2]C) ( R [ - 1 5 ] C : R [ - 2 ] C ) ) ,SUM(R[- 15]C:R[-2]C)) ,SUM(R[-15]C:R [-2]C)) , SUM(R[- 15]C:R[-2 ]C ) ) 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 4 5 IF(AND(COLUMN()>=startmonth IF(AND(COLUMN()>=startmon +1,COLUMN()<=endmonth+1,COL th+1,COLUMN() < = endmonth+1 U M N ( ) o o f fmonth),R[-2]C/hea ,COLUMN( )<>offmonth),R[-2 11ngeff i c i ency,0) ]C/heat i n g e f f i c i ency,0) 6 7 IF(AND(COLUMN()>=startmon IF(AND(COLUMN()>=startmon th+1,COLUMN()<=endmonth+1 th+1,COLUMN()<=endmonth+1 ,COLUMN( ) o o f fmonth) ,R[-2 , COLUMN ( ) o o f fmonth) ,R[-2 ]C/heat1ngef f i c iency,0) ]C/heat i ngef f i c i ency.0) R [ - 1 ] C * f u e l c o s t "per sq f t ' R [ - 1 ] C * f u e l c o s t RC[-2]/10.8 c r o p y i e l d * c a s e p r i ce l o a n / ( ( 1 - ( 1 / ( 1 + ( L o a n I n t R a t e " a n n u a l l y " ) ) a 1 5 ) ) / ( L o a n I n t R a t e ) ) "MAINT" ( 1 - t a x ) * m a i n t c o s t 144 R [ - 1 ] C * ( 1 + i n f l a t l o n ) 145 R [ - 1 ] C * ( 1 + i n f l a t i o n ) 146 R [ - 1 ] C * ( 1 + i n f l a t i o n ) 147 R t - 1 ] C * ( 1 + i n f l a t i o n ) 148 R [ - 1 ] C * ( 1 + i n f l a t i o n ) 149 R [ - 1 ] C * ( 1 + i n f l a t i o n ) 150 R [ - 1 ] C * ( 1 + i n f l a t i o n ) 151 R [ - 1 ] C * ( 1 + i n f l a t i o n ) 152 R [ - 1 ] C * ( 1 + i n f l a t i o n ) "LOAN BAL" 1 oan Rt-1 ]C-R[-1]C[+2] R[-1]C-Rt-1]C[+2] R[-1 ]C-R[-1]C[+2] R[- 1 ]C-R[- 1]C[+2] R[-1]C-R[ - 1 ]C[+2] R[-1 ]C-R[- 1 ]Ct+2] R[-1 ]C-R[- 1 ]C[+2] R[-1]C-R[-1]Ct+2] R[-1]C-R[-1 ]C[+2] R[-1 ] C * f u e l c o s t R[-1 ] C * f u e l c o s t "INTEREST" tax ) * ( L o a n I n t R a t e ) * R C [ t a x ) * ( L o a n I n t R a t e ) * R C [ t a x ) * ( L o a n I n t R a t e ) * R C [ tax )*(LoanIntRate)*RC[ ta x ) * ( L o a n I n t R a t e ) * R C [ t a x ) * ( L o a n I n t R a t e ) * R C [ t a x ) * ( L o a n I n t R a t e ) * R C [ t a x ) * ( L o a n I n t R a t e ) * R C [ t a x ) * ( L o a n I n t R a t e ) * R C [ tax)*(Loan!ntRate)*RC[ "PRINCIPAL" 1oanpayment x)) 1oanpayment x)) 1oanpayment x)) 1oanpayment x)) 1oanpayment x)) 1oanpayment x)) 1oanpayment x)) 1oanpayment x)) 1oanpayment x)) 1oanpayment x)) (RC[-(RC[-•(RCt-(RC[-•(RC[-(RC[-(RC[-(RC[-(RC[-(RC[-1 ] / ( 1 - t a 1 ] / ( 1 - t a 1 ] / ( 1 - t a 1]/( 1-ta l ] / ( 1 - t a 1 ] / ( 1 - t a 1 ] / ( 1 - t a 1 ] / ( 1 - t a 1 ] / ( 1 - t a 1 ] / ( 1 - t a o i t . 4 5 153 R[-1]C*(1 + i n f l a t i o n ) R[- 1 ]C-R[- 1]C[ 154 R[-1]C*(1 + i n f l a t i o n ) R[- 1 ]C-R[- 1 ]C[ 155 R[-1]C*(1 + i n f l a t i o n ) R[- 1 ]C-R[- 1]C[ 156 R [ - 1 ] C * ( 1 + i n f l a t i o n ) R [ - 1]C-R[- 1]C[ 157 R[- 1]C*(1 + i n f l a t i o n ) R [ - 1 ]C-R[- 1 ]C[ 158 159 160 161 162 "Short T" 163 0. 88 164 0. 75 165 0. 89 166 0. 76 167 0. 75 168 0. 78 169 0. 78 170 0 171 0. 85 172 173 174 175 176 1983 177 178 "Avg High" " R a d i a t i o n 179 "MJ/sq m" 180 5 . 29 3 . 182 181 7 . 56 4 . 354 182 9. 65 7.829 183 13 .98 14 . 36 184 16 .83 19.593 185 19 .6 20. 18 186 22 .09 22.817 6 7 ( 1 - t a x ) * ( L o a n I n t R a t e ) * R C [ 1oanpayment-(RC[- 1 ] / ( 1 - t a x)) (1 -1 (1 -1 (1 -1 (1 -1 t a x ) * ( L o a n I n t R a t e ) * R C [ 1oanpayment-(RC[-1]/(1-ta x)) ta x ) * ( L o a n I n t R a t e ) * R C [ 1oanpayment-(RC[-1]/(1-ta x)) ta x ) * ( L o a n I n t R a t e ) * R C [ 1oanpayment-(RC[- 1 ] / ( 1 - t a x)) t a x ) * ( L o a n I n t R a t e ) * R C [ 1oanpayment-(RC[-1]/(1-ta x)) "Net Present Value:" " R e l a t i v e " "Humidi t y " 0.8 0.77 0.71 0.74 0. 73 0.72 0.73 4 187 21.74 188 18.47 189 13.74 190 9.06 191 6.61 192 193 194 "Low" 195 0.75 196 2 197 0.2 198 0.5 199 200 201 202 " U r o o f v a l " 203 204 1 205 1 206 1 207 1 208 0.98 209 0.68 210 1 211 1 212 0.47 213 1 214 0 215 " 1983" 216 217 "@ 1.45 $/imp.ga1 218 "@ .405 $/therm" 219 "@ .05 $/KWhr" 220 221 222 223 "MKT PRICE/CASE" 224 10 225 11.53 226 227 0. 75 0.79 0.81 0.82 0.84 "Rtrans |" "Airchange" 1 1 1 1 0.95 1 1 1 1 0.5 0.86 0.3 0.95 0.93 0.99 0.98 0.96 0.9 1 1 0 0 1 8 3 4 5 6 7 8 9 10 1 1 12 13 [ g r e e n d a t a UNITS] " G l a z i n g type 14 [ g r e e n d a t a h o u s e l e n g t h ] "Fuel type :" 15 [ g r e e n d a t a h ousewidth] " R o o f G l a z i n g : 16 [ g r e e n d a t a w a l l h e i g h t ] "INSULATION" 17 [ g r e e n d a t a daytemp] "Per i meter 18 [ g r e e n d a t a n i g h t t e m p ] 19 "Age 20 [ g r e e n d a t a l e a k i n e s s ] 21 [ g r e e n d a t a o r i e n t a t i o n ] "Year b u i l t 22 " I n t e r e s t R a t e 23 L 0 0 K U P ( f u e l t y p e , f u e l c o s t t " I n f l a t i o n :" a b l e ) 24 [ g r e e n d a t a c r o p ] " E s c a l a t i o n : 25 [ g r e e n d a t a m a r k e t p r i c e ] "Tax Rate:" 26 [ g r e e n d a t a y i e l d ] "Loan Amount: 10 [ g r e e n d a t a g l a z i n g ] [ g r e e n d a t a f u e l type] [ g r e e n d a t a r o o f g l a z i n g ] [ g r e e n d a t a p e r i m e t e r i n s ul ] ( c u r r e n t y e a r ) - y e a r b u i11 [ g r e e n d a t a y e a r b u i l t ] [ g r e e n d a t a i n t ] [ g r e e n d a t a i n f l a t i o n ] [ g r e e n d a t a e s c ] [ g r e e n d a t a t a x ] [ g r e e n d a t a l o a n ] 8 9 27 [ g r e e n d a t a s t a r t m o n t h ] " L o a n l n t R a t e : " 28 [ g r e e n d a t a endmonth] " r o o t z o n e " 29 " s t a c k h e a t " 30 "IRheat" 31 "computer" 32 " r e d u c e d l e a k " 33 " p o l y o n g l a s s " 34 "Nwal1insul" 35 " o n e m e t e r i n s u l " 36 " t h e r m a l c u r t a i n s " 37 " h e a t s t o r a g e " 38 39 40 4 1 42 43 44 45 46 47 48 49 50 51 52 10 [greendata L o a n l n t R a t e ] [greendata rootzone] [greendata s t a c k h e a t ] [greendata IRheat] [greendata computer] [greendata reducedleak] [greendata p o l y o n g l a s s ] [greendata N w a l l i n s u l ] [greendata onemeterinsu 1 ] [greendata t h e r m a l c u r t a i ns ] [greendata h e a t s t o r a g e ] 10 53 1 1 54 " J u l ' 55 "Aug" "Sep" "Oct" 56 15.5 57 L00KUP(C0LUMN()-1.Hightem P) 58 L00KUP(C0LUMN()-1.Lowtemp ) 59 daytemp-R[-2 ]C 60 nighttemp-R[-2]C 61 vapInNight-(LOOKUP(COLUMN ()- 1 , h u m i d i t y t a b l e ) * ( ( 1 3 2 2/(R[-3]C+273.2))*(10a((R [-3]C*7.5)/(R[-3]C+237.3) ) ) ) ) 62 63 I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * LOOKUP(COLUMN()-1.Monthly R a d i a t i o n ) * L O O K U P ( r o o f g l a z i n g , S h o r t w a v e t r a n s ) * 0 . 7 * 30.4) 64 I F ( R [ - 5 ] C < 0 , 0 , r o o f a r e a * U r o o f * R [ - 5 ] C * ( 2 . 6 2 8 * R [ - 8 ] C / 24)) 65 I F ( R [ - 6 ] C < 0 , 0 , N o r t h w a l l a r ea*Uwal1s*R[-6]C*(2.628*R [-9]C/24)) 66 I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ) ) * U w a l l S * R [ - 7 ] C * ( 2 . 6 2 8 * R [-10JC/24)) 67 I F ( R [ - 8 ] C < 0 , 0 , p e r i m e t e r * I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 .39,2.77)*R[-8]C*2.628*R[ -1 1 JC/24) 68 I F ( R [ - 9 ] C < 0 , 0 , 0 1 n f i I t r a t i on*R[-9]C*(2.628*R[-12]C/ 24)) 69 I F ( R [ - 1 0 ] C < 0 , 0 , f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly R a d i a t i o n ) * L O O K U P ( r o o f g l a z i n g , S h o r t w a v e t r a n s ) * 0 . 5 * 0.7*30.4) 70 71 IF(R[-11]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R [-15]C)/24)) 14 L00KUP(C0LUMN()-1.Hightem P) L00KUP(C0LUMN()- 1,Lowtemp ) daytemp-R[-2]C nighttemp-R[-2]C vapInNight-(LOOKUP(COLUMN ( ) - 1 , h u m i d i t y t a b l e ) * ( ( 1 3 2 2/(R[-3]C+273.2))*(10a((R [-3]C*7.5)/(R[-3]C+237.3) ) ) ) ) I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly R a d i a t ion)*LOOKUP(roofgla z i n g,Shortwavetrans)*0.7* 30.4) IF(R[-5]C<0,0,roofarea*Ur OOf*R[-5]C*(2.628*R[-8]C/ 24)) IF(R[-6]C<0,0,Northwallar ea*Uwal1s*R[-6]C*(2.628*R [-9]C/24)) I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ) )*Uwal1s*R[-7]C*(2.628*R [-10JC/24)) IF(R[-8]C<0,0,perimeter*I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 .39,2.77)*R[-8]C*2.628*R[ -11]C/24) I F ( R [ - 9 ] C < 0 , 0 , Q 1 n f i I t r a t i on*R[-9]C*(2.628*R[-12]C/ 24)) I F ( R [ - 1 0 ] C < 0 , 0 , f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Rad i at i o n ) * LOOKUP(roofg1 a z i n g ,Shortwavetrans)*0.5* 0.7*30.4) 12 L00KUP(C0LUMN()-1.Might emp) L00KUP(C0LUMN()-1,Lowte mp) daytemp-R[-2]C nighttemp-R[-2]C vapInNight-(LOOKUP(COLU MN ( ) - 1 , h u m i d i t y t a b l e ) * ( (1322/(R[-3]C+273.2))*( 10a((R[-3]C*7.5)/(R[-3] C+237.3))))) I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a*L00KUP(C0LUMN()-1,Mon t h l y R a d i a t i o n )*LOOKUP(r o o f g l a z i ng,Shortwavetra ns)*0.7*30.4) IF(R[-5]C<0,0,roofarea* Uroof*R[-5]C*(2.628*R[-8]C/24)) IF(R[-6]C<0,0,Northwal 1 area*Uwal1s*R[-6]C*(2.6 28*R[-9]C/24)) IF(R[-7]C<0,0,(surfacea rea-(Northwa1larea+roof a r e a ) ) * U w a l l s * R [ - 7 ] C * ( 2 .628*R[-10]C/24)) IF(R[-8]C<0,0,perimeter * I F ( L E N ( p e r i m e t e r i n s u l ) =3,1.39,2.77)*R[-8]C*2. 628*R[-11 ]C/24) I F ( R [ - 9 ] C < 0 , 0 , O i n f i 1 t r a t1on*R[-9]C*(2.628*R[-1 2]C/24)) IF(R[-10]C<0,0.floorAre a*L00KUP(C0LUMN()-1,Mon thly R a d i a t i o n ) * L O O K U P ( r o o f g l a z i ng,Shortwavetra ns)*0.5*0.7*30.4) IF(R[-1 1]C<0,0,roofarea*U IF(R[-11]C<0,0,roofarea roof*R[-11]C*(2.628*(24-R *Uroof*R[-11]C*(2.628*( [-15]C)/24)) 24-R[-15]C)/24) ) 10 LOOKUP(C0LUMN()-1.Hightem P) L00KUP(C0LUMN()-1,Lowtemp ) daytemp-R[-2]C nighttemp-R[-2]C vapInNight-(LOOKUP(COLUMN ( ) - 1 . h u m i d i t y t a b l e ) * ( ( 1 3 2 2/(R[-3]C+273.2))*(10a((R [-3]C*7.5)/(R[-3]C+237.3) ) ) ) ) I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Radiat ion)*LOOKUP(roofgla z i ng,Shortwavetrans)*0.7* 30.4) IF(R[-5]C<0,0,roofarea*Ur oof*R[-5]C*(2.628*R[-8]C/ 24)) IF(Rt-6]C<0,0,Northwallar ea*Uwa11s*R[-6]C*(2.628*R t-9]C/24)) I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a-(Northwal1area+roofarea ) )*Uwalls*R[-7]C*(2.628*R [-10]C/24)) IF(R[-8]C<0,0,perimeter*I F ( L E N ( p e r i m e t e r i n s u l ) = 3,1 .39,2.77)*R[-8]C*2.628*R[ -11 ]C/24) I F ( R [ - 9 ] C < 0 , 0 , Q i n f i I t r a t i on*R[-9]C*(2.628*R[-12]C/ 24)) IF(R[-10]C<0,0,floorArea* LOOKUP(COLUMN()-1.Monthly R a d i a t i o n ) * LOOKUP(roofgla z i ng,Shortwavetrans)*0.5* 0.7*30.4) IF ( R [ - 11]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R t-15]C)/24)) 8 72 I F ( R [ - 1 2 ] C < 0 , 0 , N o r t h w a l l a rea*Uwal1s*R[- 12]C*(2.628 *( 2 4 - R [ - 1 6 ] C ) / 2 4 ) ) 73 I F ( R [ - 1 3 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ) ) * U w a l l s * R [ - 1 3 ] C * 2 . 6 2 8 * (24-R[-17]C)/24) 74 I F ( R [ - 1 4 ] C < 0 , 0 , p e r i m e t e r * I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1.39 , 2.77)*R[-14]C * 2.628* (24-R[-18]C)/24) 75 I F ( R [ - 1 5 ] C < 0 , 0 , O i n f i 1 t r a t ion*R[-15]C * 2.628*(24-R[-19]C)/24) 76 IF(R[-15]C< 0 , 0 ,(3600*24*3 0 . 4 167* ( (24-R [ - 2 0 3 O/24 )) * 2 . 4 5 * ( a i rchanges/3600)*v olume*(R[-15]C / 1 0 0 0 ) ) 77 9 IF(R[-12]C<0,0,Northwalla rea*Uwal1s*R[- 12]C*(2.628 *(24-R[-16]C)/24)) I F ( R [ - 1 3 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a))*Uwalls*R[-13]C*2.628* (24-R[-17]C)/24) I F ( R [ - 14]C<0,0,per imeter* I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 .39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) IF(R[-15]C<0,0,Oinf i 1 t r a t ion*R[-15]C*2.628*(24-R[-19]C)/24) IF(R[-15]C<0,0,(3600*24*3 0.4167*((24-R[-20]C)/24)) *2.45*(a 1rchanges/3600)*v olume*(R[-15]C/1000)) 10 IF(R[-12]C<0,0,Northwal l a r e a * U w a l l s * R [ - 1 2 ] C * ( 2 .628*(24-R[-16]C)/24)) IF(R[-13]C<0,0,(surface area-(Northwa1larea+roo f a r e a ) ) * U w a l l s * R [ - 1 3 ] C * 2.628*(24-R[-17]C)/24) IF(R[-14]C<0,0,perimete r * I F ( L E N ( p e r i m e t e r i n s u l )=3,1 .39,2.77)*R[-14]C* 2.628*(24-R[-18]C)/24) I F ( R [ - 1 5 ] C < 0 , 0 , O i n f i I t r ation*R[-15]C*2.628*(24 -R[-19]C)/24) IF(R[-15]C<0,0,(3600*24 *30.4167*((24-R[-20]C)/ 24 ) )*2.45*(ai rchanges/3 600)*volume*(R[-15]C/10 00)) 11 IF(R[-12]C<0,0,Northwalla rea*Uwal1s*R[- 12]C*(2.628 *(24-R[-16]C)/24)) IF(R[-13]C<0,0,(surfacear ea-(Northwal1area+roofare a))*Uwalls*R[-13]C*2.628* (24-R[-17]C)/24) IF(R[-14]C<0,0,perimeter* I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1.39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) I F ( R [ - 15]C<0,0,Oinf i 1 t r a t ion*R[-15]C*2.628*(24-R[-19]C)/24) IF(R[-15]C<0,0,(3600*24*3 0.4167*((24-R[-20]C)/24)) *2.45*(ai rchanges/3600)*v olume*(R[-15]C/1000)) 78 IF(-R[-15]C>SUM(R[-14]C:R I F ( - R [ - 15]C>SUM(R[- 14]C:R [-9]C),SUM(R[-7]C:R[-2]C) [-9]C),SUM(R[-7]C:R[-2]C) ,SUM(R[-15]C:R[-2]C)) ,SUM(R[- 15 ]C:R[-2]C)) 79 80 IF(AND(C0LUMN()>=startmon IF(AND(COLUMN()>=startmon th+1,C0LUMN()<=endmonth+1 th+1,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth+1),R[ ,C0LUMN()<>offmonth+1),R[ -2]C/0.7,0) -23C/0.7.0) 81 R [ - 1 ] C * f u e l c o s t R [ - 1 ] C * f u e l c o s t 82 83 84 85 86 IF(-R[-15]C>SUM(R[-14]C I F ( - R [ - 15 ]C>SUM(R[- 14]C:R :R[-9]C),SUM(R[-7]C:R[- [-9]C) ,SUM(R[-7]C:R[-2]C) 2]C),SUM(R[-15]C:R[-2]C ,SUM(R[- 15]C:R[-2]C)) )) IF(AND(COLUMN()>=startm IF(AND(C0LUMN()>=startmon onth+1,C0LUMN()< = endmon th+1 ,C0LUMN()< = endmonth+1 th+1,C0LUMN()ooffmonth ,C0LUMN()<>offmonth+1),R[ +1),R[-2]C/0.7,0) -2]C/0.7,0) R [ - 1 ] C * f u e l c o s t R [ - 1 ] C * f u e l c o s t 87 88 89 90 91 92 93 10 11 94 95 96 97 98 99 100 101 102 103 104 " J u l 1 105 "Aug" "Sep" "Oct" 106 daytemp-LOOKUP(C0LUMN()-1 ,H i ghtemp) 107 nighttemp-LOOKUP(COLUMN() - 1,Lowtemp) 108 109 I F ( R [ - 3 ] C < 0 , 0 , - f l o o r A r e a * LOOKUP(COLUMN()-1.Monthly Radi a t i on)*LOOKUP(roofg1 a z i n g , S h o r t w a v e t r a n s ) * 0 . 7 * 3 0 . 4 * E S G r a d i a t i o n l o s s ) 110 I F ( R [ - 4 ] C < 0 , 0 , r o o f a r e a * E S GUroof*R[-4]C*(2.628*R[-5 4]C/24)) 111 I F ( R [ - 5 ] C < 0 , 0 , N o r t h w a l l a r ea*ESGUnorthwal1*R[-5]C*( 2.628*R[-55]C/24) ) 112 I F ( R [ - 6 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ))*ESGUwalls*R[-6]C*(2.62 8*R[-56]C/24) ) 113 I F ( R [ - 7 ] C < 0 , 0 , p e r i m e t e r * I F ( L E N ( p e r 1 m e t e r i n s u l ) = 3, 1 .39,2.77)*R[-7]C*(2 .628*R [-57JC/24)) 114 I F ( R [ - 8 ] C < 0 , 0 , E S G Q i n f i I t r a t i o n * R [ - 8 ] C * ( 2 . 6 2 8 * R [ - 5 8 1C/24)) daytemp-LOOKUP(COLUMN( )-1 daytemp-L00KUP(C0LUMN() daytemp-LOOKUP(C0LUMN()- 1 .Hightemp) -I.Hightemp) .Hightemp) nighttemp-LOOKUP(COLUMN( ) nighttemp-LOOKUP(COLUMN nighttemp-LOOKUP(COLUMN() -1,Lowtemp) ()-1,Lowtemp) -1,Lowtemp) I F ( R [ - 3 ] C < 0 , 0 , - f l o o r A r e a * LOOKUP(COLUMN()-1.Monthly Radi a t i on)*L00KUP(roofg1 a z i n g ,Shortwavetrans)*0.7* 3 0 . 4 * E S G r a d i a t i o n l o s s ) IF(R[-4]C<0,0,roofarea*ES GUroof*R[-4]C*(2 ,628*R[-5 4]C/24)) I F ( R [ - 5 ] C < 0 . 0 , N o r t h w a l l a r ea*ESGUnorthwa11*R[-5]C*( 2.628*R[-55]C/24)) I F ( R [ - 6 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ))*ESGUwalls*R[-6]C*(2.62 8*R[-56]C/24) ) IF(R[-7]C<0,0,perimeter*I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 .39,2.77)*R[-7]C*(2.628*R t-57]C/24)) IF(R[-8]C<0,0,ESGOinf11tr ation*R[-8]C*(2.628*R[-58 1C/24)) I F ( R [ - 3 ] C < 0 . 0 , - f l o o r A r e a*L00KUP(C0LUMN()-1,Mon th1yRadiation)*L00KUP(r oof g l az i ng, Shor twa vet r a n s ) * 0 . 7 * 3 0 . 4 * E S G r a d i a t i o n l o s s ) IF(R[-4]C<0,0,roofarea* ESGUroof*R[-4]C*(2.628* R[-54]C/24)) IF(R[-5]C<0.0.Northwal1 area*ESGUnorthwal1*R[-5 ]C*(2.628*R[-55]C/24) ) IF( R [ - 6 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l l a r e a + r o o f area))*ESGUwalls*R[-6]C *(2.628*R[-56]C/24)) IF(R[-7]C<0,0,perimeter * I F ( L E N ( p e r i m e t e r i n s u l ) =3,1.39,2.77)*R[-7]C*(2 .628*R[-57]C/24)) IF(R[-8]C<0,0,ESGOinfi1 t r a t i o n * R [ - 8 ] C * ( 2 . 6 2 8 * R t-58]C/24)) I F ( R [ - 3 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Rad i a t i o n)*L00KUP(roofgla z i ng,Shortwavetrans)*0.7* 3 0 . 4 * E S G r a d i a t i o n l o s s ) IF(R[-4]C<0,0,roofarea*ES GUroof*R[-4]C*(2.628*R[-5 4]C/24)) IF(R[-5]C<0,0,Northwa1 l a r ea*ESGUnorthwal1*R[-5]C*( 2.628*R[-55]C/24)) IF(R[-6]C<0,0,(surfaceare a-(Northwal1area+roofarea ))*ESGUwalls*R[-6]C*(2.62 8*R[-56]C/24)) IF(R[-7]C<0,0,perimeter*I F ( L E N ( p e r i m e t e r i n s u l ) = 3,1 .39,2.77)*R[-7]C*(2.628*R [-57JC/24)) IF(R[-8]C<0,0,ESGOinfi 1 t r ation*R[-8]C*(2.628*R[-58 ]C/24)) 8 115 I F ( R [ - 9 ] C < 0 , 0 , f l o o r A r e a * L 00KUP(C0LUMN()-1.MonthlyR ad1 a t 1 on)*LOOKUP(roofg1az i n g , S h o r t w a v e t r a n s ) * E S G r a d 1 a t i o n l o s s * 0 . 7 * 3 0 . 4 * 0 . 5 ) 1 16 117 I F ( R [ - 1 0 ] C < 0 , 0 , r o o f a r e a * E S G U r o o f * I F ( t h e r m a l c u r t a i n =1,L00KUP(9,ESMUroofval), 1)*R[-10]C*(2.628*(24-R[-6 1 ] C ) / 2 4 ) ) 118 I F ( R [ - 1 1 ] C < 0 , 0 , N o r t h w a l l a r e a * E S G U n o r t h w a l 1 * I F ( t h e r malcurtain=1,LOOKUP(9,ESM U w a l l v a l ) , 1 ) * R [ - 1 1 ] C * ( 2 . 6 28*(24-R[-62]C)/24) ) 119 I F ( R [ - 1 2 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a))*ESGUwal1s*IF(therma1c urtain=1,L00KUP(9,ESMUwal 1 v a l ) , 1 ) * R [ - 1 2 ] C * ( 2 . 6 2 8 * ( 24-R[-63]C)/24) ) 120 I F ( R [ - 1 3 ] C < 0 , 0 , p e r i m e t e r * I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 . 39,2.77)*R[-13]'C*(2.628 * ( 2 4 - R [ - 6 4 ] C ) / 2 4 ) ) 121 I F ( R [ - 1 4 ] C < 0 , 0 , E S G O i n f i I t r a t i o n * R [ - 1 4 ] C * I F ( t h e r m a l curtain=1,LOOKUP(9,ESMai r c h a n g e ) , 1 ) * ( 2 . 6 2 8 * ( 2 4 - R [ -6 5 ] C ) / 2 4 ) ) 122 IF(R[-61]C<0,0,(3600*24*3 0.4167*((24-R[-66]C)/24) ) *2.45*(ESGa i rchanges/3600 ) * I F ( t h e r m a l c u r t a i n = 1 , L O O KUP(9,ESMairchange),1)*vo lume*(R[-61]C/1000)) 123 9 I F ( R [ - 9 ] C < 0 , 0 , f l o o r A r e a * L 00KUP(C0LUMN()-1.MonthlyR a d i a t i o n ) * L O O K U P ( r o o f g l a z i n g,Shortwavetrans)*ESGra d i a t i o n l o s s * 0 . 7 * 3 0 . 4 * 0 . 5 ) IF(R[-10]C<0,0,roofarea*E S G U r o o f * I F ( t h e r m a l c u r t a i n =1,L00KUP(9,ESMUroofval), 1)*R[-10]C*(2.628*(24-R[-6 1 ] C ) / 2 4 ) ) IF(R[-11]C<0,0,Northwalla r e a * E S G U n o r t h w a l 1 * I F ( t h e r malcurtain=1,LOOKUP(9,ESM Uwal1val ) , 1 )*R[-1 1 ]C*(2.6 28*(24-R[-62]C)/24)) I F ( R [ - 1 2 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a))*ESGUwal1s*IF(thermal c urtain=1,L00KUP(9,ESMUwa1 l v a l ) , 1)*R[-12]C*(2.628*( 24-R[-63]C)/24) ) IF(R[-13]C<0,0,perimeter* I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1.39,2.77)*R[-13]C*(2.628 *(24-R[-64]C)/24)) IF(R[-14]C<0,0,ESGQinf i11 r a t 1 o n * R [ - 1 4 ] C * I F ( t h e r m a l curtain=1,LOOKUP(9,ESMair change),1 ) *(2.628*(24-R[-6 5 ] C ) / 2 4 ) ) IF(R[-61]C<0,0,(3600*24*3 0.4167*((24-R[-66]C)/24)) *2.45*(ESGa i rchanges/3600 )*IF(thermalcurtain=1,LOO KUP(9,ESMairchange),1)*vo lume*(R[-61 ]C/1000)) 10 I F ( R [ - 9 ] C < 0 , 0 , f l o o r A r e a *L00KUP(C0LUMN()-1.Mont h i y R a d i a t ion)*LOOKUP( ro o f g l a z i ng,Shortwavetran s ) * E S G r a d i a t i o n l o s s * 0 . 7 *30.4*0.5) IF(R[-10]C<0,0,roofarea *ESGUroof*IF(thermal cur tain=1,LOOKUP(9,ESMUroo f v a l ) , 1 ) * R [ - 1 0 ] C * ( 2 . 6 2 8 *(24-R[-61 ]C)/24)) IF(R[-11]C<0,0,Northwal 1area*ESGUnorthwal1*IF( thermalcurtain=1.LOOKUP (9,ESMUwa11val ), 1 ) *R[- 1 1 ]C*(2.628*(24-R[-62]C) /24)) IF(R[-12]C<0,0,(surface area-(Northwa1larea+roo f a r e a ) ) * E S G U w a l 1 s * I F ( t h ermalcurtain=1,L00KUP(9 ,ESMUwa11val),1)*R[-12] C*(2.628*(24-R[-63]C)/2 4)) IF(R[-13]C<0,0,perimete r * I F ( L E N ( p e r i m e t e r i n s u l )=3,1.39,2.77)*R[-13]C* (2.628*(24-R[-64]C)/24) ) IF(R[-14]C<0,0,ESGOinfi 1 t r a t i o n * R [ - 1 4 ] C * I F ( t h e rmalcurtain=1,L00KUP(9, ESMairchange),1)*(2.628 *(24-R[-65]C)/24)) IF(R[-61]C<0,0,(3600*24 *30.4167*((24-R[-66]C)/ 24))*2.45*(ESGairchange s/3600)*IF(thermal c u r t a in=1,L00KUP(9,ESMaircha nge),1)*volume*(R[-61]C /1000)) 11 IF(R[-9]C<0,0,floorArea*L 00KUP(C0LUMN()-1.MonthlyR a d i a t ion)*LOOKUP(roofglaz i ng,Shortwavetrans)*ESGra d i a t i o n l o s s * 0 . 7 * 3 0 . 4 * 0 . 5 ) IF(R[-10]C<0,0,roofarea*E SGUroof*IF(thermal c u r t a i n =1,L00KUP(9,ESMUroofval), 1)*R[-10]C*(2.628*(24-R[-61]C)/24)) IF(R[-11]C<0,0,Northwa1 l a rea*ESGUnorthwal1*IF(ther malcurtain=1,LOOKUP(9,ESM U w a l l v a l ) , 1 ) * R [ - 1 1 ] C * ( 2 . 6 28*(24-R[-62]C)/24)) IF(R[-12]C<0,0,(surfacear ea-(Northwal1area+roofare a))*ESGUwal1s*IF(therma1c urtain=1,L00KUP(9,ESMUwal l v a l ) , 1 ) * R [ - 1 2 ] C * ( 2 . 6 2 8 * ( 24-R[-63]C)/24)) IF(R[-13]C<0,0,perimeter* I F ( L E N ( p e r i m e t e r i n s u 1 )=3, 1 .39,2.77)*R[-13]C*(2.628 *(24-R[-64]C)/24)) IF(R t-14]C<0,0,ESGOinfi1t r a t i o n * R [ - 1 4 ] C * I F ( t h e r m a l curtain=1,LOOKUP(9,ESMair change),1)*(2.628*(24-R[-65]C)/24)) IF(R[-61]C<0,0,(3600*24*3 0.4167*((24-R[-66]C)/24j) *2.45*(ESGa i rchanges/3600 ) * IF(thermal c u r t a i n=1,LOO KUP(9,ESMairchange),1)*vo lume*(R[-61]C/1000)) 124 IF(-R[-15]C>SUM(R[-14]C:R f-9]C),SUM(R[-7]C:R[-2]C) ,SUM(R[-15]C:R[-2]C)) 125 IF(-R[-15]C>SUM(R[-14]C:R [-9]C),SUM(R[-7]C:R[-2]C) ,SUM(R[-15]C:R[-2]C)) IF(-R[-15]C>SUM(R[-14]C :R[-9]C),SUM(R[-7]C:R[-2]C),SUM(R[-15]C:R[-2]C )) IF(-R[-15]C>SUM(R[-14]C:R t-9]C),SUM(R[-7]C:R[-2]C) ,SUM(R[-15]C:R[-2]C)) 8 9 10 11 126 IF ( AND(C0LUMN()> = s t a r t m o n IF(AND(COLUMN*)>=startmon I F(AND(C0LUMN()> = startm IF(AND(C0LUMN()>=startmon th+1,C0LUMN()< = endmonth+1 th+1,C0LUMN( )<=endmonth+1 onth+1,C0LUMN()< = endmon th+1 ,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth),R[-2 ,C0LUMN()<>offmonth),R[-2 th+1,C0LUMN()ooffmonth ]C/heat i n g e f f i c i e n c y , 0 ) ]C/heat i n g e f f i c i e n c y , 0 ) 127 R [ - 1 ] C * f u e l c o s t 128 129 130 131 132 R[-1 ] C * f u e l c o s t ) , R [ - 2 ] C / h e a t i n g e f f i c i e ncy,0) R[-1 ] C * f u e l c o s t ,C0LUMN()<>offmonth),R[-]C/heat i n g e f f i c i ency,0) R[-1 ] C * f u e l c o s t 133 134 135 136 137 138 139 140 141 "CCA" 142 143 0.1 * i n s t a l c o s t * t a x 144 R[-1]C*(1-0.1 145 R[-1]C*(1-0.1 146 R[-1]C*(1-0.1 147 R[-1]C*(1-0.1 148 R[- 1]C*(1-0.1 149 R[-1]C*(1-0.1 150 R[-1]C*(1-0.1 151 R[- 1]C*(1-0.1 152 R[-1]C*(1-0.1 "CASHFLOW" 1oan-i n s t a l c o s t RC[-7]+RC[-6 3]-RC[-2]+RC RC[-7]+RC[-6 3]-RC[-2]+RC RC[-7]+RC[-6 3]-RC[-2]+RC RC[-7]+RC[-6 3]-RC[-2]+RC RC[-7]+RC[-6 3]-RC[-2]+RC RC[-7]+RC[-6 3]-RC[-2]+RC RC[-7]+RC[-6 3]-RC[-2]+RC RC[-7]+RC[-6 3]-RC[-2]+RC RC[-7]+RC[-6 3]-RC[-2]+RC RC[-7]+RC[-6 3]-RC[-2]+RC -RC[ -1] -RC[ -1] -RC[ -1] -RC[ -1] -RC[ -1] -RC[ -1] -RC[ -1] -RC[ -1] -RC[ -1] -RC[ -1] "DISCOUNTED" 1oan-i n s t a l c o s t -5]-RC[- RCt-1] 5]-RC[- R C t - 1 ] * ( 1 / ( 1 + i n t 9 ] - ( c u r r e n t y e a r ) 5]-RC[- RC[-1]*(1/(1+int 9 ] - ( c u r r e n t y e a r ) 5]-RC[- RC[-1]*(1/(1+int 9 ] - ( c u r r e n t y e a r ) 5]-RC[- RCt-1 ]*•( 1/( 1 + i n t 9 ] - ( c u r r e n t y e a r ) 5]-RC[- RC[-1]*(1/(1+int 9 ] - ( c u r r e n t y e a r ) 5]-RC[- RC[-1]*(1/(1+int 9 ] - ( c u r r e n t y e a r ) 5]-RC[- RC[-1]*(1/(1+1nt 9 ] - ( c u r r e n t y e a r ) 5]-RC[- RC[-1]*(1/(1+int 9 ] - ( c u r r e n t y e a r ) 5]-RC[- RC[-1]*(1/(1+int 9 ] - ( c u r r e n t y e a r ) "YEAR NUMBER" IF(AND(SUM(R142C10:RC[-)>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",1 ) a(RC[- IF(AND(SUM(R142C10:RC[-) )>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",2) a(RC[- IF(AND(SUM(R142C10:RC[-) )>0,SUM(R142C10:R[-1JC[ ])<0),"Break-Even",3) a(RC[- IF(AND(SUM(R142C10:RC[-) )>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",4) a(RC[- IF(AND(SUM(R142C10:RC[-) )>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",5) <z(RC[- IF(AND(SUM(R142C10:RC[-) )>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",6) a(RC[- IF(AND(SUM(R142C10:RC[-) )>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",7) a(RC[- IF(AND(SUM(R142C10:RC[-) )>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",8) a(RC[- IF(AND(SUM(R142C10:RC[-) )>0.SUM(R142C10:R[-1]C[ ])<0),"Break-Even",9) a(RC[- IF(AND(SUM(R142C10:RC[-) )>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",10) 8 153 R[-1]C*(1-0.1) 154 R[-1]C*(1-0.1) 155 R[-1]C*(1-0.1) 156 R[-1]C*(1-0.1) 157 R[-1]C*(1-0.1) 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 9 RC[-7]+RC[-6]-RC[-5]-RC[-3]-RC[-2]+RC[-1] RC[-7]+RC[-6]-RC[-5]-RC[-3]-RC[-2]+RC[-1] RC[-7]+RC[-6]-RC[-5]-RC[-3]-RC[-2]+RC[-1] RC[-7]+RC[-6]-RC[-5]-RC[-3]-RC[-2]+RC[-1] RC[-7]+RC[-6]-RC[-5]-RC[-3]-RC[-2]+RC[-1] NPV(i n t , f 1 o w ) - ( i n s t a l c o s t -loan)+R[-16]C 10 R C [ - 1 ] * ( 1 / ( 1 + i n t ) a ( R C [ -9 ] - ( c u r r e n t y e a r ) ) ) RC[-1]*( 1/( 1 + i n t ) c t ( R C [ -9 ] - ( c u r r e n t y e a r ) ) ) RC[-1 ]*( 1/( 1 + i n t ) a ( R C [ -9 ] - ( c u r r e n t y e a r ) ) ) R C [ - 1 ] * ( 1 / ( 1 + i n t ) a ( R C [ -9 ] - ( c u r r e n t y e a r ) ) ) R C [ - 1 3 * ( 1 / ( 1 + i n t ) a ( R C [ -9 ] - ( c u r r e n t y e a r ) ) ) "brk even y r : " 1 1 IF(AND(SUM(R142C10:RC[-1] )>0,SUM(R142C10:R[-1]C[-1 ])<0),"Break-Even",11) IF(AND(SUM(R142C10:RC[-1 ] )>0,SUM(R142C10:R[- 1 ] C [ - 1 ])<0),"Break-Even",12) IF(AND(SUM(R142C10:RC[-13 )>0,SUM(R142C10:R[-1]C[-1 ])<0),"Break-Even",13) IF(AND(SUM(R142C10:RC[-1] )>0,SUM(R142C10:R[-1]C[-1 ])<0),"Break-Even",14) IF(AND(SL)M(R142C10:RC[-1 ] )>0,SUM(R142C10:R[-1]C[-1 3)<0),"Break-Even",15) IF(SUM(R[-15]C:R[-13C)=12 0, " never" ,120-SUM(R[- 15] C:R[-1 ]C)) t—1 4^  187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 "Per square meter" 202 " I n s t C o s t " "Maintenance 203 " " " 204 19 0.2 205 12000 100 206 21 0. 1 3 207 20000 200 208 3 0.04 209 10 2 210 3 0.15 2 1 1 3 0.15 212 12 0.12 213 17 50 214 0 0 215 216 217 218 219 220 221 222 223 224 225 226 227 10 "Change i n " "Heating" " Y i e l d (%)" " E f f i c i e n c y " 0.02 1.15 0 1.12 -0.03 1.25 0.05 1.12 0 -0. 1 -0.005 0 -0.01 0 1 .25 0 13 14 15 " S C R A T C H 1 I n s t a l c o s t " P A D " "Ma i ntenance" "Yld f a c t o r " IF(RC[-11] = 1 ,LOOKUP(RC[-2 ],ESMi n s t a l c o s t ) * f 1 o o r A r e a,0) IF(RC[-1 1 ] = 1,LOOKUP(RC[-2 ] , E S M i n s t a l c o s t ) , 0 ) IF(RC[-1 1 ] = 1 ,LOOKUP(RC[-2 ] , E S M i n s t a l c o s t ) * f 1 o o r A r e a,0) IF(RC[-1 1 ] = 1 , LOOKUP(RC[-2 ] , E S M i n s t a l c o s t ) , 0 ) IF(RC[ ],ESMi a,0) IF(RC[ ],ESMi rea,0) IF(RC[ ],ESMi 1 a r e a , IF(RC[ ],ESMi r ,0) IF(RC[ ],ESMi a,0) IF(RC[ ].ESMi a,0) -11]=1,L00KUP(RC[-2 n s t a l c o s t ) * f l o o r A r e -11]=1,L00KUP(RC[-2 n s t a l c o s t ) * s u r f a c e a -11]=1,L00KUP(RC[-2 n s t a l c o s t ) * N o r t h w a l 0) - 11] = 1,L00KUP(RC[-2 n s t a l c o s t ) * p e r i m e t e -1 1 ] = 1 ,L00KUP(RC[-2 n s t a l c o s t ) * f 1 o o r A r e - 11] = 1,L00KUP(RC[-2 n s t a l c o s t ) * f l o o r A r e IF(RC[-12]=1 ESMma i n t c o s t IF(RC[-12]=1 ESMma i n t c o s t IF(RC[-12]=1 ESMma i n t c o s t IF(RC[-12]=1 ESMma i n t c o s t IF(RC[-12]=1 ESMma i n t c o s t IF(RC[-12]=1 ESMma i n t c o s t IF(RC[-12]=1 ESMma i n t c o s t .0) IF(RC[-12]=1 ESMma i n t c o s t IF(RC[-12]=1 ESMma i n t c o s t IF(RC[-12]=1 ESMma i n t c o s t LOOKUP(RC[-3], * f l o o r A r e a , 0 ) L00KUP(RC[-3], ,0) LOOKUP(RC[-3], * f l o o r A r e a , 0 ) LOOKUP(RC[-3] , ,0) L00KUP(RC[-3] , *f1oorArea,0) L00KUP(RC[-3], *f1oorArea,0) L00KUP(RC[-3] , *Northwal1 area L00KUP(RC[-3] , *per i meter,0) L00KUP(RC[-3], * f l o o r A r e a , 0 ) L00KUP(RC[-3], ,0) IF(RC[-(RC[-4] ) IF(RC[-(RC[-4] ) IF(RC[-(RC[-4] ) IF(RC[-(RC[-4] ) IF(RC[-(RC[-4] ) IF(RC[-(RC[-4] ) IF(RC[-(RC[-4] ) IF(RC[-(RC[-4] ) IF(RC[-(RC[-4] ) IF(RC[-(RC[-4] ) 13]=1,LOOKUP ,ESMyield),0 13]=1.LOOKUP ,ESMyield),0 13]=1,LOOKUP ,ESMyield),0 13]=1.LOOKUP ,ESMyield),0 13]=1,LOOKUP ,ESMyield),0 13]=1.LOOKUP ,ESMyield),0 13]=1,LOOKUP ,ESMyield),0 13]=1.LOOKUP ,ESMy i e l d ) , 0 13]=1.LOOKUP ,ESMyield),0 13]=1.LOOKUP ,ESMyield),0 SUM(R[-11]C:R[-1 ]C) SUM(R[-11]C:R[-1]C) SUM(R[-11]C:R[-1]C) UI OI OI A 4^J^ ^ ^ ^ ^ ^ ^ C O C O C O CO CO CO CO UCJ'CO MfOfO ro O c o o D ^ c n 01 u ^ o IDOD^J cn cn A CO ro O CO CO -J ro co Z.TT 12 53 "-13 14 54 "Nov" 55 " "Dec" 'Year t o t a l ' 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 L00KUP(C0LUMN()-1.Hightem P) L00KUP(C0LUMN()-1.Lowtemp ) daytemp-R[-2 ]C nighttemp-R[-2]C vapInNi ght-(LOOKUP(COLUMN ( ) - 1 , h u m i d i t y t a b l e ) * ( ( 1 3 2 2/(R[-3]C+273.2))*(10a((R [-3]C*7.5)/(R[-3]C+237.3) )))) I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * LOOKUP(COLUMN()-1.Monthly R a d i a t i o n ) * L O O K U P ( r o o f g l a z i n g , S h o r t w a v e t r a n s ) * 0 . 7 * 30.4) I F ( R [ - 5 ] C < 0 , 0 , r o o f a r e a * U r o o f * R [ - 5 ] C * ( 2 . 6 2 8 * R [ - 8 ] C / 24)) IF(R[-6]C<0,0,Northwal l a r ea*Uwal1s*R[-6]C*(2.628*R [ - 9 3 C / 2 4 ) ) I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ) )*Uwal1s*R[-7]C*(2.628*R [-10]C/24)) I F ( R [ - 8 ] C < 0 , 0 , p e r i m e t e r * I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 .39,2.77)*R[-8]C*2.628*R[ -11]C/24) I F ( R [ - 9 ] C < O , O , 0 i n f i 1 t r a t i on*R[-9]C*(2.628*R[-12]C/ 24)) I F ( R [ - 1 0 ] C < 0 , 0 , f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Rad i a t i on)*L00KUP(roofg1 a z i n g , S h o r t w a v e t r a n s ) * 0 . 5 * 0.7*30.4) IF(R[-11]C<0,0.roofarea*U roof*R[-11]C*(2.628*(24-R [ - 1 5 J O / 2 4 ) ) 8 L00KUP(C0LUMN()-1.Hightem P) L00KUP(C0LUMN()-1.Lowtemp ) daytemp-R[-2]C nighttemp-R[-2]C vaplnN i ght-(LOOKUP(COLUMN ( ) - 1 . h u m i d i t y t a b 1 e ) * ( ( 1 3 2 2/(R[-3]C+273.2))*(10a((R [-3]C*7.5)/(R[-3]C+237.3) )))) I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * L00KUP(C0LUMN()-1.Monthly Rad i a t i o n ) * LOOKUP(roofg1 a z i n g ,Shortwavetrans)*0.7* 30.4) I F ( R[-5]C<0,0,roofarea*Ur oof*R[-5]C*(2.628*R[-8]C/ 24)) I F ( R [ - 6 ] C < 0 , 0 , N o r t h w a l l a r ea*Uwal1s*R[-6]C*(2.628*R [-9]C/24)) I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ))*Uwalls*R[-7]C*(2.628*R [-10JC/24)) IF(R[-8]C<0,0,perimeter*I F ( L E N ( p e r i m e t e r I n s u l ) = 3 , 1 :39,2.77)*R[-8]C*2.628*R[ -11]C/24) I F ( R [ - 9 ] C < 0 , 0 , 0 i n f i 1 t r a t i on*R[-9]C*(2.628*R[-12]C/ 24)) I F ( R [ - 1 0 ] C < 0 , 0 , f l o o r A r e a * L00KUP(C0LUMN()- 1.Monthly R a d i a t i o n ) * L O O K U P ( r o o f g l a z i n g ,Shortwavetrans)*0.5* 0.7*30.4) SUM(RC[-12]:RC[-1]) SUM(RC[-SUM(RC[-SUM(RC[-12]:RC[-1]) 12]:RC[-1]) 12] :RC[- 1 ] ) SUM(RC[-12]:RC[-1]) SUM(RC[ SUM(RC[ IF(R[-11]C<0,0,roofarea*U SUM(RC[-roof*R[-11]C*(2.628*(24-R [-15JO/24)) •12] :RC[-1]) •12] :RC[-1]) -12]:RC[-1]) 12 72 IF(R[-12]C<0,0,Northwal l a rea*Uwal1s*R[- 12]C*(2.628 * ( 2 4 - R [ - 1 6 ] C ) / 2 4 ) ) 73 I F ( R [ - 1 3 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ) ) * U w a l l s * R [ - 1 3 ] C * 2 . 6 2 8 * (24-R[-17]C)/24) 74 I F ( R [ - 1 4 ] C < 0 , 0 , p e r i m e t e r * I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1.39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) 75 I F ( R [ - 1 5 ] C < 0 , 0 , Q i n f i l t r a t ion*R[-15]C*2.628*(24-R[-19]C)/24) 76 IF(R[-15]C<0,0,(3600*24*3 0.4167*((24-R[-20]C)/24)) *2.45*(a i r changes/3600)*v olume*(R[-15]C/1000)) 13 14 :RC[-1]) IF(R[-12]C<0,0,Northwalla SUM(RC[-12]: rea*Uwal1s*R[- 12]C*(2.628 *(24-R[-16]C)/24)) I F ( R [ - 1 3 ] C < 0 , 0 , ( s u r f a c e a r SUM(RC[-12]:RC[-1]) e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ) ) * U w a l l s * R [ - 1 3 ] C * 2 . 6 2 8 * (24-R[-17]C)/24) IF(R[-14]C<0,0,perimeter* SUM(RC[-12]:RC[- 1]) I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1.39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) IF(R[-15]C<O,O,0inf i 1 t r a t SUM(RC[-12]:RC[- 1]) ion*R[-15]C*2.628*(24-R[-19]C)/24) IF(R[-15]C<0,0,(3600*24*3 SUM(RC[-12]:RC[- 1]) 0.4167*((24-R[-20]C)/24)) * 2 . 4 5 * ( a i rchanges/3600)*v olume*(R[-15]C/1000)) 77 78 IF(-R[-15]C>SUM(R[-14]C:R I F ( - R [ - 15]C>SUM(R[- 14]C:R SUM(RC[- 12]:RC[- 1 ]) C-9]C),SUM(R[-7]C:R[-2]C) [-9]C),SUM(R[-7]C:R[-2]C) ,SUM(R[-15]C:R[-2]C)) ,SUM(R[- 15]C:R[-2]C)) 79 80 81 82 83 84 85 86 IF(AND(C0LUMN()> = startmon IF(AND(C0LUMN()> = startmon SUM(RC[-12]:RC[-1 ]) th+1,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth+1),R[ -2]C/0.7,0) R[-1 ] C * f u e l c o s t th+1,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth+1),R[ -2]C/0.7,0) R [ - 1 ] C * f u e l c o s t SUM(RC[-12]:RC[- 1]) 87 88 89 90 91 92 93 15 (R[-7]C[-1]+RC[-1]) /SUM(R[-12]C[-1] :RC [-1]) 94 12 13 14 95 96 97 98 99 100 101 102 103 104 "Nov" "Dec" "Year t o t a l " 105 " " " " " 1 106 daytemp-L00KUP(C0LUMN()-1 daytemp-L00KUP(C0LUMN()- 1 .Hightemp) .Hightemp) 107 nighttemp-LOOKUP(COLUMN() nighttemp-L00KUP(C0LUMN( ) -1, Lowtemp) -1,Lowtemp) 108 109 I F ( R [ - 3 ] C < 0 , 0 . - f l o o r A r e a * IF(R[-3]C<0,0,-f1oorArea* SUM(RC[- 12]:RC[- 1]) L00KUP(C0LUMN()-1.Monthly LOOKUP(C0LUMN()-1.Monthly R a d i a t i o n ) * L 0 0 K U P ( r o o f g l a R a d i a t ion)*LOOKUP(roofgla z i n g , S h o r t w a v e t r a n s ) * 0 . 7 * z i n g , S h o r t w a v e t r a n s ) * 0 . 7 * 3 0 . 4 * E S G r a d i a t i o n l o s s ) 30.4*ESGradiat i o n l o s s ) 110 I F ( R [ - 4 ] C < 0 , 0 , r o o f a r e a * E S IF(R[-4]C<0,0,roofarea*ES SUM(RC[- 12]:RC[-1 ]) GUroof*R[-4]C*(2.628*R[-5 GUroof*R[-4]C*(2.628*R[-5 4]C/24)) 4]C/24)) 111 IF(R[-5]C<0,0,Northwa11ar IF(R[-5]C<0,0,Northwa11ar SUM(RC[- 12]:RC[- 1 ]) ea*ESGUnorthwal1*R[-5]C*( ea*ESGUnorthwal1*R[-5]C*( 2.628*R[-55]C/24)) 2.628*R[-55]C/24)) 112 I F ( R [ - 6 ] C < 0 , 0 , ( s u r f a c e a r e I F ( R [ - 6 ] C < 0 , 0 , ( s u r f a c e a r e SUM(RC[-12]:RC[- 1 ] ) a - ( N o r t h w a l 1 a r e a + r o o f a r e a a - ( N o r t h w a l 1 a r e a + r o o f a r e a ))*ESGUwal1s*R[-6]C*(2.62 ) )*ESGUwalls*R[-6]C*(2.62 8*R[-56]C/24)) 8*R[-56]C/24)) 113 I F ( R [ - 7 ] C < 0 , 0 , p e r i m e t e r * I IF(R[-7]C<0,0,perimeter* I SUM(RC[- 12]:RC[- 1 ]) F ( L E N ( p e r i m e t e r i n s u l ) = 3 , 1 F ( L E N ( p e r i m e t e r i n s u 1 ) = 3 , 1 .39,2.77)*R[-7]C*(2.628*R .39,2.77)*R[-7]C*(2.628*R [-57]C/24)) [-57]C/24)) 114 I F ( R [ - 8 ] C < 0 , 0 , E S G O i n f i 1 t r IF(R[-8]C<0,0,ESGQinf1 1 t r SUM(RC[- 12]:RC[- 1]) a t i o n * R [ - 8 ] C * ( 2 . 6 2 8 * R [ - 5 8 ation*R[-8]C*(2.628*R[-58 ]C/24)) ]C/24)) 12 13 14 115 I F ( R [ - 9 ] C < 0 , 0 , f 1 o o r A r e a * L IF(R[-9]C<0,O,f1oorArea*L SUM(RC[- 12]:RC[- 1]) 0OKUP(COLUMN()-1.MonthlyR OOKUP(COLUMN()-1.MonthlyR a d i a t i o n ) * L O O K U P ( r o o f g l a z a d i a t i o n ) * L O O K U P ( r o o f g l a z 1ng,Shortwavetrans)*ESGra i n g , S h o r t w a v e t r a n s ) * E S G r a d i a t i o n l o s s * 0 . 7 * 3 0 . 4 * 0 . 5 ) d i a t i o n l o s s * 0 . 7 * 3 0 . 4 * 0 . 5 ) 116 1 17 1 18 I F ( R [ - 1 0 ] C < 0 , 0 , r o o f a r e a * E S G U r o o f * I F ( t h e r m a l c u r t a i n =1,L00KUP(9,ESMUroofval), 1)*R[-10]C*(2.628*(24-R[-6 1 ] C ) / 2 4 ) ) IF(R[-11]C<0,0,Northwalla r e a * E S G U n o r t h w a l l * I F ( t h e r malcurtain=1,LOOKUP(9,ESM Uwal1 v a l ) , 1 ) * R [ - 11 ]C*(2 .6 28*(24-R[-62]C)/24) ) IF(R[-10]C<0,0,roofarea*E S G U r o o f * I F ( t h e r m a l c u r t a i n = 1,L00KUP(9,ESMUroofval ), 1)*R[-10]C*(2.628*(24-R[-6 1 ] C ) / 2 4 ) ) IF(R[-11]C<0,0,Northwalla r e a * E S G U n o r t h w a l 1 * I F ( t h e r malcurta1n=1,LOOKUP(9,ESM Uwal1val ) , 1 )*R[-1 1 ]C*(2.6 28*(24-R[-62]C)/24)) SUM(RC[-12]:RC[-1]) SUM(RC[-12]:RC[-1]) 119 I F ( R [ - 1 2 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a ) ) * E S G U w a l 1 s * I F ( t h e r m a l c urtain=1,L00KUP(9,ESMUwal I v a l ) , 1 ) * R [ - 1 2 ] C * ( 2 . 6 2 8 * ( 24-R[-63]C)/24) ) I F ( R [ - 1 2 ] C < 0 , 0 , ( s u r f a c e a r e a - ( N o r t h w a l 1 a r e a + r o o f a r e a))*ESGUwal1s*IF(therma1c urtain=1,LOOKUP(9,ESMUwal 1 v a l ) , 1 ) * R [ - 1 2 ] C * ( 2 . 6 2 8 * ( 24-R[-63]C)/24) ) SUM(RC[-12]:RC[-1]) 120 I F ( R [ - 1 3 ] C < 0 , 0 , p e r i m e t e r * I F ( R [ - 13]C<0,0,perimeter* SUM(RC[-12]:RC[-1]•) I F ( L E N ( p e r i m e t e r i n s u l ) = 3 , I F ( L E N ( p e r i m e t e r i n s u 1 ) = 3 , 1.39,2.77)*R[-13]C*(2.628 1.39,2.77 ) *R[-13]C*(2.628 *(24-R[-64]C)/24) ) *(24-R[-64 ] C )/24)) 121 I F ( R [ - 1 4 ] C < 0 , 0 , E S G Q i n f i l t r a t i o n * R [ - 1 4 ] C * I F ( t h e r m a l curtain=1,LOOKUP(9,ESMair ch a n g e ) , 1 ) * ( 2 . 6 2 8 * ( 2 4 - R [ -6 5 ] C ) / 2 4 ) ) 122 IF(R[-61]C<0,0,(3600*24*3 0.4167*((24-R[-66]C)/24)) *2.45*(ESGa i rchanges/3600 ) * I F ( t h e r m a l c u r t a i n=1,LOO KUP(9,ESMairchange),1)*vo lume*(R[-61]C/1000)) I F ( R [ - 1 4 ] C < 0 , 0 , E S G Q i n f i l t r a t i o n * R [ - 1 4 ] C * I F ( t h e r m a l curtain=1,LOOKUP(9,ESMa1r change),1 ) *(2 . 628*(24-R[-6 5 ] C ) / 2 4 ) ) IF(R[-61]C<0,0,(3600*24*3 0.4167*((24-R[-66]C)/24)) *2.45*(ESGa i rchanges/3600 )*IF ( t h e r m a l c u r t a i n = 1 , L O O KUP(9,ESMai rc h a n g e ) , 1 ) * v o lume*(R[-61]C/1000)) SUM(RC[-12]:RC[-1]) SUM(RC[-12]:RC[-1]) 123 124 IF(-R[-15]C>SUM(R[-14]C:R I F ( - R [ - 15]C>SUM(R[- 14]C:R SUM(RC[- 12]:RC[ [-9]C),SUM(R[-7]C:R[-2]C) [-9]C),SUM(R[-7]C:R[-2]C) ,SUM(R[-15]C:R[-2]C) ) ,SUM(R[- 15 ]C : R[-2]C)) •1]) 125 15 (R[-7]C[-1]+RC[-1]) /SUM(R[-12]C[-1]:RC [-1]) 126 127 128 129 130 131 132 12 IF(AND(COLUMN()>=startmon th+1,C0LUMN()<=endmonth+1 ,C0LUMN( ) o o f f month) , R[-2 ]C/heat i n g e f f i c i e n c y , 0 ) R [ - 1 ] C * f u e l c o s t 13 IF(AND(COLUMN()>=startmon SUM(RC[-12] th+1,C0LUMN()<=endmonth+1 ,C0LUMN( )<>offmonth),R[-2 ] C / h e a t i n g e f f i c i e n c y , 0 ) 14 :RC[-15 1]) R[-1 ] C * f u e l c o s t SUM(RO[-12]:RC[-1]) 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 t o APPENDIX E: USER'S MANUAL 123 1 24 GREENSIM v e r s i o n 1.0 DRAFT COPY Copywrited by Barry S h e l l , September 1984 USER'S MANUAL I n t r o d u c t i o n Most growers re c o g n i z e the i n c r e a s i n g need f o r energy c o n s e r v a t i o n in greenhouses. However, when i t comes to spending money on energy c o n s e r v a t i o n equipment, the overwhelming number of a l t e r n a t i v e s can make the c o r r e c t c h o ice d i f f i c u l t . The GREENSIM c a p i t a l budgeting model can be used by a g r i c u l t u r a l a d v i s o r s i n governments, marketing co-ops and h o r t i c u l t u r a l a s s o c i a t i o n s to analyse the p o t e n t i a l of energy c o n s e r v a t i o n to improve the p r o f i t a b i l i t y of greenhouse o p e r a t i o n s . New equipment budgets l i k e GREENSIM are a l s o used by l e n d i n g i n s t i t u t i o n s to determine investment p o t e n t i a l . GREENSIM makes p r e d i c t i o n s based on a choice from among 10 popular greenhouse energy c o n s e r v a t i o n measures (see t a b l e 1 f o r a l i s t and page 5 f o r d e s c r i p t i o n s ) . A l l that i s r e q u i r e d to use GREENSIM i s a microcomputer equiped with M u l t i p l a n (copywrited by M i c r o s o f t , B e l l e v u e , Washington). M u l t i p l a n i s one of the most popular spreadsheet programs a v a i l a b l e and GREENSIM i s a spreadsheet template t h a t can be loaded by M u l t i p l a n . Development of a new equipment budget can be a complex pro c e s s . The model must i n c l u d e a l l revenues and expenses a s s o c i a t e d with each energy saving a l t e r n a t i v e . I t must c a l c u l a t e the expected changes i n energy consumption as w e l l as 1 2 5 the e f f e c t s on c r o p y i e l d over a c e r t a i n p e r i o d of t i m e . Because e x p e c t a t i o n s about these f a c t o r s can v a r y w i d e l y and change r a p i d l y , the i n i t i a l budget may change many tim e s b e f o r e i t i s f i n a l i z e d . W i t h M u l t i p l a n and the GREENSIM energy p l a n n i n g model, however, you can b u i l d and modify budget p r o j e c t i o n s q u i c k l y and e a s i l y . In the p r o c e s s you w i l l l e a r n which energy c o n s e r v a t i o n t e c h n i q u e i s be s t f o r your i n d i v i d u a l greenhouse. P r i n c i p l e s The p r o f i t p o t e n t i a l of any new c a p i t a l e x p e n d i t u r e depends on two f a c t o r s : how much money i s e x p e c t e d t o f l o w i n and how much i s e x p e c t e d t o f l o w o u t . The l e v e l of i n f l o w s and o u t f l o w s depends on the number of u n i t s s o l d , u n i t p r i c e , u n i t c o s t , s e l l i n g p r i c e and so on. L e t ' s t a k e a l o o k a t some i m p o r t a n t r e l a t i o n s h i p s t h a t a f f e c t the p r o f i t a b i l i t y of energy c o n s e r v a t i o n i n greenhouses. By i mplementing energy c o n s e r v a t i o n measures, a grower e x p e c t s t o lower the u n i t c o s t of p r o d u c t i o n . GREENSIM u t i l i z e s a d e s c r i p t i o n of the grower's greenhouse t o model the d e c r e a s e i n energy use t h a t would be e x p e c t e d . The model i s based on the grower's p l a n n e d c h o i c e of energy c o n s e r v a t i o n equipment and the p h y s i c a l p r o p e r t i e s of h i s greenhouse. The energy s a v i n g s a r e then t r a n s l a t e d i n t o d o l l a r amounts and i n c l u d e d i n a ca s h f l o w budget a n a l y s i s . The c a s h f l o w a n a l y s i s i n c l u d e s e x t r a a n n u a l maintenance c o s t s brought about by the new energy c o n s e r v a t i o n equipment. In a d d i t i o n the p o s i t i v e or n e g a t i v e e f f e c t on the o v e r a l l a n n u a l c r o p y i e l d i s i n c l u d e d . Energy c o n s e r v a t i o n can a f f e c t y i e l d s i n a number of ways, which may i n c l u d e reduced l i g h t l e v e l s or 1 26 in c r e a s e d humidity. In gen e r a l the e f f e c t of energy c o n s e r v a t i o n on crop y i e l d i s estimated as a percentage of expected annual y i e l d . The cash flow a n a l y s i s a l s o a l l o w s f o r the c o s t s of borrowing money to i n s t a l l the energy c o n s e r v a t i o n equipment. A l l cash flows are shown i n d i s c o u n t e d a f t e r tax d o l l a r s . I n f l a t i o n and d e p r e c i a t i o n are a l s o i n c l u d e d . The goal of the model i s to determine the value of f u t u r e cash flow due to the farmer's investment i n energy c o n s e r v a t i o n equipment, expressed i n today's d o l l a r s . T h i s i s known as the net present value or NPV. The<= break-even year of the investment i s a l s o c a l c u l a t e d . When choosing among energy c o n s e r v a t i o n a l t e r n a t i v e s , the one with the hi g h e s t net present value and the lowest break-even year w i l l be the most p r o f i t a b l e o p t i o n . The business of greenhouse vegetable growing i s very complicated. In an i d e a l world a l l i n t e r - r e l a t i o n s h i p s would be i n c o r p o r a t e d i n t o the model. U n f o r t u n a t e l y not even M u l t i p l a n can i n c l u d e every r e l a t i o n s h i p , although the GREENSIM model does i n c l u d e a l l those d i s c u s s e d above. Keep i n mind that the model i s meant to be an i n t e r a c t i v e a i d to d e c i s i o n making. I t i s not designed to p r e d i c t the exact cash flows, though i n p r e l i m i n a r y t e s t s i t came w i t h i n 20% of growers' a c t u a l v a l u e s . Using The Model The model e x i s t s as two seperate spreadsheets: GREENSIM and GREENDAT. GREENSIM i s a dependant spreadsheet l i n k e d to GREENDAT that does a l l the c a l c u l a t i o n s and d i s p l a y s the r e s u l t s of the s i m u l a t i o n . GREENDAT i s a spreadsheet i n the form of a 1 27 q u e s t i o n n a i r e t h a t makes d a t a e n t r y e a s i e r f o r the u s e r . GREENDAT GREENDAT i s d e s i g n e d f o r da t a e n t r y and s h o u l d be l o a d e d f i r s t . The user f i l l s i n the answers t o q u e s t i o n s , a l l of which are marked by a dashed l i n e . A l l o t h e r p a r t s of the spre a d s h e e t are l o c k e d , t h e r e f o r e you may use the " n e x t - u n l o c k e d - c e l l " key to move through the q u e s t i o n a i r e q u i c k l y . A l t e r n a t i v e l y t he " a r r o w - d i r e c t i o n " keys may be used t o move from one answer t o the n e x t . The q u e s t i o n s a l l have d e f a u l t answers and may be l e f t unchanged i f d e s i r e d . At any time you may go back and change an e n t r y s i m p l y by u s i n g the d i r e c t i o n keys t o move t o the d e s i r e d l o c a t i o n and r e t y p i n g i t . Most of the q u e s t i o n s a r e s e l f e x p l a n a t o r y but some e l a b o r a t i o n i s g i v e n h e r e : Your Name F i f t e e n c h a r a c t e r s a re a l l o w e d f o r the grower's name. Dimensions You may type i n 'meters' or ' f e e t ' i n the f i r s t b l a n k . The next t h r e e measurements must a l l be i n the u n i t s you have chosen. The w a l l h e i g h t i s measured from the ground t o the g a b l e s . I f you have a m u l t i u n i t g u t t e r c o n n e c t e d greenhouse complex, the w i d t h and l e n g t h d imensions s h o u l d be o v e r a l l t o t a l measurements. The l e n g t h i s the dimension of the b u i l d i n g w a l l p a r a l l e l t o the roof r i d g e . I f you have s e v e r a l s e p a r a t e detached greenhouses or greenhouse complexes i t i s recommended t h a t you do s e p a r a t e s i m u l a t i o n s f o r each one. A l t e r n a t i v e l y you may e n t e r d i m e n s i o n f i g u r e s t h a t w i l l r e s u l t i n a t o t a l f l o o r a r e a e q u i v a l e n t t o the 128 sum of the f l o o r a r e a s i n a l l of your greenhouses. T h i s method may not be as a c c u r a t e as s e p a r a t e s i m u l a t i o n s but may be adequate f o r rough comparisons. Temperature S e t p o i n t s These must be i n C e n t i g r a d e d e g r e e s . Here i s a handy c o n v e r s i o n t a b l e t o h e l p you e n t e r the t e m p e r a t u r e i n t h e c o r r e c t u n i t s . T F M P P R A T I I D C C* ~f -*? | M 40 (0 M I M C O N V ^ I O N p* JW»WWr^H#Wf' G l a z i n g Type Separate c h o i c e s f o r s i d e w a l l s and r o o f a r e a l l o w e d . There a r e 9 g l a z i n g t y p e s a v a i l a b l e . A c r y l i c SDP i s s i m i l a r t o the " t w i n w a l l " p o l y c a r b o n a t e type g l a z i n g . E s t i m a t e d L e a k i n e s s Example: i f you had a d o u b l e - p o l y greenhouse w i t h w e a t h e r s t r i p p i n g around a l l v e n t s and doors i t would be r e l a t i v e l y t i g h t . T h e r e f o r e you would e n t e r a 1 or a 2. O r i e n t a t i o n The r o o f l i n e i s the r i d g e l i n e . Energy C o n s e r v a t i o n Measures 1. Root zone h e a t i n g i s an attempt t o d i s t r i b u t e heat t o the p l a n t s more e f f e c t i v e l y . To b e n e f i t from t h i s form of h e a t i n g , the t r a d i t i o n a l placement of h e a t i n g p i p e s i s changed. Hot water i s d e l i v e r e d t o t h e p l a n t e d a r e a v i a hundreds of s m a l l e r tubes t h a t a r e p l a c e d a l o n g t h e f l o o r of the greenhouse near t he p l a n t s ' r o o t s . Heat i s t h e r e b y brought t o the p l a n t s where i t may be needed most. 2. A s t a c k heat r e c o v e r y u n i t i s s i m p l y a g a s - t o - l i q u i d 129 heat exchanger t h a t r e c l a i m s some of the heat from the smokestack of the b o i l e r . 3. I n f r a - r e d r a d i a n t h e a t i n g i s a c c o m p l i s h e d by b u r n i n g n a t u r a l gas i n such a way t h a t the b u l k of i t s energy i s r e l e a s e d as r a d i a t i o n i n the l o n g wave i n f r a - r e d . These b u r n e r s a r e t y p i c a l l y p l a c e d i n the peak of a greenhouse and the r a d i a n t energy i s r e f l e c t e d onto the . p l a n t canopy. In t h i s way the p l a n t s themselves a r e heated d i r e c t l y w i t h no i n t e r v e n i n g heat t r a n s f e r media such as water and a i r — a much more e f f i c i e n t p r o c e s s . 4. Energy s a v i n g s r e s u l t i n g from the use of a microcomputer greenhouse environment management system can a l s o be s i m u l a t e d by the model. The more a c c u r a t e e n v i r o n m e n t a l c o n t r o l r e s u l t i n g from t h e s e systems saves energy, i n c r e a s e s y i e l d , and d e c r e a s e s l a b o u r c o s t s . 5. I n f i l t r a t i o n l o s s e s can be reduced by c a u l k i n g between g l a s s l a p s w i t h s i l i c o n e s e a l a n t . 6. C o v e r i n g the greenhouse w i t h a l a y e r of p l a s t i c f i l m t h a t i s i n f l a t e d w i t h a s m a l l fan i s an o t h e r method t h a t reduces i n f i l t r a t i o n and heat l o s s . 7. Energy s a v i n g measures f o r d e c r e a s i n g c o n d u c t i v e and r a d i a t i v e heat l o s s e s i n v o l v e i n s u l a t i n g some of the greenhouse w a l l s . T y p i c a l l y the n o r t h w a l l i s i n s u l a t e d w i t h opaque p o l y u r e t h a n e or p o l y s t y r e n e foam. I n Canada the sun i s almost always i n the s o u t h e r n p a r t of the sky so l i t t l e d i r e c t s u n l i g h t i s b l o c k e d u s i n g t h i s t e c h n i q u e . 8. A s i m i l a r method i s t h e i n s t a l l a t i o n of s t y r o f o a m i n s u l a t i o n a l o n g i n s i d e w a l l s t o a h e i g h t of one meter. T h i s i s 130 the usual l o c a t i o n of hot water h e a t i n g pipes and i s t h e r e f o r e an i d e a l p l a c e f o r e f f e c t i v e i n s u l a t i o n . 9. A thermal c u r t a i n or blanket i s a f l e x i b l e m a t e r i a l which i s p u l l e d a c r o s s the roof from g u t t e r to g u t t e r and sometimes around the s i d e w a l l s of a greenhouse at n i g h t . M a t e r i a l s range from l i g h t p l a s t i c f i l m to dark heavy laminated f a b r i c . These c o v e r i n g s reduce heat l o s s by co n v e c t i o n , conduction, i n f i l t r a t i o n and r a d i a t i o n and have t h e r e f o r e come under c o n s i d e r a b l e i n v e s t i g a t i o n r e c e n t l y . 10. Heat storage i s based on the r e s u l t s of experiments i n Saanich, B.C. with the Japanese wet e a r t h heat storage system. In t h i s technique hot s o l a r heated a i r i n s i d e the greenhouse i s drawn through pipes b u r i e d beneath the f l o o r , thereby s t o r i n g heat i n the e a r t h under the b u i l d i n g . At night t h i s heat can be recla i m e d to h e l p decrease energy requirements. Remember a "1" i n d i c a t e s that an energy c o n s e r v a t i o n measure w i l l be simulated. S e v e r a l techniques can be simulated s i m u l t a n e o u s l y but be reasonable: f o r example i t would be u n l i k e l y to have i n f r a - r e d h e a t i n g i n s t a l l e d along with a stack-h e a t - r e c o v e r y u n i t because with IR heat there i s no b o i l e r . ECONOMIC VARIABLES -T h i s s e c t i o n asks you to estimate expected r a t e s of economic change. You may enter i n i t i a l v alues or l e t the d e f a u l t values stand. Use decimal f r a c t i o n s when i n p u t i n g % v a l u e s : e.g. Enter 12% as .12. These v a r i a b l e s are a l l a v a i l a b l e f o r m o d i f i c a t i o n l a t e r at any time. As you conduct s e v e r a l s i m u l a t i o n s you can vary some of the economic i n d i c a t o r s to l e a r n how t h e i r f u t u r e change c o u l d a f f e c t your greenhouse's p r o f i t p i c t u r e . 131 I n t e r e s t Rate The f i r s t v a r i a b l e , " i n t e r e s t r a t e " i s a l s o known as the " d i s c o u n t r a t e " . I t i s the r a t e a t which f u t u r e net annual cash f l o w s w i l l be d i s c o u n t e d so t h a t they can be e x p r e s s e d i n p r e s e n t d o l l a r s . I f i n s t e a d of spending money on energy c o n s e r v a t i o n equipment, you put t h a t money i n t o s a v i n g s bonds, you would have a r e t u r n on your investment of about 10% (1984). S i n c e s a v i n g s bonds are a r e l a t i v e l y r i s k l e s s i n v e s t m e n t , t h i s c o u l d be c o n s i d e r e d a r i s k l e s s investment of money. The reason we d i s c o u n t the energy c o n s e r v a t i o n i n v e s t m e n t a t a minimum l e v e l of 10% i s t o r e f l e c t the o p p o r t u n i t y you l o s t t o i n v e s t t h a t money i n s a v i n g s bonds. The reason why the d e f a u l t v a l u e of the d i s c o u n t r a t e i s s e t h i g h e r i s t h a t most pe o p l e view energy c o n s e r v a t i o n i n v e s t m e n t s as b e i n g c o n s i d e r a b l y more r i s k y than s a v i n g s bonds. By i n c l u d i n g the r i s k of energy c o n s e r v a t i o n investment i n t h e d i s c o u n t r a t e you get a more r e a l i s t i c a n a l y s i s of p r o f i t a b l i l i t y . I f you f e e l t h e r e i s l e s s r i s k you may s e t the d i s c o u n t r a t e lower ( t o 16% f o r example, or 10% i f you p e r c e i v e no r i s k ) . Tax Rate T h i s r e p r e s e n t s the m a r g i n a l f e d e r a l and p r o v i n c i a l t a x r a t e . Investment tax c r e d i t s a re not i n c l u d e d i n t h e model. Loan Amount You may want t o l e a v e t h i s b l a n k a t f i r s t and f i l l i t i n a f t e r a number of s i m u l a t i o n r u n s . A f t e r the f i r s t run you w i l l f i n d out the e s t i m a t e d c o s t of your chosen energy c o n s e r v a t i o n scheme based on your greenhouse d i m e n s i o n s . With t h i s knowledge i t w i l l be e a s i e r t o d e c i d e how much t o borrow. 132 Crop V a r i a b l e s Expected Annual Y i e l d There are s e v e r a l ways of exp r e s s i n g y i e l d . We have s t a n d a r d i z e d i t to cases/m2 (where a case i s about 20 pounds). The f o l l o w i n g c o n v e r s i o n t a b l e may h e l p you determine your y i e l d i n cases/m2. To get y i e l d i n cases/m2, MULTIPLY BY pounds tomatoes/sq f t 0. 5376 pounds cucumbers/sq f t 0. 5988 tons tomatoes/acre 0. 025 tons cucumbers/acre 0. 0275 pounds tomatoes/plant 0. 1 344 pounds cucumbers/plant 0. 1 497 cucumbers/sq f t 0. 5988 When you have answered the l a s t q u e s t i o n on GREENDAT you must save i t with your answers on d i s k thereby o v e r w r i t i n g the o l d v e r s i o n of GREENDAT i n the process. Next you load GREENSIM which c o p i e s the data from GREENDAT as i t lo a d s . 1 33 GREENSIM When you f i r s t l o a d GREENSIM the s c r e e n w i l l d i s p l a y a page of v a r i a b l e s . These v a r i a b l e s g i v e you a c o n c i s e r e p o r t on the s i m u l a t i o n you have s e t up and i t s e f f e c t on the energy use and economics of your greenhouse. GREENSIM i s an i n t e r a c t i v e m o d e l l i n g program. That means you can a l t e r 17 of the program v a r i a b l e s , r e c a l c u l a t e the s p r e a d s h e e t , and watch the r e s u l t s change b e f o r e your eyes. In o r d e r t o do t h i s e a s i l l y the v a r i a b l e s a r e d i v i d e d i n t o two groups: c o n t r o l l i n g v a r i a b l e s and i n d i c a t i n g v a r i a b l e s . (see f i g u r e 1) The unshaded a r e a c o n t a i n s the i n d i c a t i n g v a r i a b l e s . These a r e l i k e the speedometer or f u e l gauge of a c a r . They show the c u r r e n t s t a t e and r e s u l t s of the s i m u l a t i o n . The o t h e r v a r i a b l e s a r e the c o n t r o l s , l i k e a c a r ' s gas p e d a l and s t e e r i n g wheel. You can a l t e r any of t h e s e , r e c a l c u l a t e the sp r e a d s h e e t and watch the i n d i c a t o r s change. INDICATING VARIABLES Some of t h e s e a re s i m p l y c o p i e d over from the GREENDAT spr e a d s h e e t t o remind you of the s i z e and age of the greenhouse or the l e n g t h of the h e a t i n g season. O t h e r s a r e c a l c u l a t i o n s based on the da t a i n p u t . There a r e t h r e e s e c t i o n s : 1. A d justments 2. H e a t l o s s summary 3. R e s u l t s . 1. A d justments T h i s s e c t i o n l i s t s the c a l c u l a t e d w e i g h t i n g f a c t o r s t h a t a r e used i n the s i m u l a t i o n . % l i g h t T h i s i s the e x p e c t e d r e d u c t i o n i n l i g h t a v a i l a b l e t o the c r o p brought about by use of energy c o n s e r v a t i o n . 1 34 % f u e l The p r e d i c t e d s a v i n g s i n f u e l use or b o i l e r e f f i c i e n c y are i n d i c a t e d here. % y i e l d The p e r c e n t i n c r e a s e or decrease i n y i e l d e x p e c t e d i s shown her e . 2. Heat Loss Summary A q u i c k comparison of your greenhouse's h e a t - l o s s c h a r a c t e r i s t i c s b e f o r e and a f t e r energy c o n s e r v a t i o n i s p r e s e n t e d i n t h i s s e c t i o n . The t o t a l heat l o s s b e f o r e and a f t e r i s shown on the bottom l i n e i n terms of m e g a j o u l e s / m 2 / y r . Above t h i s i s a comparison of t h e heat l o s s component i n d o l l a r s / f o o t 2 / y r . Many growers keep t r a c k of h e a t i n g c o s t s t h i s way so by p r e s e n t i n g heat l o s s as d o l l a r s , i t s h o u l d be e a s i e r t o comprehend. 3. I n d i c a t o r s And R e s u l t s The r e s u l t s of the c a s h f l o w a n a l y s i s are summarized h e r e . Annual Loan Payment I f the user i n d i c a t e d t h a t money would be borrowed f o r the new equipment, the a n n u a l payment of i n t e r e s t and p r i n c i p a l i s shown h e r e . I t i s c a l c u l a t e d based on the l o a n i n t e r e s t r a t e , and the l o a n amount over a f i f t e e n year term w i t h a n n u a l payments. Change In S a l e s / Y e a r T h i s i s the a n n u a l d o l l a r v a l u e of s a l e s g e n e r a t e d or l o s t by energy c o n s e r v a t i o n . P a r e n t h e s e s around a number i n d i c a t e i t i s a l o s s . The change i n s a l e s i s c a l c u l a t e d by m u l t i p l y i n g the % y i e l d adjustment f a c t o r by the c r o p y i e l d t i m e s the c r o p p r i c e . 1 35 Annual Energy S a v i n g T h i s i s the an n u a l d o l l a r v a l u e of the f u e l saved by energy c o n s e r v a t i o n . Net P r e s e n t V a l u e T h i s i s the p r i m a r y f i n a n c i a l i n d i c a t o r d e r i v e d from the ca s h f l o w a n a l y s i s . I t i s the net economic b e n e f i t i n c u r r e n t d o l l a r s f o r the l i f e of the energy s a v i n g investment (15 y e a r s ) , i n c l u d i n g a l l r i s k s , c o s t s , i n f l a t i o n , d e p r e c i a t i o n and t a x e s . The h i g h e r the NPV the b e t t e r the investment p o t e n t i a l . Zero or n e g a t i v e NPV i m p l i e s a poor i n v e s t m e n t . Break Even Year T h i s i s the p o i n t i n time a t which the c u m u l a t i v e d i s c o u n t e d f u t u r e cash f l o w i s e q u a l t o the i n i t i a l i n v e s t m e n t . CONTROLLING VARIABLES A l l of the v a r i a b l e s i n t h i s s e c t i o n can be changed by the user a t any t i m e . They a r e l i n k e d t o o t h e r p a r t s of the s p r e a d s h e e t . When the r e c a l c u l a t i o n key i s p r e s s e d , changes you have made w i l l be r e f l e c t e d i n the i n d i c a t i n g v a r i a b l e s s e c t i o n of the s c r e e n . When GREENSIM i s f i r s t l o a d e d , the c o n t r o l l i n g v a r i a b l e s ( t h e shaded a r e a i n f i g u r e 2) s h o u l d be checked f o r a c c u r a c y . Some a r e s i m p l e e s t i m a t e s t h a t may need t o be changed by the u s e r . The c o n t r o l l i n g v a r i a b l e s a r e i n 3 s e c t i o n s : 1. Energy S a v i n g Measures, 2. P h y s i c a l S t a t e of the Greenhouse, 3. F i n a n c i a l V a r i a b l e s . 1. Energy S a v i n g Measures As i n GREENDAT you choose a t e c h n i q u e by t y p i n g a "1" t o the 1 36 r i g h t of the energy s a v i n g measure d e s i r e d , (see page 5 f o r a d e t a i l e d d e s c r i p t i o n of the energy s a v i n g measures). You may choose more than one, but p l e a s e don't make up u n r e a s o n a b l e c o m b i n a t i o n s . The e f f e c t s of m u l t i p l e energy s a v i n g measures a r e c u m u l a t i v e not a d d i t i v e . 2. P h y s i c a l S t a t e Of The Greenhouse S i x v a r i a b l e s can be changed t o s i m u l a t e p h y s i c a l a l t e r a t i o n s t o the greenhouse. N i g h t Temp And Day Temp These a r e the n i g h t and day t h e r m o s t a t s e t p o i n t s i n s i d e the greenhouse. To see the e f f e c t on t o t a l heat l o s s you may v a r y t h e s e . No y i e l d e f f e c t s a r e l i n k e d t o the s e temperature changes. G l a z i n g Type F u e l Type Crop Grown These v a r i a b l e s a r e i n d i c a t e d i n e n g l i s h but have c o r r e s p o n d i n g code numbers t o the extreme r i g h t . To change t h e s e , you must change the code numbers. Use the f o l l o w i n g t a b l e t o d e t e r m i n e the p r o p e r numbers: MATERIAL CODE G l a s s 1 double g l a s s 2 p o l y e t h y l e n e 3 double p o l y 4 p o l y + g l a s s 5 f i b r e g l a s ( f l a t ) 6 f i b r e g l a s ( c o r r u g a t e d ) 7 1 i n c h s t y r o f o a m 8 double a c r y l i c ( t w i n w a l l ) 9 o i l 1 n a t u r a l gas 2 e l e c t r i c i t y 3 cucumber 1 tomato 2 There i s no need t o type i n the name, j u s t type the code number i f a change i s d e s i r e d . 1 37 3. F i n a n c i a l V a r i a b l e s A t o t a l of e l e v e n parameters a r e a v a i l a b l e f o r e x p e r i m e n t a t i o n . I n t e r e s t Rate A l s o known as the " d i s c o u n t r a t e " , i t i s t h i s parameter t h a t i s v a r i e d t o s i m u l a t e r i s k . A no r i s k p r o j e c t would have the r i s k e q u i v a l e n t of one year government s e c u r i t i e s (at 11% i n 1984). Average r i s k would be s i m u l a t e d by a 20% d i s c o u n t r a t e based on the h i s t o r i c a l average r i s k of the s t o c k market. By e x p e r i m e n t i n g w i t h t h i s v a r i a b l e you can d e t e r m i n e the r i s k s e n s i t i v i t y of each method of energy c o n s e r v a t i o n f o r your greenhouse. F u e l E s c a l a t i o n And I n f l a t i o n Rate H i s t o r i c a l l y f u e l p r i c e s have c l i m b e d s l i g h t l y f a s t e r than the r e s t of the economy and t h e r e i s no reason t o expect t h i s t r e n d t o change. However, you can s i m u l a t e o t h e r outcomes t o o , t h e r e b y c h e c k i n g the s e n s i t i v i t y of a p a r t i c u l a r energy c o n s e r v a t i o n p l a n t o i n f l a t i o n . Tax Rate T h i s a l l o w s you t o compare, what w i l l happen t o your cash f l o w i f you change t a x b r a c k e t . I t must i n c l u d e the p r o v i n c i a l as w e l l as the f e d e r a l p o r t i o n of income t a x . Loan Amount And Loan I n t e r e s t T h i s c o r r e s p o n d s t o the money you e x p e c t t o borrow f o r purchase and i n s t a l l a t i o n of energy c o n s e r v a t i o n equipment. You s e t the i n t e r e s t r a t e t h a t you f e e l you w i l l have t o pay. Do not s e t the l o a n amount h i g h e r than the i n s t a l l e d c o s t (see b e l o w ) . Crop P r i c e And Y i e l d These can be m o d i f i e d t o match your market p r i c e and greenhouse 1 38 p r o d u c t i v i t y . The e f f e c t of l e a n y e a r s or bumper c r o p s on the f u t u r e cash f l o w can a l s o be m o d e l l e d t h i s way. I n s t a l l e d Cost And Maintenance Cost I f you know what the a c t u a l c o s t of energy c o n s e r v a t i o n w i l l be f o r your s i t u a t i o n you can f i l l i n the c o r r e c t v a l u e s . The v a l u e s shown a r e m a n u f a c t u r e r s ' e s t i m a t e s based on the a r e a of your greenhouse. S i m i l a r l y i f maintenance c o s t s seem too low or h i g h , they may be changed t o your own e x p e c t e d v a l u e s . F u e l Cost T h i s i s the f u e l c o s t i n B r i t i s h Columbia as of 1984. I f you have another s o u r c e or pay a d i f f e r e n t p r i c e f o r energy you may change t h i s v a l u e , t o o . LOOKUP TABLES The user has c o n t r o l over another a r e a of GREENSIM. A l l da t a on p r i c e s , weather, p r o p e r t i e s of m a t e r i a l s , and adjustment f a c t o r s a r e c o n t a i n e d i n l o o k u p t a b l e s a t the end of the program. GREENSIM s e a r c h e s here f o r i n f o r m a t i o n used i n s i m u l a t i n g your greenhouse. By changing t h e s e v a l u e s t o s u i t l o c a l weather o r p r i c e s , the program can e a s i l l y be adapted t o l o c a l needs. There a r e 4 t a b l e s of i n t e r e s t t o the u s e r . These t a b l e s are l o c a t e d a t the bottom of the sp r e a d s h e e t s t a r t i n g a t l i n e 160. The f i r s t time you run GREENSIM, you may want t o "c u s t o m i z e " i t by s e t t i n g up the l o o k - u p t a b l e s t o r e f l e c t l o c a l c o n d i t i o n s . T h i s i s easy t o do w i t h M u l t i p l a n . The weather t a b l e c u r r e n t l y c o n t a i n s 40 year average weather d a t a f o r A b b o t s f o r d , B.C. o b t a i n e d from Environment 1 39 Canada. T h i s c o n s i s t s of average d a i l y low and h i g h t e m p e r a t u r e , r e l a t i v e h u m i d i t y , and s o l a r r a d i a t i o n f o r each month. S i m i l a r i n f o r m a t i o n can be o b t a i n e d from your l o c a l weather o f f i c e . S i m ply type the new v a l u e s over the e x i s t i n g ones. Temperature must be i n C e n t i g r a d e . S o l a r r a d i a t i o n i s i n MJ/m 2/day. The ESM (energy s a v i n g measures) t a b l e c o n t a i n s the w e i g h t i n g f a c t o r s and u n i t p r i c e s f o r the 10 energy s a v i n g t e c h n i q u e s i n the program. These u n i t p r i c e s a r e used t o e s t i m a t e the t o t a l i n s t a l l a t i o n and maintenance c o s t s of energy c o n s e r v a t i o n measures. You can c o r r e c t these c o s t s on the main v a l u e s s c r e e n a f t e r they have been c a l c u l a t e d or you can update the u n i t v a l u e s i n t h i s l o o k - u p t a b l e . These p r i c e s a r e l o c a t e d i n columns 8 and 9. A l l v a l u e s a r e i n $/m2 e x c e p t f o r " s t a c k -heat r e c o v e r y " and "microcomputer c o n t r o l " . S i n c e t h e s e measures are not d i r e c t l y a f u n c t i o n of a r e a the p r i c e g i v e n i s average t o t a l i n s t a l l e d p r i c e . The v a l u e s i n the Maintenance column (column 9) a r e i n $/m 2/yr, e x c e p t f o r s t a c k - h e a t and computer which a r e t o t a l average annu a l maintenance c o s t s . The F u e l Cost t a b l e and Crop Data t a b l e can a l s o be changed, or you can change t h e s e v a r i a b l e s on the main " v a l u e s s c r e e n , " and r e c a l c u l a t e . I f f u t u r e r e s e a r c h y i e l d s new v a l u e s f o r the p r o p e r t i e s of m a t e r i a l s such as t r a n s m i s s i v i t i e s of g l a z i n g s or a i r change r a t e s i n b u i l d i n g s , t h e s e t a b l e s can a l s o be updated. GENERAL USER HINTS In normal use you would p r o b a b l y l o a d GREENDAT, answer the q u e s t i o n s , l o a d GREENSIM, make a few changes and r e c a l c u l a t i o n s , 1 40 and f i n a l l y p r i n t out the v a l u e s page. In some c a s e s a d e t a i l e d l o o k a t the energy b a l a n c e or ca s h f l o w a n a l y s i s might be r e q u i r e d . Use M u l t i p l a n 1 s GOTO command t o a c c o m p l i s h t h i s . The imp o r t a n t a r e a s of GREENSIM have s i n g l e l e t t e r names. For example t o see the cash f l o w a n a l y s i s t y p e : g n f ( t h i s s t a n d s f o r ( g ) o t o (n)ame ( f ) i n a n c i a l a n a l y s i s ) . I n s t r u c t i o n s f o r g e t t i n g around the sp r e a d s h e e t l i k e t h i s a r e g i v e n a t the top of the s p r e a d s h e e t , or by t y p i n g : g n i ( t h a t i s , ( g ) o t o (n)ame ( i ) n s t r u c t i o n s ) . S i m i l a r l y t h e s e a r e a s can be p r i n t e d out u s i n g M u l t i p l a n ' s PRINT command. To p r i n t t he main " v a l u e s s c r e e n " t y p e : pp. The program i s a l r e a d y s e t up t o p r i n t the v a l u e s s c r e e n . I f you wis h t o p r i n t out o t h e r a r e a s of the s p r e a d s h e e t , you must use M u l t i p l a n ' s PRINT OPTIONS command. The most commonly p r i n t e d a r e a s of the scr e e n have been g i v e n names. T h e r e f o r e t o p r i n t the c a s h - f l o w a n a l y s i s s e c t i o n you would t y p e : p o c a s h f l o w <return> <return>. S i m i l a r l y the energy b a l a n c e s can be p r i n t e d by r e p l a c i n g " c a s h f l o w " i n the sequence above by one of the v a r i a b l e s i n the f o l l o w i n g t a b l e : VARIABLE NAME REPRESENTS r e f e b a l a n c e R e f e r e n c e Greenhouse Energy Balance e s g e b a l a n c e Energy S a v i n g Greenhouse Energy B a l a n c e c a s h f l o w C a s h f l o w T a b l e v a l u e s V a l u e s Screen C e r t a i n v a r i a b l e s on GREENSIM cannot be changed w i t h o u t r e l o a d i n g GREENDAT: a r e a , age, and growing season. These a r e v a r i a b l e s t h a t t y p i c a l l y a r e not changed d u r i n g a s i n g l e s i m u l a t i o n . 141 When r e c a l c u l a t i n g GREENSIM t r y changing o n l y one v a r i a b l e a t a t i m e . A l s o w r i t e down r e s u l t s f r e q u e n t l y or e l s e p r i n t out the v a l u e s page a f t e r every r e c a l c u l a t i o n . T h i s w i l l make i t e a s i e r t o i n t e r p r e t the r e s u l t s a f t e r w a r d s . 142 TABLE I: ENERGY SAYING MEASURES INCLUDED IN THIS STUDY ENERGY SAVING MEASURE INSTALLED COST ($/M) ANNUAL ENERGY SAVED POSSIBLE SIDE EFFECTS 1• Root Zone Heating $19.00 15% 2 Uneven heat, fruitset problems 2. Stack Heat Recover 12,000 ea 3 12% 2 High maint, cost, low operating temp. 3. Infra-red Heating 21.00 25% 2 Less light, fruitset problems 4. Microcomputer Control 20,000 ea 4 12% 3 D i f f i c u l t to master 5. Sealing Glass Laps 8.501 20%1 High humidity 6. Second Layer of Glazing 8.001 40%1 Less light, high humidity 7. North Wall Insulation 4.003 10%1 Less light 8. Meter Height Wall Insulation 4.003 10%1 — 9. Thermal Curtains 30.001 35% 3 Less light, high humid, maint. probs. 10.Heat Storage 17.OO5 25%3 High cost, maintenance unproven badger 1979, 2Blom 1982, 3Bryenton 1983, V r i v a 1984, 5Staley 1983. COMPUTER GREENHOUSE ENERGY SAVING SIMULATION FOR:John Q. Farmer 1984 *•* ENERGY BcWJN© MEASURE© *•* URhfaafc r^duce -Ci l©** ; Kl^sl 1I rts-u-1 tan feme fc er i h.£u 1 t n er maj c: W t& i n-5 h e ^ t s i or-age O O O J D O 1 0 o ** "INDICATORS AND RESULTS ** Annual Loan Payment: Change i n S a l e s / y r .: Annual Energy S a v i n g Net Present V a l u e : Break-Even Year : $0. O O $0. O O $2,519.71 Old House Heat Cost $/-ft2 $1.03 •leating from month 2 t o month 10: Tot L o s s e s MJ/m2 2,736 Area and Age' ©lazing Wade Of •SXaxiog an Roo-f Fuel Typ-e Lte-e-d Crap Gr'tJlMtt .Jirfcer&st R-at-e-Fuel eswral at ton In* 1st: i-on Rate T&y. Loan AfttfJurtfe « Loan Interest- ; Crop-Price Y i e l d ftaae&/yr3 Ifts - t A l l e d Cost i •:Mtfi:nte™a'Pee::$Vyrt Fuel Cost * / r w ? G l a s s 3 -ram Natu r a l Gas Tomato 97, 7% 30X. $1-1-.5-4 *S„47r4.7B O. 00-40490/' 35' year's''01 ti' mmmmmm 17 0-ay T e m p , $12,681.21 HEATLOSS SUMMARY ADJUSTMENTS 7. L i g h t O . 007. "/. F u e l O . 007. 7. Y i e l d 0. 007. With E Save $0.79 2, 115 Figure 1: Values Screen Display APPENDIX F: USER'S CHECKLIST 143 1 44 GREENSIM v e r s i o n 1 .0 d r a f t copy C o p y w r i t e d by B a r r y S h e l l , September 1984 USER CHECKLIST 1 . I n s e r t d i s k i n d r i v e A. 2. T y p e : mp < r e t u r n > ( M u l t i p l a n w i l l now l o a d ) 3. T y p e : on < r e t u r n > ( s e l e c t s ( o ) p t i o n : ( n ) o a u t o r e c a l c u l a t i o n ) 4. T y p e : t 1 g r e e n d a t < r e t u r n > ( ( t ) r a n s f e r s and ( l ) o a d s t h e GREENDAT d a t a e n t r y program) D i s k s m i g h t have t o be swapped i n o r d e r t o l o a d GREENDAT. 5. I f n e c e s s a r y p r e s s <Next U n l o c k e d C e l l > t o f i n d t h e s t a r t o f t h e pr o g r a m . 6. YOUR NAME: p r e s s t h e < r e t u r n > key once and t y p e y o u r name. 7. P r e s s <Next U n l o c k e d C e l l > t o move t o t h e n e x t q u e s t i o n . 8. UNITS: You t y p e : ' f e e t ' o r 'meters' d e p e n d i n g what u n i t s , y ou w i l l use f o r t h e g r e e n h o u s e d i m e n s i o n s . 9. A g a i n p r e s s <Next U n l o c k e d C e l l > . 10. E n t e r w a l l h e i g h t . T h i s i s t h e h e i g h t of t h e w a l l f r o m t h e g r o u n d t o t h e g a b l e s . 11. P r e s s <Next U n l o c k e d C e l l > and c o n t i n u e a n s w e r i n g q u e s t i o n s i n t h i s manner u n t i l you have come t o t h e end. F u r t h e r d i s c u s s i o n and e x p l a n a t i o n of t h e s e q u e s t i o n s c a n be f o u n d i n t h e U s e r ' s M a n u a l . 12. A f t e r f i l l i n g i n a l l t h e b l a n k s p r e s s <Cancel> t o e n t e r command mode. Then t y p e : t s < r e t u r n > y (what t h i s means i s : ( t ) r a n s f e r ( s ) a v e ( y ) e s ) 13. To l o a d GREENSIM, t h e s i m u l a t i o n m o d el, t y p e : t 1 g r e e n s i m < r e t u r n > D i s k s w a p p i n g may be n e c e s s a r y . I t w i l l t a k e G r e e n s i m 2 t o 3 m i n u t e s t o l o a d and r e c a l c u l a t e t h e s p r e a d s h e e t . 14. F o r i n t e r p r e t a t i o n o f t h e main v a l u e s s c r e e n s e e t h e U s e r ' s M a n u a l . Use t h e " a r r o w - d i r e c t i o n " k e y s t o move a r o u n d t h e s c r e e n and s i m p l y t y p e i n new v a l u e s where d e s i r e d . I t i s recommended t h a t y ou change o n l y one v a r i a b l e a t a t i m e . 15. P r e s s t h e R e c a l c u l a t i o n Key> o r t h e ! ( e x c l a m a t i o n p o i n t ) t o have t h e s p r e a d s h e e t r e c a l c u l a t e d . A f t e r a p p r o x i m a t e l y one m i n u t e new r e s u l t s w i l l have a p p e a r e d . 145 16. Repeat s t e p s 14 and 15 as many t i m e s as n e c e s s a r y . 17. To view o t h e r p a r t s of GREENSIM t y p e : g n I ( ( g ) o t o (n)ame ( i ) n s t r u c t i o n s ) The i n s t r u c t i o n s f o r moving a b o u t t h e s p r e a d s h e e t a r e shown. T h e r e a r e f i v e s e c t i o n s : (E) n e r g y b a l a n c e (F) i n a n c i a l a n a l y s i s ( L ) o o k u p t a b l e s ( V ) a r i a b l e s and ( Y ) i e l d 18. To p r i n t p a r t s of t h e s p r e a d s h e e t f o l l o w t h e s e s t e p s : To p r i n t t h e main v a l u e s s c r e e n t y p e : pp To p r i n t t h e c a s h f l o w a n a l y s i s t y p e : po c a s h f l o w < r e t u r n > < r e t u r n > . To p r i n t t h e e n e r g y b a l a n c e t y p e : po r e f e b a l a n c e < r e t u r n > < r e t u r n > . F o r more i n f o r m a t i o n see t h e u s e r ' s manual. 

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