Open Collections

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

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

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
831-UBC_1985_A6_7 S52.pdf [ 7.72MB ]
Metadata
JSON: 831-1.0096280.json
JSON-LD: 831-1.0096280-ld.json
RDF/XML (Pretty): 831-1.0096280-rdf.xml
RDF/JSON: 831-1.0096280-rdf.json
Turtle: 831-1.0096280-turtle.txt
N-Triples: 831-1.0096280-rdf-ntriples.txt
Original Record: 831-1.0096280-source.json
Full Text
831-1.0096280-fulltext.txt
Citation
831-1.0096280.ris

Full Text

ECONOMICS  OF  ENERGY  CONSERVATION IN C O M M E R C I A L  MICROCOMPUTER  SPREADSHEET  GREENHOUSES:  MODEL  by BARRY  A THESIS S U B M I T T E D  SHELL  IN P A R T I A L F U L F I L M E N T  THE REQUIREMENTS  FOR  MASTER  OF  THE  DEGREE  OF  SCIENCE  in THE  FACULTY  OF  GRADUATE  STUDIES  Resource Management Science Programme  We  accept this thesis as to the  THE  conforming  required standard  UNIVERSITY  OF  April  BRITISH 1985  Barry Shell,  1985  COLUMBIA  OF  In  presenting  degree  this  thesis  in  partial  fulfilment of  the  requirements  at the The University of British Columbia, I agree that the  for  an  Library shall make  it freely available for reference and study. I further agree that permission for copying  of  Department publication  this  thesis  or  by  of  this  for  his thesis  scholarly  or for  her  purposes  representatives.  Management Science Programme  The University of British Columbia 2075 Wesbrook Place Vancouver, Canada V 6 T 1W5  Date:  April  1985  be  It  financial gain shall not  permission.  Resource  may  granted  is be  by  understood  advanced  the that  Head  extensive of  copying  my or  allowed without my written  Abstract Microcomputer software management  for capital cost analysis in greenhouse  is developed for use by extention  proprietary "spreadsheet"  software is created  energy  workers in agriculture. A "template"  that models greenhouse  for  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  determine  the specifications  for the software  growers was done to  developed and to verify the accuracy  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.  ii  of  Table of Contents Chapter  Page  I. I N T R O D U C T I O N A.  A N D LITERATURE SURVEY  Introduction  1  Industrial Energy Use  2  B . C . Greenhouse  4  Industry Overview  Energy Saving Measures B.  10 Research  12  Agricultural Engineering Studies  15  Energy Balance  16  Economically Optimizing Models  19  Capital Budgeting  20  Computer Simulation  23  OF A GREENHOUSE SIMULATION M O D E L  Introduction The Questionnaire  26  Summary of Questionnaire Results  27  Conclusions Derived from the Questionnaire  28  Project  Software and Hardware  29  Microcomputers and Simulation Models  29  Microcomputer Spreadsheet  30  Modelling Programs  32  Introduction  32  Program Overview B.  26 26  III. T H E S P R E A D S H E E T M O D E L A.  11  Biophysical Studies  II. D E V E L O P M E N T  B.  8  Previous Work History of Greenhouse  A.  1  32  Input Variables  34  L o o k - u p Tables  34 i$i  C.  The Greenhouse Basic  Energy Balance  Assumptions  36  The Energy Fluxes and Balance Equations  36  Convective and Thermal  36  Losses  Conduction Losses  37  Infiltration and Ventilation Losses  38  Net Solar Radiation  40  Energy Saving Greenhouse D.  35  Simulation  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 A.  41  Test of M o d e l Accuracy  48 48  Heat Loss Predictions  48  Financial Analysis  50  Sensitivity Analysis  52  B.  User Testing of the M o d e l  55  C.  Test of the M o d e l with Greenhouse  Operators  V. C O N C L U S I O N  56 59  A.  Energy Use and Y i e l d i n B . C . Greenhouses  59  B.  Value and Limitations of the Study  60  C.  Future Use of Greenhouse  62  Spreadsheet Models  References  64  APPENDIX  A: GREENHOUSE QUESTIONNAIRE  69  APPENDIX  B:  LISTING  73  APPENDIX  C: G R E E N S I M LISTING  77  GREENDAT  IV  APPENDIX  D: GREENSIM  EQUATIONS  APPENDIX  E:  USER'S  MANUAL  APPENDIX  F:  USER'S  CHECKLIST  List of Tables  Number 1.  Page  Table of relative energy consumption for delivery of fresh winter vegetables Northern U.S. market  to a 7  2.  Energy conservation measures  included in this study  3.  Effects of energy saving measures  4.  Sample cash flow analysis  5.  Characteristics of commercial greenhouses  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  on energy balance variables  9 42 44  vi  :  used for program verification  49  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  17  5.  H o w spreadsheet programs work  6.  Logical flow of the G R E E N S I M  7.  Energy use versus yield in B . C . greenhouses  energy fluxes  31 program  viii  33 60  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 D r . M . Novak and D r . R. Heinkel of U . B . C . for providing input towards greenhouse  energy modelling and cost/benefit  development of this software microcomputer  analysis, respectively. Finally, the  would not have been possible without the provision of  facilities by the U . B . C . Centre  vii?  for Continuing Education.  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  SURVEY  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: supply (1) measures  economic analyses or (2)  "Few North American  productivity changes  researchers  for their recommended  for energy conservation, leaving report readers on their own to perform  cost/benefit  analyses."  The objective  model that brings together  project is to create a computer  crop productivity data and energy consumption data in order  to conduct a proper cost/benefit greenhouses.  of this research  analysis of energy saving measures  i n commercial  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 to increase  its authenticity, and lend credence  marriage of a simple energy balance the user to "experiment"  are included in each simulation  to the resulting recommendations.  model with a popular spreadsheet  with a variety of energy saving options (see  The  program allows 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 introduced.  1  that are the subject of this thesis  are  2  World* erlergy demand proje ctions  I  ^---^^—- . 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 i n 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 o f the  "energy crises"  of  the 1970's is thought to be a major reason for the reduction i n 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 i n all sectors is still great that, overall, only 5-10%  It is estimated  of possible savings have been realized. F o r 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 m e t T o date they have only reduced energy consumption by 2% (Tutton 1984).  3  A techniques  need therefore  exists for research  into the application of energy conservation  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 p a y - o f 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 terms of Watts or Megajoules;  1983). In addition, energy is often expressed in  terms that are practically meaningless to the  energy consumer. Energy use engineering studies often use these units of where actual cash flows would be more meaningful to plant managers. comprehensive to demonstrate  average  measure  A  financial analysis of energy conservation investment in industry is needed its viability.  A second problem is that end users are often skeptical about conservation measures  because they believe there  energy  will 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 i n 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 will 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 i n 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  i n 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 B . C . representing approximately 120 hectares  growers in  "under glass". About 90% of these are  located i n the south coastal region. The greenhouse industry accounts for about 10% the total agricultural production of B . C . In recent years the percent market share  of  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% the trade. The remainder is devoted to market vegetables;  of  mainly tomatoes and  cucumbers. Cultural practices vary widely i n 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 i n 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.  trend toward microcomputer control which can cost-effectively  monitor the  There  is a  outdoor  environmental conditions and adjust indoor climate for optimum growth. Also roof vents can be opened i n consideration of the wind direction for passive ventilation with need for electric management  fans. These systems save energy, labour and time. They also aid in  by keeping historical records of climate and control  settings.  Another recent innovation is the use of carbon dioxide enrichment. technique  no  increases yield and produces larger more marketable  N o single type of greenhouse  This  fruits.  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 i n location and management  techniques.  A study showed no correlation between  in 27 Dutch greenhouses  yield and energy  (de Visser 1981). A recent survey of local greenhouse  use energy  use supports this view. The typical greenhouse Polyethylene houses  is usually made of glass, polyethylene, or  fibreglass.  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  plastic slowly deteriorates,  becoming less transparent  but the  It must be replaced every three to  four years. The average  size of a B . C . vegetable  square meters (50,000 square 15,000 square  meters.  greenhouse  complex is approximately 5000  feet) but individual installations can range  from 500  to  6 The two major crops are cucumbers and tomatoes. growing plants and require more energy than tomatoes. for energy are approximately $ 8 / m cucumbers.  1  Between  1975  Cucumbers are warmer  Currently, average  industry costs  2  of planted area for tomatoes  and $ 1 0 / m  and 1983  the fuel cost as a percentage  of total cucumber  2  production cost has risen from 12% to nearly 30% (Fig. 2). As fuel costs are to increase  in the future, energy conservation is essential  for greenhouse  for  expected  vegetables  to  remain competitively priced. The major source of competition i n the B . C . vegetable  market is field grown  produce transported from California and Mexico. Despite transportation costs, the  market  prices o f 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 i n order to be truly competitive. Energy used for greenhouse  heating would have to decrease by a factor  3  of  eight according to the Ohio study. Although great potential for energy conservation been widely claimed in the greenhouse effect this eight fold decrease.  has  industry, it is doubtful whether it alone can  Perhaps changes i n government policies (i.e. energy  tax  credits) or trends in international trade (i.e. falling dollar), will 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  Based on a 1984 B . C . Hydro natural gas price of $0.4049/Billing F o r imported cucumbers, transportation currently works out to cost cucumber. M a n y B . C . growers would not be able to survive i f they were not subsidy from the B . C . Farm Insurance Program amounting to about 1  2  3  U n i t (100 M J ) . about 2 cents per receiving a 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. M a r k e t  Energy  Input  Field  Grown  Local  Greenhouse  33.7  4.0  11.9  9.9  Transportation  54.4  0.0  Heating  0.0  693. 1  100.0  707.0  Cultural  Energy  Processing  and  and  Containers  Cooling  TOTALS  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 industry is relatively small (Bryenton  Energy Saving  conservation in the  greenhouse  1983).  Measures  This thesis  examines  the costs and benefits  available to local growers (see  of ten energy saving  Table 2). Each of these techniques  measures  relies on one of the  three basic methods of reducing energy use i n 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 envelope can be made more resistant to conductive and radiative There are several other practices  building  losses.  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 lowered. There are also techniques  for storing solar heat under benches or below  ground. This can reduce the total energy Dozens of methods  temperatures the  demand.  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 effectively. To benefit  to distribute heat to the plants more  from this form of heating, the traditional placement  of heating  pipes is changed. H o t water is delivered to the planted area via hundreds of smaller tubes that are placed along the floor of the greenhouse thereby brought to the plants where it may be needed  near the plants' roots. Heat is most  Infra-red radiant heating is accomplished by burning natural gas i n such a way that the bulk of its energy is released  as radiation i n the long wave i n f r a - r e d . These  9  Table  2: Energy Conservation Measures Included i n this Study.  ENERGY SAVING MEASURE  INSTALLED COST ($/m )  ANNUAL ENERGY SAVED  POSSIBLE SIDE E F F E C T S  Root Zone Heating  $19.00  15%  2  Uneven heat, fruitset  Stack Heat Recovery  12,000 ea.  12%  2  High mainL cost, low op. temp.  Infra-red Heating  21.00  25%  3  Less light, fruitset problems  12%  3  Difficult to master  J  Microcomputer Control 20,000 ea. Sealing Glass Laps  8.50  Second  8.00  Glazing  North Wall  Insulation  3  4  problems  1  20%'  High humidity  1  40%'  Less light, high humidity  4.00  5  10%'  Less light  Meter Height Insul.  4.00  3  10%'  -  Thermal Curtains  30.00'  35%  3  lowlight, high humid., mainL probs.  Heat Storage  17.00  25%  3  H i g h cost, maintenance  'Badger  5  1979, B l o m 1982, 'Bryenton 1983, K o w a l s k i 2  4  unproven  1984, Staley 1984  burners are typically placed i n the peak of a greenhouse  5  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 efficient  more  process. A  stack heat recovery unit is simply a gas-to-liquid heat exchanger  reclaims some o f the heat from the smokestack  o f the boiler.  Energy savings resulting from the use of a microcomputer environment management  that  greenhouse  system can also be simulated by the model. These were  discussed i n the previous section. The fifth energy saving measure is based on the results of experiments  i n the model is heat storage. This technique i n Saanich, B . C . with the Japanese wet earth  10  heat storage system  (Staley 1984). In this technique  hot solar heated  greenhouse is drawn through pipes buried beneath the the earth energy  floor,  thereby  air inside the storing heat in  under the building. A t night this heat can be reclaimed to help decrease  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 i n the southern part of the sky so little direct sunlight is blocked using this technique. polyurethane  The  model can simulate  this, i n addition to the installation  of  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 night  Materials range from light plastic  around the side walls of a greenhouse at film  to dark heavy laminated fabric. These  coverings reduce heat loss by convection, conduction, infiltration and radiation and therefore come under considerable  investigation  have  recently.  B . Previous Work Four main bodies of information are of interest for this study: the of plant growth, the physics of the greenhouse environment, the capital investment and the computer simulation of real systems.  financial  biophysics  analysis  Plant growth is  of  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-conditioning 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 i n 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 environment was published in 1963.  The 1981  greenhouse  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 i n the past 50 years. " U n t i l World War II, the research  was limited to a single . . . experiment . . . in 4  A demonstration of the "greenhouse effect" showing that convection rather than radiation was the dominant causative factor (Woods 1909).  4  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) these are rare (Van Steekelenburg  but  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 ( P A R ) 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]  ,(5.59-0.4T). ,(1.704-0.006391). (1-e >(6-e' ')  =  [LI]  where, P  =  leaves/plant (plastochron age of plant)  fIT}  =  function of temperature  g{I}  =  function of P A R on rate of leaf formation  T  =  temperature  inside  on rate of leaf formation  greenhouse  I  =  P A R inside greenhouse  t  =  time in days  W/m  2  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 greenhouses.  of  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; first  fresh vegetables  the  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  Y  earliness in days  T  24 hour mean  [1.2]  where,  I  average  temperature  daily irradiation i n  J / c m /day 2  14  T I M E I N MONTHS  Figure 3: Market Price and Crop Y i e l d Dynamics of B . C . Greenhouse B.C. Hothouse Products,  Cucumbers.  (Source:  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 Verhaegh  1981,  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 i n 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  decrease i n light during the early part o f the growing season resulted i n a  10% 20%  reduction i n yield. However, there was no correlation between light levels and yields later on i n 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 Another common energy conservation technique  12%.  that has the potential  of  reducing yields is the use of double glazing; in particular double poly (Baurle Verhaegh 1981,  ASHRAE  1982). For a tomato greenhouse  1978,  Baurle reported a light  reduction of 18% and reduced yield of 7% for the spring crop and 11% for the crop. The higher yield in the spring season  fall  is the opposite of the light reduction  effects reported by other workers and is not explained. F r o m the preceding discussion it can be seen that attempts correlations between  environmental parameters  to  demonstrate  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 greenhouse  are predominantly concerned  with the response  of  the  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 A great deal of work has been parameters  energy  fluxes.  done in measuring the response  of single  to a specific change. For example many studies have measured the  of glazing types on available radiant energy can be found that give equations  effect  flux (e.g. Stoffers 1981). Similar studies  expressing overall heat loss as a function of wind  speed (e.g. Bot 1981). The first attempts greenhouse  to combine all this work into a comprehensive  behavior began in the 60's  analysis  using abstract models. These are sets of  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. that the flow of energy supplied to the greenhouse  " A n energy balance simply states  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 become  environment using this method. In recent years the analyses have  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 ( A T ) (c) the radiation i n the greenhouse  net  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 i g . 4 illustrates the typical energy fluxes that are considered. The energy balance can be written,  17  Figure 4:  QTI  +  e  Schematic  + Q r + Q Ff  Illustration of Typical Greenhouse  =  where, net solar input  Qi  equipment  heat  heat of respiration  Qr  furnace  heat  = convective heat flux Q  g  %  = heat flux to ground  -  ventilation heat loss  (Qc + Q g ) + Q  Energy  Fluxes.  + Q.i + Q.t + Q p . . . .  [1.3]  18  Q.  =  infiltration heat loss  Q  =  thermal radiation to sky  =  heat of  1 t  Qp  Several terms represent photosynthesis  photosynthesis a negligible energy  flux. The heats of respiration and  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 Q j 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 substituted into equation [1.3]  to solve for Q^, the greenhouse  heating  can  be  requirement  M o r e detailed discussion of these individual relationships can be found in Chapter III:B. Businger's steady state analysis was adequate requirements. temperatures  for determining greenhouse  heating  It could not, however, predict transitional changes i n interior and exterior or the influence of thermal storage i n the greenhouse  ground, and the plants themselves.  Throughout the  structure,  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,  Kimball  1973, Kindelan  1980). Takakura (1971) devised a very sophisticated dynamic model which was soon followed by many others.  K i m b a l 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 refinements.  He considered variations in soil moisture and homogeneity,  new  non-linear  evapotranspiration and radiative fluxes from the crop, and humidity. H i s 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. V a n 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 hand-calculated energy balance  based on electrical network  a simple  theory.  Economically Optimizing Models Very little work has been published that includes economic constraints in the greenhouse  or financial  plant/environment model. Perhaps inclusion of these  constraints will be the trend for the  1980's. Seginer  (1980) proposed a model that  optimizes indoor temperature  for maximum economic return from the crop. H i s 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, i n 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  optimal levels. Seginer dV/dT  =  expressed  the economically optimal conditions with the  C(dE/dT)  5  Seginer  =  Market value of the crop ( $ / m  1980.  equation, [1.4]  where, V  economically  2  planted area)  20  E  =  Energy supplied by the heating system ( J / m  T  =  A i r temperature inside the greenhouse  C  =  Cost of energy  His objective  2  planted area)  (°C)  ($/J)  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 i n 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 N e w 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 engineers' physical analyses of energy saving measures.  to  21  A recent review of the energy conservation literature commissioned by the B . C . Greenhouse Vegetable Growers Association noted, "Few energy conservation reports analysed the cost effectiveness (Bryenton eL al. 1983)  of the measures, especially using actual fuel use data."  A procedure was presented for determining annual "normalized"  fuel costs for B . C . greenhouses there  have  with and without energy saving measures. However,  was no attempt to formally calculate the return on investment U n t 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 i n the capital budget 6  There are five conceptual steps that are used i n capital budgeting: 1.  Future cash flows due to the project  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  5.  Brigham  estimated.  project  The calculated N P V is compared to the cost of the project NPV  6  are  1983  exceeds the cost the project is accepted.  and i f the  22  A considerable the three major  problem in capital budgeting is the correct  estimation  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 distribution of values can be developed by a variety of techniques Moore and C h e n 1983, expected  of  learning  After it has been  Saxena  expected  (Hillier  1969,  1983). Some of these methods even allow for an  curve i n the estimation of future cash flows (Harpaz 1984). 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  determination is not absolutely  financial  essential  community. However, its accurate  for a comparative analysis such as this  thesis. A capital budgeting technique evaluate the  financial  was presented  by White et  al. (1980) to  viability of energy conservation i n greenhouses.  White's  investment model was designed specifically for thermal curtains. The cash flows included only the costs and benefits the curtains. H e therefore  (after tax) that resulted from the use  described his technique  as partial  was calculated for the life of the thermal curtain (10 generated  scenarios  rate and crop yield  of  budgeting. The N P V  years) under 100  randomly  of varying fuel price, amount of energy saved, fuel  escalation  changes.  White reported positive N P V i n every case, but this may be due to his choice of relatively low system cost ($21.50/m ) and low discount rate (5%). 2  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  used i n energy use studies of buildings (Marshall and Ruegg 1977,  has  been  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 this  modelling technique  that is developed in  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 energy saving measure.  to explain the behavior of one type  of  The subject of this thesis is a model designed to compare  different strategies for energy conservation in  10  greenhouses.  As Bronson (1984) stated, simulation techniques  are used "to provide structure  and definition to imprecisely worded verbal descriptions, to identify key components, quantify interactions, and then to code, experiment and recommend." environmental effects and sometimes  artfull nature of greenhouse  The  to  complex  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 i n buildings have  been  developed by A S H R A E and others as design tools for mechanical engineers. such as B L A S T , D O E , and C A L - E R D A 1960's at Canada's National Reseach  Programs  all originated from early work done in the  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 relation to greenhouses  They are too complex in areas of little  and not sophisticated enough in dealing with solar heat gain,  methods of controlling and storing solar heat, and simulating heat radiation back the cloudless sky (Bycraft  to  1983).  Several recentiy published simulations were created when the concept of solar heating became popular in the Wylie 1981)  1970's. Programs such as W A T S U N  and E N E R P A S S (Enermodal Engineering L t d 1982)  (Chandrashekar and  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  simulation language was designed to compare the 'degree-day' complicated hourly weather data heat loss analysis (Duncan et engineering design studies employ the 'degree-day' heating load. In this technique 18  ° C . The differences  a representative  method with a more al. 1981).  method to estimate  indoor temperature  between this and the average  computer  Some  the building  is chosen — usually  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 i n ordinary buildings. Duncan reported greenhouse conventional degree-day real greenhouses.  heating requirements 9% less than calculated by the  method. This was corroborated with experimental data from  H i s energy balance  was simple and took advantage  to simulate both discrete (e.g. thermostat  of G A S P ' s ability  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 more feasible  practices to determine  the  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  the potential for energy conservation in  home. Many complicated greenhouse  simulation studies have been developed on large  computers but few simple models for use on microcomputers have been reported. companies offer microcomputer financial accounting packages greenhouse  industry (e.g. Ball Technical Service  1983)  Some  specifically for the  but these do not include heat  loss calculations or capital budgeting analysis. Dempster (1983) described the successful use of VisiCalc microcomputer spreadsheet  models as tools for advising English greenhouse  growers.  financial  II. D E V E L O P M E N T  OF A GREENHOUSE SIMULATION  MODEL  "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 conservation in commercial greenhouses.  i n heat loss and cash flow due to  energy  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 visited,  fifteen  O f the eighteen growers that were  questionnaires were completed. There were three  'Peters and Waterman,  1982. 26  flower growers, one  27  forest seedling nursery, one lettuce grower, four tomato and six cucumber Early in the work it was decided that vegetable  greenhouses.  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 m  2  in size. The units are 2-3  is about 4600  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. F o r 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 was used in this project 128K  on a microcomputer. A Victor 9000 microcomputer  It is based on the Intel 8088 16-bit microprocessor and has  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 spreadsheet  fig.  5). Once the relationships between the individual cells of the  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 " w h a t - 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 i n a very easy to read and flexible format The spreadsheet  software chosen for this project  has emerged as the second most popular spreadsheet 1-2-3  (Cobb et  is Multiplan version 1.05.  8  It  program i n the world after Lotus  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: H o w Spreadsheet Programs W o r k .  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-if?" 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.  A.  THE SPREADSHEET MODEL  Introduction Electronic  themselves.  spreadsheets like Multiplan do not actually do anything all by  In this respect they are similar to programming languages  or B A S I C . Commands must be entered  in a systematic  like  way to create a  program. Spreadsheet programs are called models or templates. The be stored on floppy disk and loaded by Multiplan on any  FORTRAN  specific  resulting model can  machine.  The greenhouse spreadsheet model in this thesis is really two models: entry model, G R E E N D A T , and the analysis of heat-loss and cash-flow, What would otherwise  GREENSIM.  have been a very large model is broken into two smaller ones.  Multiplan's model-linking feature allows values to be transferred GREENSIM  a data  whenever G R E E N S I M  from G R E E N D A T  to  is loaded.  Program Overview F i g . 6 shows the organization and logical flow of G R E E N S I M . A l l of the and many of the program's assumptions  originate in G R E E N D A T  data  and internal l o o k - u p  tables. The basis of the heat-loss simulation is an energy balance.  Simply stated, it is  a summation of all the ways i n which heat can escape from the greenhouse throughout the year. The calculations are based on data from the user and data, etc.,  from internal tables. The  greenhouse serves as a baseline  energy  consumption of a simulated  when evaluating energy  management  weather  reference  schemes. This is  accomplished by calculating the greenhouse energy consumption twice: once in its original reference energy  state as defined by the user, and again with the effects of proposed  conservation  included. These effects are simulated by applying adjustment  based on observations  reported i n the  literature.  32  factors  33  • REFERENCE GREENHOUSE ENERGY BALANCE  PARAMETERWEIGHTING  ENERGY SAVING GREENHOUSE ENERGY BALANCE COST ESTIMATOR  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 i n 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 i n one video screen. Because of the interactive nature of Multiplan 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 i n Figure 6.  34  B. Input Variables GREENSIM GREENDAT  receives data from two sources:  the greenhouse  grower via  and from internal l o o k - u p tables.  The user initially interacts with the simulation model through G R E E N D A T , self-prompting annotated computerized questionnaire (see chosen  a  Appendix B). This method was  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 will accept answers in free format or characters.  numbers  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 l o o k - u p function that is ideally suited for this purpose. G R E E N S I M tables (see  Appendix C , line 160).  light transmission coefficients the Fraser Valley, B . C . area;  has a total of six  These tables contain such information as heat and  of greenhouse  materials; mean monthly weather data for  A S H R A E design values for air changes  and costs of fuel, crops, and energy saving measures.  in  greenhouses;  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 l o o k - u p tables gives the program two levels of adaptability. The first allows individual growers to simulate their own type of greenhouse. there are eight types of greenhouse to choose from.  For  example  glazings and ten different energy saving measures  35  The principle of information tables. A great  hiding  is also employed by the use of l o o k - u p  deal of information about the weather and physical properties o f  materials is hidden from the user. H e need not concern himself with bothersome  countless  details. In this way the task o f modelling appears less daunting, and the  program becomes more  "friendly" and usable.  However, this does not imply that the model has built i n obsolescence  or  error. O n the contrary. The nature of l o o k - u p tables allows a second level of adaptability. A n experienced user can easily update and customize the program to changing needs.  F o r example, by replacing the numbers i n the weather table  similar values for another location, the model can simulate greenhouses climatic zones. Similarly, prices of materials can be updated to reflect economic areas or future trends. Even the adjustment  i n different different  factors used to simulate the  effects of energy saving measures may be updated should future research accurate  with  yield more  results.  C . The Greenhouse Energy  Balance  The computer model i n this project was adapted from the work of Walker et al. (1983) and B l o m et al. (1982). A modified A S H R A E steady-state loss model was used. It is calculated monthly using average published heat transfer coefficients monthly weather  data adequate  for greenhouse  building heat  weather data and  materials. Parsons (1983) found  for the prediction o f greenhouse  heating loads. H i s  results varied within 3% of hourly studies. In order to attain a level o f 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 and Mahrer 1982).  models (e.g. K i m b a l l  1973, Takami and Uchijima 1977, Avissar  36 Basic Assumptions The major 1.  assumptions are:  W i t h respect to radiation, the greenhouse  presents  a horizontal surface  equal to  its floor area. 2.  The convective wall surface  heat transfer through the greenhouse  area and the mean temperature  wall is proportional to the  difference  between  inside and outside  air ( A T ) . 3.  The net radiation in the greenhouse  4.  A l l masses and materials are horizontally and vertically  5.  Optical properties of components  6.  Heat storage in plants, internal air, and structural materials is negligible.  The Energy Fluxes and Balance The energy balance  is entirely absorbed at the ground surface. homogeneous.  do not vary during the simulation.  Equations  equation used i n the model is a simplified version of  Walker et_ al. (1983) given i n Chapter I (see equation [1.3] and F i g . 4): Q  I  +  Q  f  =  Q  g  +  Q  v  +  Q  i  +  Q  t  +  Q  c  where all the Q's are i n 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  Convective and Thermal  month.  Losses  Losses through the building skin, Q , were divided into three components: 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  Q  c  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 of the greenhouse Q  ,  .  roof day  through the roof  for one month is: =  A r  U  ( A T , ) ( H . / 2 4 ) 2.628 r  day  7  v  d  '  [3.2] 1  J  37  where, Q A  c  r o Q  r  U  r  =  daytime heat loss through the roof (MJ/month)  -  2 roof area (m ) '  =  overall coefficient of heat transfer for the roof ( W / m  day  A T  j. ^  v  =  m e a n  a v e r a  8  e  °C)  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 M J / m o n t h . 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 ) / 2 4 in place of their daytime counterparts. The d  program assigns U values from a l o o k - u p 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  day  ( H  d  / 2 4 )  [ 1 3 ]  where, P Up  =  perimeter (m) =  perimeter heat loss coefficient ( W / m  The perimeter U value was 1.39 W/m°C  i f not (from B l o m et  W/m°C  °C)  i f perimeter insulation was used and 2.77  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 heating would not affect total heat requirements. Infiltration losses represent  no 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  day  2-628(^/24)  [3.4]  where, Q. ... , = I sensible day  daytime sensible heat loss by infiltration ( M J / m o ) ' J  V  =  greenhouse  volume  S  =  number of airchanges/hour  v  (m ) 3  (hr ) 1  The constant 0.333 is the product of the heat capacity and density of air multiplied by conversion factors  for h r  1  on the age of the greenhouse,  to sec  -1  and kilojoules to joules.'  A i r changes  are  based  the type of glazing material and the user's estimate  leakiness. For example with an old glass greenhouse, a new double polyethylene greenhouse  of  S would vary from 2 to 4. For  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.  Assuming an average greenhouse air density of 1.19 heat for greenhouse air of 1.009 k J / k g ° C . 9  kg/m  3  and an average  specific  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  crop. H i s suggested Q.  , .  ,  ^i  latent day  Qj  l a t e n t  with evapotranspiration and radiation impinging upon the  ratio was 0.5. Thus,  .  =  0.5 Q  [3.5]  ^ i  L  J  where, 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)] - 10 • 1(  V * TT  =  (1322/(T+273.2))  [3.6]  Wj*  =  saturation vapour density at temperature  where,  T  =  average  night temperature  T (g/m ) 3  (°C)  Saturation vapour densities were calculated for both inside and outside conditions. Latent heat loss was then determined from published monthly average values outside (Environment Canada) and inside the greenhouse  relative humidity  (Mastalerz 1977), as  follows: Q. , . . = ^ i latent night  1.789 V S(V *. R H . T in in  ^i  m  t  t  V * R H Y 2 4 - H ,)/24 T out out d' /v  where, latent night  V-p*^  =  ^T*out RHRH  n  Q u t  =  =  =  8  n t u m e  latent heat losses  saturation vapour density inside greenhouse s a t u r a u o n  va  p o u r density outside greenhouse  relative humidity inside =  (MJ/mo)  greenhouse  relative humidity outside  greenhouse  (g/m ) 3  (g/m ) 3  . . [3.7] L  J  40  The constant factors  1.789  is the latent heat of vaporization of water multiplied by conversion  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 i n chapter  IV, section A .  Net Solar Radiation The monthly average  mean daily solar radiation on a horizontal surface  found i n published tables (Duffie and Beckman Qj  =  30.4  A Q  was  1980). The following formula was used,  t f  H  [3.8]  where, A  =  Qpj  floor area =  (MJ/m  The  (m ) 2  monthly average 2  mean daily solar radiation on a horizontal surface  day)  t  =  short wave transmissivity of the glazing  f  =  shading factor  for framing members  f factor i n the program is set at 0.7.  and reflection, etc. (Businger  The constant  30.4  represents  the  1963). 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  not counted in the model.  is little or no heat storage, so it is  41  The user can specify the heating season as beginning i n 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  (MJ/m )  2.  Annual heating costs (heat load * fuel cost/boiler efficiency)  3.  Heating costs per unit area ( $ / m  2  2  and $/ft ). 2  Energy Saving Greenhouse Simulation GREENSIM  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 parameter the effects are cumulative. For example the U  w  a  same  ^ 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 i n 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. D u e to the nature of spreadsheet software,  42 Table 3: Effects of Energy Saving Measures on Energy Balance Variables. ENERGY SAVING  INFILTRATION  MEASURES  RATE  Rootzone heating  —  Stack Heat Recovery I n f r a - r e d Heat  U walls  U  roof  -  -  NET  U north w a l l  HEATING  RADIATION  —  —  —  -  -  + .15  + .02 e  —  + .12  -  -.05 e  + .25  -.03 e  + .12  + .05  Microcomputer controls S e a l i n g Glass Laps  -0.50 e  -  -  Poly on Class  -0.60 e  -0.32  -0.32  Nwall I n s u l a t i o n  -0.07  -  One meter w a l l l n s u l  -0.02 e  -0.26  -  Thermal Curtains -0.10 e ( e f f e c t s only at night)  -0.53  -0.53  -  -  e  -  Heat Storage  YIELD  EFFICIENCY  --  -  -  -  -.14  ~  -.10  -0..83  -.05 e  -  -.01 e  -0..53  -.04"  -  -  Kowalskl 1984 Baurle 1978 Average of Stokes 1981, Verhaegh 1981, Grange 1983 " Stokes 1981  e  -  1  2  -.005 e  -.05 + .25  3  + .05  denotes estimated value  2  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 all Financing costs and taxes: ACF  t  =  ( 1 - T ) (AS  t  =  net change i n annual cash flow for year L  x  t  -  AK  t  -  AC  {  -  Ay  +  T  x  (ACCAp -  P  where, ACF T  =  x  AS  t  =  federal and provincial tax rate. change i n annual sales revenue due to energy conservation.  t  [3.9]  43  AK  t  =  change in annual expenditure on energy  AC  t  =  change i n annual cost of maintenance  AIj.  =  due to energy  conservation  change in annual interest payments due to loan for energy conservation equipment  ACCAj.  =  change  in Capital Cost Allowance due to energy  conservation  equipment P  All  =  annual portion of principal on loan for energy conservation equipment  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 i n the cash flow analysis can be thought of as taken from statistical distributions. For example A K , the change  means  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  elevated discount rate effectively devalues the future cash-flow of the project  The  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 probability distributions.  This is easier to understand than  44 Table 4: Sample Cash Flow Analysis. E. $'s saved/year : Change i n S a l e s / y r :  $13,715 -$1,207  Loan Payment:  $2,907  annually  ENERGY TEAR 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998  •/-  SALES  -$845 -921 -1,004 -1,094 -1,193 -1,300 -1,417 -1,545 -1,684 -1 ,835 -2,000 -2,180 -2,377 -2,591 -2,824  $'S SAVED MA I NT $9,601 10,561 11,617 12,779 14,057 15,462 17,009 18,709 20,580 22,638 24,902 27,393 30,132 33, 145 36,460  LOAN BAL  $260 $17,000 16,642 283 309 16,231 336 15,759 367 15,215 400 14,591 13,872 436 475 13,045 518 12,095 564 11,002 615 9,745 671 8,300 6,638 731 797 4,726 2,528 869  INTEREST PRINCIPAL $1,785 1 ,747 1 ,704 1 ,654 1,597 1,532 1,456 1,369 1,270 1,155 1,023 871 696 496 265  $357 410 472 543 624 718 826 950 1 ,092 1 ,256 1 ,445 1 ,662 1 ,911 2, 198 2,528  CCA $1,043 939 845 760 684 616 554 499 449 404 363 327 294 265 238  DI SCOUNTED TEAR NUMBER CASHFLOW -$17,786 -$17,786 $7,397 1 $7,397 7,201 2 8,137 7,026 Break-Even 8,972 6,868 4 9,910 6,721 5 10,958 6,582 6 12,127 7 6,449 13,426 8 14,868 6,319 6,193 9 t6,464 6,068 10 18,230 5,945 11 20,181 5,822 12 22,334 5,700 13 24,709 5,579 14 27,327 5,458 15 30,211 brk  Net  Present V a l u e :  even y r :  3  $77,548  Provisions for new equipmentfinancingwere 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 Net  present  Value  value of the project after 15 years is calculated from the cash  flows using the formula:  I  s  ACF  1;  NPV  =  22  ~. ~  <  t=l  + 1  V  IC  [3.10]  where, ^  =  ACF IC  appropriate risk adjusted discount rate =  t  =  net annual change  i n cash flow for year t  grower's contribution to the installed cost  The discount rate used must reflect the uncertainty o f the future cash flows from the energy conservation project  One economic  model often used i n 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  P(T -  f  v  r)  [3.11]  f  m  f  where, Tj. = r  riskless discount rate =  m  j3  =  expected  market return  beta coefficient  of risk  The riskless discount rate, which is equivalent to the one year government rate (=12% term, ^ (  r  m  i n 1984), reflects _ r  f). *  riskier projects.  s  a  risk  the minimum return on any investment  The second  premium designed to adjust the discount rate upward for  The market price o f risk,  ( ~ p> reflects the overall level of risk r  r  m  aversion i n the economy and has a historical average coefficient  10  securities  of risk is a weighting factor that reflects  value of =8%.  The beta  the relative riskiness of a  See page 8 of the User's Manual i n Appendix E for a more detailed explanation.  46  particular venture compared to an investment i n a well diversified securities portfolio. A  B value o f 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 An  attempt was made to determine a B value for energy  investments. A market index Exchange  average. conservation  is published for ten companies on the N e w Y o r k  11  Stock  that specialize i n conservation and solar energy technologies. It was felt that  investments i n these companies would have levels o f risk similar to investment i n greenhouse  energy saving technology.  The B value is defined by the following regression equation, r  g  r  g  -  r  f  =  a  +  /3(r -r ) m  [3.12]  f  where, =  % monthly return of solar stocks  Tj. =  TM  % monthly riskless rate of return on 90 day treasury bills  -  % monthly return o f 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 ) was regressed s t r  a  against (T-TX ° m t  The B coefficient of risk for  v  conservation technologies is the slope of the resulting line. F o r a sample of 23 months (April  1982 to March 1984) the calculated B was 1.46 but statistically different from  zero at only the 10% level.  12  W h e n 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 + 1 . 5 ( r - r ) (12 to 24% in 1984). This f  m  f  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  The Lynch Solar Index, prepared by J. Peter i n Renewable Energy News, Washington, D . C . F test = 2.84, T test = 1.69, n = 23 11  12  growers  Lynch, White Plains, N . Y . , published  47 and is therefore  included in the analysis. Again, due to the interactive nature  Multiplan, the discount rate can be set by the user to any value to reflect perceived risk of the energy conservation measures energy saving measures  of the  i n a particular greenhouse.  Some  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 convenience. It is expected that nominal interest rates would be more to the average greenhouse  comprehensible  operator because they are used i n everyday conversation.  Nominal rates were also necessary two separate inflation rates:  of  because the after-tax  cash flow is generated  with  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  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 point is greater  than three  are rejected i f their  break-even  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.  of  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 There were two main objectives  in testing the G R E E N S I M  MODEL  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: farmers, bankers,  extension workers,  etc.  A. Test of M o d e l 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 which are summarized in Table 5. The weather table was loaded with data for 1983.  Actual energy consumption of the seven farms for the  of  meteorological 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 - 3 0 % average  to  +28%  the  error was essentially zero with standard deviation of 21.8%. T o 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. T w o 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%, were 2415  annual latent heat loss fell to 1,973,000 M J but total annual losses per m M J , a difference of only 1.8%.  2  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 CODE  of Commercial Greenhouses  ENERGY SAVING MEASURES USED  FLOOR A R E A (m ) 2  used for Program Verification.  ROOF GLAZING  CROP TYPE  DAY/NIGHT TEMP °C  Cucumber  23/18  1  1 meter insul, Computer 3846  Glass  2  1 meter insulation  2542  Double poly  Cucumber  20/16.5  3  North Wall  2956  Double poly  Tomato  20/17  4  Poly-on-glass, computer, reduced leakage  4701  Glass  Cucumber  22.5/17  5  Poly-on-glass,  4108  Glass  Cucumber  22/18  6  None  2492  Double poly  Cucumber  23/20  7  None  1003  Glass  Tomato  20/17.5  Insulation  computer  +  +  poly  poly  Table 6: Comparison o f Actual and Simulated Heat Loss. CODE  ACTUAL*  PREDICTED*  ERROR  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:  -0.4%  Standard Deviation:  21.8%  *A11 values i n 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 m m 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 changed from the reference They became times  fibreglass  walls. F o r cultural effects, nothing was  case except for the day and night temperature  setpoints.  2 0 ° C and 15° C , respectively. To model size difference a greenhouse  smaller than the reference  case was simulated.  ten  51 Table 7: Comparison of Energy Saving Investments REFERENCE H E A T L O S S ( M J / m y r ) 2683 E N E R G Y C O S T ($/ft ) 1.00  STRUCTURE 1542 0.58  1  J  2  ENERGY  SAVING  $ NPV  B/E  5  i n Four Types of Greenhouses. 2  CULTURE 1915 0.72  SIZE 3579 1.34  3  4  $ NPV  B/E  $ NPV  B/E  $ NPV  B/E  -43,836  N  -37,921  N  -1,163  N  8,999  7  13,857  5  -6,388  N  -76,588  N  -67,686  N  -2,839  N  MEASURES Rootzone  Heat  Stack-Heat  -25,759 N  Recovery  23,848 4  I.R. Heating  -49,905 N  6  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  Poly-on-Glass  -26,009 N  -101,504  N  -70,976  N  -1,150  N  8,216 1  3,400  2  4,564  1  1,000  1  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  N.  Wall  Insulation  One meter Insulation  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 Y i e l d : 2.45 cases/m Fuel Price : .004049 $ / M J Leakiness (1 to 10): 5 W a l l Height: 8 feet  Inflation Rate: 7% Tax Rate : 30% Crop Price: $11.54/case Fuel Type: Natural Gas Heating Season: February Orientation: North/South Perimeter Insulation: N o  2  1. 2. 3. 4. 5. 6.  October  "Reference" greenhouse was 4700 m , 14 years o l d , made of 3 m m glass throughout, moderately leaky, and kept at 22° C by day and 18° C at night "Structural" changes were air inflated double poly roof, corrugated fibreglass side walls, one year old. "Cultural" changes: daytemp set at 20° C by day and 15° C at night "Size" changes: Small greenhouse was 474 m i n size. " B / E " means: Break Even year o f investment " N " means the investment never breaks even. 2  J  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 usually the lowest return on investment north wall of a large greenhouse energy efficient greenhouse,  insulation had the quickest payback, but  Note that it is more profitable to insulate the  than a small one. However, i f one has a reasonably  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  example, reduced-leakage  like the best options but not in every case. For  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 i n energy  Each energy consuming system is unique and requires special treatment energy savings and  financial  management  to optimize  returns. For example, i n 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 i n Table 7. F o r each setting all other variables remained constant and the results i n terms of N P V and break-even year were noted (see  Table 8).  53 Table  8: Results o f Sensitivity Analysis on Selected Program  VARIABLE  Discount rate 12% 18% 24% Fuel Escalation rate 5% 7% 9% Inflation 5% 7% 9% Tax rate 20% 30% 40% Fuel Price ( $ / M J ) .003239 .00404907 .004858 Crop Y i e l d (case/m ) 1.96 2.45 2.80 Crop price ($/case) 9.23 11.54 13.85 Yield Effect Weighting Rootzone 0.00 + .02 + .04 Infra-red Heating -.05 -.03 + .01 Computer control 0.00 + .05 + .07 Poly-on-glass -.05 -.10 -.15 North W a l l Insulation 0.00 -.005 -.01 -.015  N P V ($)  B-E  68,075 35,285 15,231  Variables.  SENSITIVITY (NPV)  SENSITIVITY (B-E)  6 7 9  High  Moderate  18,000 26,196 35,285  9 8 7  Moderate  Moderate  36,285 35,285 34,136  7 7 7  Low  Low  45,000 35,285 25,524  7 7 8  Low  Low  16,154 35,285 54,390  10 7 6  High  High  36,821 35,285 33,749  7 7 7  Low  Low  36,823 35,285 33,748  7 7 7  Low  Low  -41,119 -25,759 -10,399  N N N  Moderate  Low  -71,231 -49,905 -19,184  N N N  High  Low  16,264 54,663 70,026  6 3 2  High  High  12,515 -26,009 -64,410  12 N N  High  High  12,057 8,216 4,377 536  1 1 2 10  High  Low  1  2  Factor  2  54  Table VARIABLE  NPV  Thermal Curtains -.01 -.05 -.10 Infiltration Effect Weighting Factor Sealing Glass Laps -.70 -.50 -.30 Poly-on-glass -.70 -.40 North W a l l Insulation -.05 -.07 -.09 One-meter Insulation 0.00 -.02 -.04 Thermal Curtains -.05 -.10 -.15  8:  ($)  continued... B-E  1  SENSITIVITY (NPV)  SENSITIVITY (B-E)  35,285 4,564 -33,837  7 14 N  High  High  79,320 52,968 26,617  2 2 4  High  Moderate  -26,009 -65,536  N N  High  Low  1,627 8,216 15,137  4 1 1  High  High  4,296 6,930 9,566  1 1  Moderate  Moderate  29,653 35,285 40,918  7 7  Moderate  Low  NOTES: 1.  B-E  2.  N indicates project never breaks With  indicates break-even  year. even.  thermal curtains being simulated (the  model was very sensitive  first seven variables i n Table  8)  the  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 o f ± 20%.  However, had the yield effect variable  been set higher, as i n other possible scenarios,  the latter parameters would  have shown  55 greater sensitivity. The yield effect variable found in the energy saving measure l o o k - u p 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 yield effects of energy saving measures  and explains why greenhouse  for the  operators are  so  concerned about crop yield effects. The infiltration weighting factor i n 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, i n leakier greenhouses, become  infiltration would  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 i n A u g u s t 1984  at the  Canadian Society of Agricultural Engineering i n Winnipeg, both federal and provincial government extension workers expressed considerable interest i n 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 i n the B . C . Ministry of Agriculture and Food. H e was working on a Multiplan greenhouse greenhouse  enterprise model at the  time.  H e and the  departmental  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 i n the  field.  The object is to  motivate growers to make use of more energy conservation measures and to them i n energy management The above experience 1.  educate  techniques. demonstrated three objectives of this project:  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 their reactions.  farmers were telephoned for  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% Whereas it appeared that G R E E N S I M  too much for the other  greenhouse.  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 i n stages throughout the  growing season. H e noted that B . C . Hydro had sent people out to investigate he had been tampering with his gas  whether  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 i n G R E E N S I M . In November, 1984, greenhouse  the greenhouse  simulation model was demonstrated to a  workshop i n 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 Prince George weather and price data before  the  was pre-loaded with  "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 printout for their own greenhouse  techniques once the simulation  was i n 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.  CONCLUSION  The advent of powerful popular microcomputers and their novel programs such as spreadsheets  and newer integrated packages is a profound revolution i n 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 agriculture, spreadsheets  like  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.  13  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 i n 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 i e l d 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 Sales of energy management systems are expected to grow at the rate of 20% per year (Gessert 1983). 13  59  60  "I  e  3 <—»•© CJ — PJ  1 1 1  •  PLASTIC  ©  GLASS  r  1  i  1—i—i  1  1—g~  o  \ 55 § O  CO s  o  Oo  w 2 W  8L o o  J 0.0  I 1.0  I  I 2.0  I  I S.0  I  i 4.0  L  I  6.0  0.0  i  ' 7.0  l _  YIELD (CASES/M2/YR)  a.o  Figure 7: Energy Use Versus Y i e l d i n B . C . Greenhouses.  reduced energy consumption. Conserving energy does not necessarily imply reduced yields. F i g . 7 therefore indicates the potential for energy conservation i n 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 o f the numbers in the l o o k - u p tables. Installation costs and crop values may change. In addition, continuing research will probably yield better values for the weighting factors i n Table 3. A t present some o f these values are estimates. Fortunately, due to the interactive nature o f 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  middle of a month, it cannot be accurately  in the  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 phenomena  models could be constructed to examine  on an hourly basis. G R E E N S I M  was designed to present cash-flow  these for a  fifteen year period, so hourly variations could not be included. The GREENSIM  emphasis on energy conservation measures  is another  restriction of the  model. In trials with farmers questions were raised about the optimum  size of the greenhouse, irrigation water and C 0  best planting or harvesting time, boiler sizing, heating 2  enrichment  of  W i t h 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 Since G R E E N S I M  to use it without plenty of guidance and explanation.  is designed for agricultural support workers who are already skilled  i n the use of electronic spreadsheets, not be a major  perhaps with appropriate documentation this will  barrier. Future versions could use integrated software packages  that  present the results graphically. The 21.8%  standard deviation of the heat loss simulation is  considering that the simulated greenhouses  reasonable,  were not under experimental control. In  some cases, for example, farm residential energy use may have been included i n Hydro's energy consumption figures. Also effects of local microclimate and variations i n  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  robust model that could be easily adapted to many research  indicates this is a  and extension applications.  Just as N R C ' s microcomputer program, H O T C A N , was used to promote conservation i n the home, so G R E E N S I M  energy  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  C.  Future Use of Greenhouse Spreadsheet In  Models  the coming years microcomputers will become  GREENSIM  will be more accurate  expert system spreadsheets and  financing.  more powerful. Programs like  and allow more refined analysis. There may be  especially for greenhouses.  They will  find  use i n government  university extension offices where they can be used to advise and educate  public. In addition they may  find  their way into marketing co-ops, seed  agricultural supply houses, engineering and economic consulting It is possible that G R E E N S I M  the  companies,  firms.  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  i n B . C . similar to one reported by Rynk et  al. (1982) i n 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  fisheries  with  spreadsheet  models i n federal  policy making. H e felt that microcomputer  spreadsheet  models were a "significant breakthrough in getting decision-makers to use  63 quantitative tools."  Micros were cheaper  mainframes. The spreadsheet  and quicker to yield results than  large  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  For farmers they have developed models of swine barns, wheat These are used to assist the farmer and to determine  fields,  "enterprises". dairy herds,  the status of his venture  etc.  for  credit purposes. Spreadsheet  models of greenhouses  will probably be used for educational  purposes as well. The physics of the greenhouse addition to management  techniques,  economics,  environment can be studied in  crop scheduling and nutritional  requirements. It is possible that the future power and portability of microcomputers will tomorrow's electronic  spreadsheets as common as pocket calculators are today.  make  References  Albright, L . D . , Personal  communication, M a y 1983  Aldrich, R . A . , Downs, R.S., e t al. 1983 The effect o f environment on plant growth, i n Ventilation £ f Agricultural Structures. E d . M . A . Hellickson, A S A E monograph # 6 , S t Joseph, Michigan, p. 217 A S H R A E 1982, American Society o f Heating, Refrigeration and Airconditioning Engineers Applications Handbook, Chapter 21.7 Avissar, R . ; Mahrer, Y . 1982, Verification study o f a numerical greenhouse model, Trans, of Hie. A S A E . p. 1711 Badger, P . C ; Poole, H . A . 1979, Conserving Energy i n O h i o Greenhouses. Department o f Energy  microclimate  Ohio  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 o f 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 Bot  G . P . A . 1981, Heating load o f a glasshouse Hon, #115, p. 335  from the physical point of view, Acia  Brigham, E . F . ; K a h l , A . L . ; Rentz, W . F . 1983, Canadian Financial Management: and Practice. H o l t Rinehart and Winston o f Canada, p. 340  Theory  Bronson, R . 1984, Computer simulation, Bvte. M a r c h , 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, T h e greenhouse climate, i n Physics o f Plant Environment 2nd Edition, E d . W . R . V a n Wijk, North Holland Publishing C o . , Amsterdam, p. 277 Bycraft, R.S., Chief, Computer Aided Design Centre, N . R . C . , Personal March, 1983  communication,  Challa, H . ; V a n de Vooren, J. 1980, A strategy for climate control i n greenhouses i n 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 . ; V a n de Vooren, J. 1981, Economical optimization o f energy consumption i n an early cuke crop, Acta F i e r i #118, p. 191 Chandrashekar, M . ; W y l i e , R . H . 1981, W A T S U N - 3 : Solar heating simulation and economic evaluation program, The Energy Resource Group, Department o f Mechanical Engineering, U . o f Waterloo, Ontario 64  65  Cobb, D . F . ; Cobb, G . B . ; Henderson, T.B. 1983, Multiplail 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, N o v a Energy Ltd., Dartmouth, Nova Scotia, A g . Can. contract #34SZ-01799-0-0550 Dempster, J . H . 1983, Financial computer models for glasshouse Ada Hon... #135, p. 93 de Visser, A . J . 1981, Economic aspects o f cucumber H o r t #118, p. 11  crops on microcomputers,  growing i n the Netherlands, Ada  Duffie, J . A . ; Beckman, W . A . 1980, Solar Engineering £>f Thermal Processes. John and Sons, N e w York, p. 749  Wiley  Dumont, R.S.; L u x , M . E . ; O r r , H . W . 1982, H O T C A N : A computer program for estimating the space heating requirement o f residences, National Research Council of Canada, Computer Program # CP49, Ottawa, September Duncan, G . A . ; Loewer Jr., O . J . ; Colliver, D . G . 1981, Simulation o f energy flows i n a greenhouse: magnitudes and conservation potential, Trans, o f Hie A S A E . v o l . 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 100  saving systems,  Business  Week.  M a y 23, p.  Grange, R.I.; H u r d , R . G . 1983, Thermal screens: environmental plant studies, U Q I L , v o l . 19, no. 3 - 4 , A p r i l , p. 201 Harpaz, G . ; Thomadakis, S.B. 1984, Project valuation with imperfect Engineering Economist, v o l . 29, no. 2, p. 101  Scientia  information,  H i l b o r n , R . ; Walters, C . J . ; Peterman, R . P . ; Staley, M . J . 1984, Models and fisheries: a case study i n implementation, North American 1 £ f Fisheries Management 4:9-14 Hillier, F.S. 1969, J h £ Evaluation o j Risky Interrelated Publishing C o . , Amsterdam  Investments, North Holland  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 o f cucumber growth to form bases for managing the plant environment system, Ada H Q I L #87, p. 215 Horiguchi, I. 1979, The variation o f heating load coefficient H o r t #87, p.95 Jackson,  for the greenhouse,  B . 1983, The nature o f investment problems in energy conservation,  Ada  Energy  66  World. December,  p. 9  Kimball, B . A . 1973, Simulation of the energy balance MeteoroL v o l . 11, p. 243  of a greenhouse,  Kindelan, M . 1980, Dynamic modelling of greenhouse  environment, Trans, .of J&fi  ASAE, vol. 23, p. 1232  Agric.  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 E d . , 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 . ; C h e n , S. 1983, The value of perfect information i n capital budgeting decisions with unknown cash flow parameters, Engineering Economist v o l . 29, no. 1. P- 41 Morris, L . G . ; Winspear, J. 1967, The control o f temperature and humidity i n glasshouses by heating and ventilation, Proc. Agric. Eng, Symp.. Silsoe, England Nehring, R . ; V a n D r i e s t E . R . 1981, T h e discovery of significant o i l and gas fields in the U.S., Rand Corp. 1981, as reported by Bill S t John, Exploration and Economics Hie Petroleum Jjiajjstry, v o l . 20, p. 1 Parsons, B . K . 1983, The simulation and design of building attached Thesis, U . of Wisconsin, Madison  sunspaces,  Masters  Peters, T.J.; Waterman, R . H . Jr. 1982, I D Sfiaicli £f Excellence. Harper and Row, N e w 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 i n greenhouses—how Acta H Q I L , #115, p. 143.  low can we go?,  Rynk, R ; Schrader, R ; L i g h t P . G . 1982, A n energy audit for commercial growers, ASAE Paper m 82-4535. Winter Meeting, Chicago Saxena, U . 1983, Investment 33.  greenhouse  analysis under uncertainty, Eng. Econ., vol. 29, no. 1, p.  Scott, G . 1984, Managing Director, B . C . Hothouse Products, Personal communication  67  Seginer, I. 1980, Optimizing greenhouse JJQIL  Seginer,  #106,  operation for best aerial environment, Acta  p. 169-178  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, N e w Jersey Silveston, P.L.; Costigane, W . D . ; Tiessen, H . ; Hudgins, R . R . 1980, Energy conservation through control o f greenhouse humidity, C a n . Agric. Eng.. V o l . 22, N o . 2, p. 125 Staley, L . M . ; M o n k , G . J . 1984, A comparative study o f solar energy capture, use and conservation in conventional, solar shed, rock storage and wet earth thermal storage units, A g . C a n . contract #08SB.01843-1-ER04 Stoffers, J . A . ; van den Kieboom, A . M . 1981, Energy fluxes in greenhouses, #115, p.151 Stokes, D . A . ; Tinley, G . H . 1981, Cucumber: thermal screen investigations, Ada Hort.. #118, p. 135  Acta Hort..  and growing media  Takakura, T.; Jordan, K..A.; Boyd, L . L . 1971, Dynamic simulation o f plant growth and environment i n the greenhouse, Trans, o f i h e A S A E . 14(5), p. 964 Takami, S.; Uchijima, Z . 1977, A model for the greenhouse environment as affected by the mass and energy exchange o f a crop, L Agric. Meteorology. 33(3), p. 117 Tutton, M . 1984, Pulp and paper: the need to modernize i n Canada's largest industry, Renewable Energy News. V o l . 6, N o . 11, p. C 3 Van  de Braak, N J . 1981, Thermal problem solving by handcalculations, an application of network theory, A c t a H o r t . #115, p. 365  Van  de Vooren, J.; Challa, H . 1978, Influence o f varying night temperatures cucumber crop, Ada Hort,. #87, p. 249  Van  Steekelenburg, N . A . M . ; V a n de Vooren, J. 1981, Influence o f the glasshouse climate on development o f diseases i n a cucumber. crop with special reference to stem and fruit rot caused by didymella bryoniae, Ada Hon,, #118, p. 45  on a  Verhaegh, A . P . 1981, The influence o f insulation techniques on crop production and profitability i n the Dutch glasshouse industry, Acta BssL, #115, p. 453 Walker, J . N . ; Duncan, G . A . 1978, Engineering considerations o f energy problems i n protected cultivation, Acta Hon,, #76, p. 67 Walker, J . N . ; Aldrich, R . A . ; Short, T . H . 1983, Quantity o f air flow for greenhouse structures, i n Hellickson, M . A . , E d . , 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 o f movable interior blankets for greenhouses, i n Agricultural Energy, A S A E National Energy  Symposium, V o l . 2, p. 562 Wilmer, D . B . 1982, Energy supply and demand i n the 1980's, Exploration and Economics jas Petroleum industry, vol. 20, p. 173 Woods, R . W . 1909, Note on the theory  of the greenhouse, Phil. Mag., p. 319  APPENDIX  A: G R E E N H O U S E  69  QUESTIONNAIRE  70  1983 GREENHOUSE QUESTIONAIRE  Name:  Address:  1. Crop: Special  "  Date:  Species: Care R e q u i r e m e n t s :  Yield:_ Costs and Gross Sales ( e s t . ) :  2. S t r u c t u r e  type  Dimensions:  Number o f  (Gutter-connected)  (Gable)  A  B:  C:  D  E:  F:  (Arched)  (Quonset)  G:  H:  Units A 1  Age:  t E  Glazing: For each house i n d i c a t e t y p e , l a y e r s ,  1'  1  Number o f Growing A r e a s ^  B  Single gable greenhouse.  age.  A  .A 1\  E  B  Gutter-connected gable greenhouses. •D-  Insulation:  Infiltration:  A  -BGutter-connected, curved-roof greenhouse. •D-  Orientation:  Ouonset-style house.  71  Page 2  3.  Envi ronmental Ventilation:  1983 GREENHOUSE QUESTIONAIRE  Control ( f a n , vent  location)  Heating:  Fuel:  Set P o i n t s :  Night:  Day:  Night:  Day:  Night:  Day:  Water and H u m i d i t y  Boiler:  control:  Rel H u m i d i t y :  Controls:  4.  Costs:  May we use B.C. H y d r o ' s energy consumption f i g u r e s ?  Other c o s t s :  ( l a b o u r , growing, chemicals, w a t e r , maintenance,  etc.)  I s greenhouse on s e p a r a t e meter? 5.  Time  Growing s e a s o n :  Day l e n g t h ( h o u r s ) :  Start:  End:  Days:  72 Page 3  ENERGY SAVING MEASURE SUMMARY TABLE  6.  ITEM  COMMENTS DATE  COST  ....  7.  GREENHOUSE QUESTIONAIRE  Additional  Comments  MAI NT  pro  con  APPENDIX  B:  GREENDAT  73  LISTING  74  ***************** GREENHOUSE ECONOMIC ANALYSIS ***************** Copywrited by Barry S h e l l Sept 1984 Data Entry Spreadsheet *GREENDAT* v e r s i o n 1.1 T h i s spreadsheet c o n t a i n s a l l the q u e s t i o n s and blanks which when answered w i l l be used to perform the s i m u l a t i o n a n a l y s i s on another s p r e a d s h e e t . A l l the input v a l u e s a r e i n d i c a t e d by dashed l i n e s . By p r e s s i n g the <FUNCTION 4> key, you w i l l a u t o m a t i c a l l y move to the next input p o i n t . D e f a u l t values a r e presented a t a l l input p o i n t s and may be l e f t the same or changed t o s u i t the s i m u l a t i o n . To begin use <F4> t o move to the f i r s t input v a l u e : your name. Press the <Return> key once then type i n your name (15 l e t t e r s o n l y ) . Now use the <F4> key t o move to the next input p o i n t . Type i n the a p p r o p r i a t e words or numbers and simply use <F4> to move to the next input p o i n t . The arrow keys can a l s o be used t o move around on the screen. ****************************************************************** YOUR NAME: John Q. Farmer DIMENSIONS:  Choose u n i t s , type i n ' f e e t ' or 'meters'  Wall h e i g h t A: House width B: House l e n g t h C:  8 200 54  /  /  7 /  feet  /  /  c  TEMPERATURE SET POINTS: Day  (C) :  HUMIDITY SET AT: WALL GLAZING TYPE; Glass Double g l a s s Polyethylene Double p o l y Poly + g l a s s Fibreglass Fibreglass A c r y l i c SDP  20 85%  Night  (C) :  (% R e l a t i v e Humidity)  Enter a number which best d e s c r i b e s your type of wall glazing. (See manual f o r d e t a i l e d d e s c r i p t i o n of types.)  (1) (2) (3) (4) (5) (6) ( f l a t ) (7) (Corrugated) (9)  ROOF GLAZING:  17  Enter  1  a number from the l i s t  above:  FUEL TYPE: Oil Gas Electricity YEAR BUILT:  (1) (2) (3) 1 949  CURRENT YEAR:  1 984  75  ESTIMATED LEAKINESS: of  Rate the  ' t i g h t n e s s ' of your greenhouse on a s c a l e  1 to 10 with  Leakiness:  10 being very leaky and  1 very  tight.  5  ORIENTATION: Type i n 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 a l i g n e d East/West. North/South ( 1 ) : East/West (2): 2 HEATING SEASON: 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. S t a r t i n g month: 2 t o Ending month 10 ***************** ENERGY CONSERVATION MEASURES ******************* In t h i s s e c t i o n you may d e s c r i b e the kinds of energy s a v i n g t e c h n i q u e s used, or those you would l i k e to e v a l u a t e . 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 p e r i m e t e r f o o t i n g w a l l s are u n i n s u l a t e d .  yes or no ?  no  Here are the 10 energy s a v i n g measures that can be s i m u l a t e d by the GRNHEAT program. Typing a '1' to the r i g h t of a name t u r n s on t h a t technique. Typing a '0' t u r n s i t o f f . You can s t a r t o f f by s w i t c h i n g on the energy c o n s e r v a t i o n measures you now use. L a t e r you can t r y adding o t h e r s to see the e f f e c t i t w i l l have on energy consumption. NAME ON/OFF 1 Rootzone h e a t i n g 0 2 Stackheat r e c o v e r y 0 3 InfraRed h e a t i n g 0 4 Computer c o n t r o l 0 5 Reduced leakage 1 6 Poly-on-glass 0 7 North Wall I n s u l a t i o n 0 8 Onemeter perim i n s u l a t i o n 1 9 Thermal C u r t a i n s 0 10 Thermal Storage 0 ******************* ECONOMIC VARIABLES ************************** In t h i s s e c t i o n you are asked to enter e s t i m a t e s about f u t u r e trends i n major economic parameters. I n d i c a t e what you expect the average v a l u e s to be f o r the next 10 or 15 y e a r s . I n t e r e s t Rate:  18%  T h i s s h o u l d be the long term government bond r a t e + 13% r i s k f a c t o r , (approx. 24%)  I n f l a t i o n Rate:  7%  T h i s i s the expected i n f l a t i o n r a t e f o r the next 10 - 15 y e a r s , ( c u r r e n t l y about 8%)  Fuel E s c a l l a t i o n :  9%  T h i s i s the r a t e at which the p r i c e of  fuel  76  i s expected to i n c r e a s e f o r the next 10 -15 years. U s u a l l y about 2% above i n f l a t i o n . Tax Rate: Loan Amount:  LoanInterestRate: *******************  30% $0.00  15% CROP  T h i s should be the tax r a t e you expect to pay f o r the next 10 - 15 y e a r s . I f you w i l l borrow money to pay f o r energy c o n s e r v a t i o n equipment, enter the amount you expect to borrow. Otherwise enter 0. T h i s i s the i n t e r e s t f o r the above l o a n . VARIABLES  r a t e you expect t o pay  **************************  In t h i s s e c t i o n you d e s c r i b e your crop, expected y i e l d market p r i c e you w i l l r e c e i v e over the growing season.  and average  Type the number c o r r e s p o n d i n g to the main c r o p grown: Cucumber ( 1 ) : Tomato (2): 2 Expected annual y i e l d i n c a s e s / s q u a r e meter (e.g. 2.45 f o r tomatoes, 4.12 f o r cukes)  2.45  Expected market p r i c e per c a s e : (e.g. $11.53 f o r tomatoes & $10 f o r cukes)  $11.54  ************************************************************************* T h i s ends the data e n t r y p o r t i o n of the program. Next you w i l l l o a d the Greenhouse S i m u l a t i o n Spreadsheet and t r y v a r i o u s c o m b i n a t i o n s of energy s a v i n g measures. To change spreadsheets type: <F1>  (T)ransfer  (S)ave <Return>  (Y) ( T ) r a n s f e r  (L)oad GREENSIM  <Return>  A f t e r a c o u p l e 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 v a r i a b l e s show the c u r r e n t s c e n a r i o being s i m u l a t e d and g i v e the s t a t e of the s i m u l a t i o n model. The ' I n d i c a t o r s ' show the p r e d i c t e d outcome of the use of v a r i o u s energy c o n s e r v a t i o n measures. You may change the v a l u e s of the v a r i a b l e s to t r y out the e f f e c t s of v a r i o u s combinations of energy c o n s e r v a t i o n t e c h n i q u e s . Type a '1' t o s w i t c h on an energy c o n s e r v a t i o n measure or a '0' t o t u r n i t o f f . To see how the r e s u l t s change w i t h d i f f e r e n t economic f u t u r e s , you can change the v a r i o u s i n t e r e s t r a t e s , p r i c e s or y i e l d v a l u e s .  *************************************************************************  APPENDIX  C: G R E E N S I M  77  LISTING  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 4  1 2 3 4 5 * * * * * * * * * * * * * * * * * * * * * * * * GREENHOUSE ECONOMIC ANALYSIS * * * * * * * * * * * * * * * * * * * * C o p y w r i t e d by B a r r y S h e l l S e p t 1984 •GREENSIM* v e r s i o n 1.1 GETTING AROUND: To s e e a p a r t o f t h e p r o g r a m t y p e ( G ) o (N)ame a n d t h e f i r s t l e t t e r of the s e c t i o n : (E) nergy balance (V)ariables (F) i n a n c i a l anal Crop ( Y ) i e l d ( L ) o o k u p t a b l e s (I ) n s t r u c t i o n s F o r example t o s e e t h e F i n a n c i a l a n a l y s i s , you would type: G N F <return> COMPUTER GREENHOUSE ENERGY SAVING ** ENERGY SAVING MEASURES ** 0 rootzone stackheat 0 IRheat 0 computer 0 reducedleak 1 0 polyonglass 0 Nwal1insul onemeterinsu1 1 0 therma1 c u r t a i n s 0 heatstorage  SIMULATION FOR J o h n Q. F a r m e r 1984 A r e a a n d Age 1,003.32 m2 35 y e a r s o l d G l a z i n g Made Of 1 * * * * * * * * * VARIABLES TRANSFERRED FROM GR Glass 3 mm 1 Units of length feet G l a z i n g on Roof Glass 3 mm 2 N a t u r a l Gas Houselength 54 Fuel Type Used 2 Tomato Housew i d t h 200 C r o p Grown 18% Wal1 h e i g h t 8 N i g h t Temp. I n t e r e s t Rate 9% 17 Day temp. C 20 Fuel e s c a l a t i o n 7% 17 N i g h t temp. C I n f l a t i o n Rate Day Temp. 30% 20 Tax R a t e 5 L o a n Amount Leakiness e s t . ADJUSTMENTS $0.00 Loan I n t e r e s t % Light Or i e n t a t i on 2 15% $1 1 .54 C r o p P r i c e $/case 0.00% .00404907 2,458.1 F u e l C o s t $/MJ Yield (cases/yr) % Fuel INDICATORS AND RESULTS ** 2 $3,474.78 Crop Type $0.00 I n s t a l l e d C o s t : 0.00% A n n u a l L o a n Payment: $63.37 Marketpr i ce $11.54 $0.00 M a i n t e n a n c e $ / y r % Yield Change i n S a l e s / y r : Yield $2,519.71 F u e l C o s t $/MJ : 0.00404907 0.00% 2.45 Annual Energy Saving O l d House $12,681.21 HEATLOSS SUMMARY With E Save Startmonth 2 Net P r e s e n t V a l u e $0.79 Endmonth 10 2 Heat C o s t $ / f t 2 $1 .03 Break-Even Year 2,115 2,736 H e a t i n g f r o m month 2 t o month 10:Tot L o s s e s MJ/m2  CALC OF AREAS ETC Surface area: Volume: Perimeter: Floor area: N wal1 a r e a : Roof a r e a T INTERMEDIATE  CALCS  1,510.93 2,689.63 154.94 1,003.32 40.13 1,103.65 FOR  REFERENCE  square meters c u b i c meters meters square meters square meters square meters GREENHOUSE  0  41 42 43 44 45 46 47 48 49 50 51 52 53 54  Wal1 Roof  U U  value: value:  6.45 6.45  G r e e n h o u s e Age: Airchanges: Infiltration  CALCULATION  m * C m * C  offmonth=0  Old  1 3  losses  Vapour D e n s i t y i n Nightime  W/sq W/sq  2,686.94  W/deg C ( s e n s i b l e  C a l c : T e t e n s Vap* Vap i n s i d e  OF NET  ANNUAL HEATING |  Jan  0=new  volumes/hour heat  1=old 1  only)  14.45 g / c u b i c M 11.13 g / c u b i c M LOAD OF REFERENCE Feb  GREENHOUSE Mar  Apr  May  Jun  Jul  cc OO  56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 "7 V  1  2  Avg O a y l i g h t Hours Outside Day Temp C Outside N i t e Temp C DeltaT (day) deg C DeltaT ( n i g h t ) C DeltaVap (N1te)g/M3 DAYTIME LOSSES 0 r a d i a t i o n (MJ/mo) 0 roof (MJ/month) 0 North wal1 0 E,W,S walIs 0 perimeter Qi n f 1 1 t r a t ion(sens) Qinfltration(latnt) NIGHTTIME LOSSES 0 roof (MJ/month) 0 North wal1 0 E,W,S walIs 0 perimeter Oinfiltration(sens) Qi n f 1 t r a t ion(1atnt)  3  4  5  6  7  9 5.29 -0.27 14.71 17.27 7.33  10 7.56 0.96 12.44 16.04 7.15  12 9.65 2.30 10.35 14.70 7.11  14 13.98 4.83 6 .02 12. 17 6. 16  15.5 16.83 7.84 3. 17 9. 16 5. 16  -59,785 103,196 3.753 34,329 6,222 38,952 29,893  -81,805 96,968 3,526 32,257 5,846 36,601 40,903  -147,096 96.812 3,520 32,205 5,837 36,542 73,548  -269,804 65,695 2,389 21 ,854 3,961 24,797 134,902  -368,124 38,300 1 , 393 12,741 2,309 14,457 184,062  201,925 7 , 343 67,172 12,174 76,218 66,096  175,040 6,365 58,229 10,553 66,070 60,166  137,501 5,000 45,741 8,290 51,900 51 ,276  94,863 3,450 31,557 5,719 35,807 37,051  587,486  510,719  401,077  0 $0.00  729,599 $2,954.20  572,967 $2,319.98  8 16 19.60 10. 74 0.40 6 . 26 4 .05  15.5 22 .09 12.47 -2 .09 4 . 53 3.13  -379,153 4,989 181 1 ,660 301 1 ,883 189,577  0 0 0 0 0 0 0  60,690 2,207 20,189 3,659 22,908 26,384  39,036 1 ,420 12,986 2, 354 14,735 19,467  30,014 1 ,091 9, 984 1,810 11,329 15,98 1  208,447  136,037  89,997  70,209  297,781 $1,205.74  194,339 $786.89  128,567 $520.57  100,299 $406.12  / /  78 Monthly 7Q / 23  80 81 82 83 84 85  totals  Fuel Required Cost of Fuel  (MJ)  O t o t a l MJ/sq m y r  2,735.72  Costs @ $0.405/100MJ  $11.08 per sq m  $1.03  per sq f t  8 6  87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108  INTERMEDIATE  CALC FOR ENERGY SAVING GREENHOUSE i f reduceleak i f polyonglass i f Imeterinsul ESG E, W,& S wall U: 6.321 6.321 4.67754 W/sq m * C ESG Roof U v a l u e : 6.45 W/sq m * C ESG N wall U v a l u e : 6.321 W/sq m * C ESG Airchanges: I n f i l t r a t i o n losses: ESG B o i l e r E f f i c i e n c y ESG r a d i a t i o n  loss:  i f reduceleak 1f polyonglass i f N w a l l i n s u l If Imeterlns 1.5 1.5 1.5 1.47 1,316.60 W/deg C ( s e n s i b l e heat only)  volumes/hr  i f rootzone i f stackheat i f IRheat i f computer i f heatstorage 0.7 0.7 0.7 0.7 0.7 i f IRheating i f Polyonglass i f Nwallinsul i f thrmlcurtn 1.000 1.000 1.000 1.000 f r a c t i o n of l i g h t  t r a n s m i t t e d with ener  CALCULATION OF NET ANNUAL HEATING LOAD OF ENERGY SAVING GREENHOUSE Jan DeltaT (day) deg C I DeltaT ( n i g h t ) C | DAYTIME LOSSES  14.71 17.27  Feb  Mar 12.44 16.04  Apr 10.35 14.70  6.02 12.17  May  Jun 3.17 9.16  0.40 6.26  Jul -2.09 4.53  2 3 4 5 1 -81,805 -147,096 -269,804 -59,785 109 0 r a d i a t i o n (MJ/mo) 103,196 96,968 96,812 65,695 1 10 0 roof (MJ/month) 3,678 3,456 2,341 3,450 1 1 10 North wal1 24,895 23,393 23,355 15,849 1 12 0 E,W,S walIs 5,837 6, 222 5,846 3,961 1 13 0 perimeter 19,086 17,934 17,906 12,150 1 14 O l n f i 1 t r a t i o n ( s e n s ) 29,893 40,903 73,548 134,902 115 O i n f 1 t r a t i o n ( l a t n t ) 1 16 NIGHTTIME LOSSES 201,925 137,501 94,863 175,040 117 0 roof (MJ/month) 6,238 3,381 7, 196 4,900 118 Q North wal1 48,713 42,228 33,171 22,885 119 Q E,W,S walIs 120 0 p e r i m e t e r 12,174 10,553 5,719 8,290 32,374 17,545 121 O i n f i l t r a t i o n ( s e n s ) 37,347 25,431 122 Qi n f 1 t r a t i o n ( 1 a t n t ) 32,387 29,481 25,125 18,155 123 124 Monthly t o t a l s 402,609 162,549 466,926 308,230 125 232,212 126 Fuel Requ i red (MJ) 575,156 440,329 0 $940.24 $2,328.84 $1,782.92 127 Cost of Fuel $0.00 128 129 Q t o t a l MJ/sq m yr 2,115.48 130 131 Costs 9 $0.405/100MJ $8.57 per sq m $0.79 per sq f t 132 *********************** CROP YIELD ANALYSIS ***** * * * * * * * * * * * * * * * * * * 133 134 Annual Y i e l d (cases) $28,366.87 2,458.1 Crop Value: 135 P r i c e per case: $11.54 136 ****************** ECONOMIC ANALYSIS * * * * * * * * * * * * * * * * * * * * * * 137 138 E . $'s saved/year : $0.00 annual 1y $2, 519. 71 Loan Payment: 139 Change in S a l e s / y r : $0. 00 140 141 YEAR E $'s SAVED +/- SALES MA I NT LOAN BAL 142 $44 36 143 1984 $1,763 80 $0 00 $0 00 144 1 ,922 54 47 47 1984 $0 00 0 00 2,095 56 145 1984 50 79 $0 00 0 00 2,284 17 54 34 146 1984 0 00 $0 00 58 15 147 1984 2,489 74 0 00 $0 00 1984 62 22 148 0 00 2,713 82 $0 00 1984 2,958 06 66 57 149 0 00 $o 00 1984 3,224 29 71 24 0 00 150 $0 00 1984 3,514 47 76 22 151 0 00 $0 00 152 1984 81 56 0 00 3,830 77 $0 00 153 1984 4, 175 54 87 27 0 00 $0 00 154 1984 4,551 34 93 37 0 00 $0 00 155 1984 0 00 4,960 96 99 91 $0 00 156 1984 5,407 45 106 90 0 00 $0 00 157 1984 5,894 12 114 39 0 00 $0 00 158 159 160 ***************** LOOKUP TABLES BEGIN HERE ****************** 161 GLAZING TABLE GLAZING NUMBER U VALUE Short T 162  7 -379,153 4,989 178 1 ,203 301 923 189,577  6 -368, 124 38,300 1 ,365 9,240 2,309 7,084 184,062  8 0 0 0 0 0 0 O  39,036 1 , 391 9,417 2,354 7,220 9,539  30.014 1 .070 7,241 1,810 5.551 7 , 831  105,306  68,957  53,516  150,438 $609.13  98,510 $398.87  76,451 $309.56  60,690 2, 163 14,641 3,659 1 1 ,225 12,928  INTEREST $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0  PRINCIPAL 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00  $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0  CCA $104 93 84 75 68 61 55 49 44 40 36 32 29 26 23  00 00 00 00 00 00 00 00 00 00 00 00 00 00 00  Net Present  Value:  24 82 44 99 39 55 40 86 87 39 35 71 44 50 85 co o  163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216  1 2 3 4 5 6 7 8 9  1 .  2 Glass 3 mm double g l a s s polyethylene double poly poly + g l a s s fibrglas flat f i b r g l a s —>—•—•—< 1 in.styrfoam Double a c r y l  6 3 6 4 4 5 6 0 3  3 45 70 53 25 00 68 53 97 12  4 0.88 0. 75 0.89 0.76 0. 75 0. 78 0.78 0 0.85  U values a r e o v e r a l l heat l o s s (W/m2 O based on 25 km/hr o u t s i d e and no wind i n s i d e . Taken from Blom 1982. WEATHER TABLE  Average weather data f o r the Vancouver area.  MONTH  Number  January February March Apr i 1 May June Jul y August September October November December  1 2 3 4 5 6 7 8 9 10 1 1 12  Avg Low  Avg High  -0.270 0.960 2 . 300 4.830 7 .840 10.740 12.470 12.430 9 . 900 6.460 2 . 900 1 . 240  5 . 290 7 .560 9 .650 13 .980 16 830 19 600 22 090 21 .740 18 . 470 13 740 9 060 6 610  1983 Rad1at i on scj m 3 . 182 4.354 7.829 14.360 19.593 20.180 22.817 16.663 10.550 6 .699 3.978 2 . 345 MO/  Relat i ve Humidi t y 80% 77% 71% 74% 73% 72% 73% 75% 79% 81% 82% 84%  Values i n greenhouse a i rchanges/hr •- AIRCHANGE TABLE --AGEcode Airchange range Low new g l a s s 0 0.75 0 75 old glass 1 2 00 2 .00 new doublepoly 2 0.80 0 20 o l d doublepoly 3 0 50 1 .50 ESM TABLE  Adjustment f a c t o r s f o r ESM used: f r a c t i o n of 1 or  ESM  er  Root Zone Heating Stack Heat Recovery I.R. Heat i ng Computer Control Reduced Leakage Poly on G l a s s N Wal1 I n s u l a t ion 1 meter I n s u l a t i o n Thermal C u r t a i n s Heat Storage  1 2 3 4 5 6 7 8 9 10 12 TABLE --  TYPEcode  TYPE  |  Uwa11va1 1 1 1 1 0 0 1 0 0 1 0  Uroofval  00 00 00 00 98 68 00 74 47 00 00 -- ($/MJ) COST $/MJ  1 .00 1 .00 1 .00 1 .00 0.98 0.68 1 .00 1 .00 0.47 1 .00 0.00 1983  $/m2 Rtrans  |  1 .00 1 .00 0.95 1 .00 1 .00 0. 86 0.95 0.93 0.96 1 .00 0.00  A i rchange 1 1 1 1 0 0 0 0 0 1 0  00 00 00 00 50 30 93 98 90 00 00  -Per square me Inst Cost $19 $12,000 $2 1 $20,000 $3 $10 $3 $3 $12 $17 $0  OO 00 00 00 00 00 00 00 00 00 00  1 217 218 219 220 221 222 223 224 225 22G 227  1 2 3  2 Oil N a t u r a l Gas Electricity  CROP DATA TABLE NUMBER 1 2 3 4  From R i c k  CROP Cucumber Tomato  3 0.00817132 0.00404907 0.014598  @ @ @  4 1.45 $ / i m p . g a l .405 $ / t h e r m .05 $/KWhr  5  W a l l a c e ( B . C . H o t h o u s e P r o d u c t s ) 1982 YIELD CASE/SO M MKT PRICE/CASE 4.12 $10.00 2.45 $11.53  00  9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54  10  11  12  13  14  S C R A T C H P A D Instalcost Maintenance EENDATA * * * * * * * * * * * * * G l a z i n g type 1 Fuel type : 2 R o o f G l a z i ng: 1 INSULATION Perimeter : no Age  1 2 3 4 5 6 7 8 9 10  35  Y e a r bu i 1 1 InterestRate: Inflation : Escalation : Tax Rate: L o a n Amount: LoanIntRate: rootzone stackheat IRheat computer reducedleak polyonglass Nwal1insul onemeterinsul thermalcurta i heatstorage  Aug  1949 0. 18 0.07 0.09 0. 30 $0.00 0. 15 0 0 0 0 1 0 0 1 0 0  Sep  $0 .00 0 .00 0 .00 0 .00 3,009 .96 0 .00 0 .00 464 .82 0.,00 0..00 TOTALS:  Oct  Nov  $3,474.. 78  Dec  Year  15  Yld factor  $0 .00 0 .00 0 .00 0 .00 40 . 13 0 .00 O..00 23 , 24 0..00 0..00  0..000 0..000 0,.000 0..000 0..000 0..000 0..000 0..000 0..000 0..000  $63 . 37  0..000  total  10 55 --. 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76  14 21 . 74 12.43 -1 .74 4 . 57 2.93 0 0 0 0 0 0 0 35,622 1 ,295 1 1 ,850 2, 148 13,446 17,606  81,967 78 79 117,096 80 $474 . 13 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 g y c o n s e r v a t i o n 101 102 103 104 Aug 105 106 -1 .74 107 4.57 108  12  11  13  14  12 18.47 9.90 1 .53 7 . 10 3 . 76  10 13.74 6.46 6.26 10.54 5.08  9 9.06 2.90 10.94 14.10 6.29  -198,219 14,311 520 4,761 863 5,402 99, 1 10  -125,865 48,796 1 ,774 16,232 2,942 18,418 62,932  -74,741 76,748 2,791 25,531 4,627 28,969 37,370  -44,059 83,498 '3,036 27,776 5,034 31,517 22,030  -1,748,652 629,311 22,884 209,346 37,942 237,537 874,326  115,020 4 , 183 38,263 6,935 43,415 42,734  164,860 5,995 54,842 9,940 62,228 56,743  196,554 7, 147 65,386 11,850 74,191 . 64,471  1,317,539 47,910 438,292 79,436 497,313 485,110  147,127  275,779  455,903  548,431  3,513,180  210,181 $851.04  393,970 $1,595.21  0 $0.00  0 $0.00  2,744,800 $11,113.89  66,412 2,415 22,093 4,004 25,068 27,136  Sep  Oct 1 . 53 7 . 10  6.26 10. 54  Nov 10.94 14. 10  8 6.61 1 . 24 13.39 15 . 76 6.70  Dec 13.39 15.76  Year  total  11 9 10 109 0 -198,219 -125,865 14,311 48,796 1 10 0 1 1 1 0 510 1 ,739 112 3,453 0 1 1 ,772 1 13 863 2,942 0 1 14 2,647 0 9,025 1 15 0 99, 1 10 62,932 1 16 1 17 35,622 66,412 115,020 1 18 1 ,269 2, 367 4 ,099 119 8, 594 16,022 27,748 120 2, 148 4,004 6,935 121 6, 588 12,283 21,273 122 8,627 13,297 20,939 123 124 62,849 114,384 207,355 125 126 89,784 163,405 296,222 127 $363 . 54 $661.64 $1 ,199.42 128 129 130 131 132 133 134 135 136 137 138 139 140 141 CASHFLOW DISCOUNTED YEAR NUMBER 142 I[$3,474 . 7 8 ) ( $ 3 , 4 7 4 . 7 8 ) 143 $ 1 , 8 2 3 .68 $1,823.68 1 144 $1,968 .89 1,968.89 Break-Even 145 $ 2 , 1 2 9 .21 2,129.21 3 146 $2,305 .81 2,305.81 4 147 $ 2 , 4 9 9 .99 2,499.99 5 148 $ 2 , 7 1 3 . 15 2,713. 15 6 149 $2,946 .89 2,946.89 7 150 $3,202 .91 3,202.91 8 151 $ 3 , 4 8 3 . 12 3,483. 12 9 152 $ 3 , 7 8 9 .60 3,789.60 10 153 $4,124 .63 4,124.63 1 1 154 12 $ 4 , 4 9 0 .68 4,490.68 155 4,890.49 13 $ 4 , 8 9 0 . 49 156 $5,327 .04 5,327.04 14 157 $ 5 , 8 0 3 . 58 5,803.58 15 158 brk even y r : 2 159 $12,681 .21 160 161 162  12 -74,741 76,748 2,735 18,515 4,627 14,195 37,370  13 -44,059 83,498 2,976 20,143 5,034 15,443 22,030  14 -1 ,748,652 629,311 22,426 151,818 37,942 116,393 874,326  164,860 5,875 39,772 9,940 30,492 27,804  196,554 7,004 47,418 1 1 ,850 36,353 31 ,591  1 ,317,539 46,952 317,850 79,436 243,683 237,704  358,192  435,836  2,746,708  0 $0.00  0 $0.00  2,122,507 $8,594. 18  9 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 t e r 202 M a i n t e n a n c e 203 -204 $0,. 20 205 $100 .00 206 $0,. 13 207 $200..00 208 $0,.04 $2..00 209 $0.. 15 210 211 $0 . 15 212 $0,. 12 213 $50..00 214 $0..00 215 216  10  Change i n Y i e l d (%) 0..020 0..000 -0..030 0..050 0,,000 -0.. 100 -0..005 0..000 -0..010 0,,000 0..000  11  Heating Efficiency 1 . 15 1 . 12 1 .25 1 . 12  1 .25  CO  CM  O  r-oooO-^CMCO'a-miDr' - i - ' - C M C M C M C M C M C M C M C N CMCMCMCMCMCMCMCMCMCMCM  APPENDIX D: GREENSIM EQUATIONS  j  n * * * * * * * * * * * * * * * * * * * * * * * * GREENHOUSE EC ONOMIC ANALYSIS * * * * * * * * * * * * * * * * * * * * " " C o p y w r i t e d by B a r r y 1 S e p t 1984"  Shel "  "GETTING AROUND: T o s e e a p a r t o f t h e p r o g r a m t y p e ( G ) o (N)ame a n d 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  *GREENSIM*  v e r s i o n 1.1'  4  "(E)nergy balance" "(F)inancial analysis" "(L)ookup tables"  7 8  "For example t o see t h e F i n a n c i a l a n a l y s i s , you would type: G N F <return>"  9 10 "COMPUTER GREENHOUSE ENERGY SAVING L A T I O N FOR:" 11 "** ENERGY SAVING MEASURES **"  SIMU "Area  a n d Age  :"  12  "rootzone"  greendata.rootzone  "Glazing  Made Of : "  13  "stackheat"  greendata.stackheat  "Glazing  on Roof:  14  "IRheat"  greendata.IRheat  "Fuel  Type  15  "computer"  greendata.computer  "Crop  Grown  16  "reducedleak"  greendata.reducedleak  "Interest  17 " p o l y o n g l a s s "  greendata.polyonglass  "Fuel  18 " N w a l l i n s u l "  greendata.Nwal1insul  ' Inf1 a t i o n Rate  19  greendata.onemeterinsu1  "Tax R a t e  20 " t h e r m a l c u r t a i n s "  greendata.thermal c u r t a i n s  "Loan  Amount  21  greendata.heatstorage  "Loan  Interest  "onemeterinsul"  "heatstorage"  22 23  "** INDICATORS  AND  RESULTS  24  "Annual  25 26  " C h a n g e i n S a l e s / y r :" "Annual Energy Saving:"  Loan Payment:"  Used  Rate  escalation:  : "  "CropPrice $/case" "Yield (cases/yr)"  **' 1oanpayment  "Installed  Cost  : "  deltaSales y e a r 1 y s a v i ng  "Maintenance $/yr " F u e l C o s t $/MJ :"  00  27 28 29  1 NPV "Net P r e s e n t V a l u e :" "Break-Even Year : " breakeven " H e a t i n g f r o m month "&FIXED(startmonth, 0 ) & " t o month " & F I X E D ( e n d m o n t h , 0 ) & " : "  "HEATLOSS SUMMARY" "Heat C o s t $ / f t 2 " "Tot L o s s e s Mu/m2"  30 31 32  "CALC OF AREAS E T C " "Surface area."  33  "Volume:"  34  "Per i m e t e r : "  35  "F1oor  35  "N wal1 a r e a :  37 38 39  "Roof  "INTERMEDIATE  40  "  41  "Wal 1 U v a l u e : "  LOOKUP(g1az i n g , U v a 1 u e )  "W/sq  42  "Roof  "W/sq m * C"  43 44  LOOKUP(roofglazing,Uvalue )  "Greenhouse Age:"  IF(age=0,"New","01d")  45  "A i r c h a n g e s : "  0.1*leakiness*L00KUP((age +IF(0R(glaz=2,glaz=4,glaz =5 ) , 2 , 0 ) ) , a i rchangerange) +L00KUP((age+IF(0R(g1az=2 ,glaz = 4,glaz = 5),2,0)),ai r  IF(((currentyear)-yearbu i1t)>9, 1,0) "volumes/hour"  (houselength*wa11 h e i g h t * 2 +housewidth*wallheight*1. 1*2+houselength*housewidt h*1 . 1 ) * I F ( L E N ( u n i t s ) = 4 , 0 . 0929,1) ( h o u s e w i d t h * w a 1 1 h e i g h t * 1. 1*houselength)*IF(LEN(uni ts)=4,0.0283,1) (2*housewidth+2*houseleng th)*IF(LEN(units)=4,0.305 ,1 ) housewidth*houselength*IF (LEN(units)=4,0.0929,1) (IF(orientation=1,housewi dth*wal1 h e i g h t , h o u s e l e n g t h*wallheight))*IF(LEN(uni ts)=4,0.0929,1)  area:'  area: "  1.1*R[-2]C  50 51 52  "cubic  meters'  meters"  "meters"  "square  meters'*  "square  meters"  "square  meters"  CALCS" "FOR  46 47 48 49  "square  U value: "  REFERENCE  GREENHOUSE  m * C"  changelow) "Infiltration  losses:  "Vapour D e n s i t y  0.333*volume*airchanges  Calc:  "Tetens  " i n Nightime" "CALCULATION OF NET ANNUAL HEATING OF REFERENCE GREENHOUSE"  "Vap LOAD  Vap* ="  inside  "W/deg C ( s e n s i b l e  heat  only)"  (1322/(nighttemp+273.2))*(10a(( n i g h t t e m p * 7 . 5 ) / ( n i ghttemp+237 . 3 ))) 0.77*vapstarInNight  o  1  2  3  53  "  I"  54 55  "  56  "Avg D a y l i g h t  57  " O u t s i d e Day Temp C |"  58  "Outside Nite  59 60 61  "DeltaT (day) deg C "DeltaT (night) C "DeltaVap (Nite)g/M3  62 63  "DAYTIME L O S S E S " "0 r a d i a t i o n (MJ/mo)|"  I"  Hours  Temp  (MJ/month)  C|" " "  64  "0 r o o f  |"  65  "Q N o r t h  wal1  |  66  "0 E.W,S  walIs  | "  67  "0 p e r i m e t e r  68  "Oinfi1tration(sens)|"  69  "Oinf1tration(latnt)|"  70 71  "NIGHTTIME L O S S E S " "0 r o o f ( M J / m o n t h )  | "  |"  •Feb"  "Jan"  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,humiditytable)*((132 2/(R[-3]C+273.2))*(10a((R t-3]C*7.5)/(R[-3]C+237.3)  10 L00KUP(C0LUMN()-1.Hightemp) L00KUP(C0LUMN()-1,Lowtemp) daytemp-R[-2]C nighttemp-R[-2]C vapInNight-(L00KUP(C0LUMN()-1,h umiditytable)*((1322/(R[-3]C+27 3.2) ) * ( 1 0 a ( ( R [ - 3 ] C * 7 . 5 ) / ( R [ - 3 ] C +237.3)))))  ))))  IF(R[-4]C<0,0,-floorArea* L00KUP(C0LUMN()-1.Monthly Radiat ion)*LOOKUP(roofgla zing,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 t-9]C/24)) IF(R[-7]C<0,0,(surfaceare a-(Northwal1area+roofarea ))*Uwalls*R[-7]C*(2.628*R [-10]C/24)) IF(R[-8]C<0,0,perimeter*I F(LEN(perimeterinsul)=3,1 .39,2.77)*R[-8]C*2.628*R[ -11JC/24) IF(R[-9]C<0,0,OinfiItrati on*R[-9]C*(2.628*R[-12]C/ 24)) IF(R[-10]C<0,0,floorArea* L00KUP(C0LUMN()-1.Monthly Radiat1on)*L00KUP(roofgla z i ng,Shortwavetrans)*0.5* 0.7*30.4)  IF(R[-4]C<0,0,-floorArea*LOOKUP (COLUMN()-1,MonthlyRadiation)*L 00KUP(roofglaz i ng.Shortwavetran )*0.7*30.4) s  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 t h w a l 1 a r e a + r o o f a r e a ) ) * U w a l 1 s*R[ -7]C*(2.628*R[-10]C/24)) IF(R[-8]C<0,0,perimeter*IF(LEN( 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) 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 00KUP(roofglazi ng,Shortwavetran s)*0.5*0.7*30.4)  I F ( R [ - 11]C<0,0,roofarea*U IF(R[-11]C<0,0,roofarea*Uroof*R roof*R[-11]C*(2.628*(24-R [-11 ] C * ( 2 . 6 2 8 * ( 2 4 - R [ - 1 5 ] C ) / 2 4 ) ) [-15]C)/24))  72  "Q N o r t h wal1  1 | "  73  "Q  | "  74  "0 p e r i m e t e r  75  "Qinfi1tration(sens)|"  76  "Qinf1tration(latnt)|"  E,W,S  walIs  | "  77  n  78  "Monthly  79 80  "Fuel  Required  "Cost  of Fuel  81 82 83 84 85 86 87  "Qtotal  totals  MJ/sq  (MJ)  |" m yr"  CALC  88 89  "ESG  E, W,&  S wall  90  "ESG  Roof  value:  91  "ESG  N wal1  FOR  n  U  U  IF(R[-15]C<0,0,(3600*24*30.4167 *((24-R[-20]C)/24))*2.45*(airch anges/3600)*volume*(R[-15]C/100 0))  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))  I F ( - R [ - 15]C>SUM(R[- 1 4 ] C : R [ - 9 ] C ) , S U M ( R [ - 7 ] C : R [ - 2 ] C ) \ S U M ( R [ - 15]C :R[-2]C))  IF(AND(C0LUMN()>=startmon th+1,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth+1),R[ -2]C/0.7,0) R[-1]C*fuelcost  IF(AND(C0LUMN()> = s t a r t m o n t h + 1 ,C 0LUMN()<=endmonth+1,C0LUMN()<>o ffmonth+1),R[-2]C/0.7,0)  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 ( 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,Q1nf i 1 t r a t i o n * R [ -15]C*2.628*(24-R[-19]C)/24)  R[-1 ] C * f u e l c o s t  R[-3]C[+12]/floorArea  " C o s t s @ $0.405/100MJ:" " "INTERMEDIATE EENHOUSE " "  92 93  |"  2 IF(R(-12]C<0,0,Northwalla r e a * U w a l 1 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* 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[-14]C*2.628* (24-R[- 18]C)/24) IF(R[-15]C<0,0,Qinfi1trat 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))  U:"  value:  1  R[-2]C*fuelcost  "per s q  " i f reduce leak"  "  m"  II ENERGY SAVING  GR i f polyonglass"  IF(reducedleak=1,LOOKUP ( 5 IF(polyonglass=1,LOOKUP(6,ESMUw .ESMUwalIval)*Uwalls.Uwal a l 1 v a l )*RC[-1],RC[-1]) is) I F ( p o l y o n g l a s s = 1 , L 0 0 K U P ( 6 "W/sq m * C" ,ESMUroofval)*Uroof,Uroof ) I F ( N w a l 1 i n s u l = 1 , L 0 0 K U P ( 8 , "W/sq m * C" Uvalue),R[-2]C) 'if  reduceleak"  if  polyonglass"  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 r c h a n g e s r c h a n g e ) * R C [ - 1 ] , R C [ - 1 ] ) ,airchanges) O . 3 3 3 * v o l u m e * E S G a i r c h a n g e "W/deg C ( s e n s i b l e h e a t o n l y ) " s  94  "ESG  95  "Infiltration  96 97 98  "ESG  BoilerEfficiency:'  " i f rootzone" " i f stackheat" 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  radiation  " i f IRheating" IF(IRheat=1,LOOKUP(3,ESMR trans).1)  " i fPolyonglass" IF(polyonglass=1,LOOKUP(6,ESMRt r a n s ) * R C [ - 1],RC[-1])  "Jan"  "Feb"  daytemp-L00KUP(C0LUMN()-1 .Hightemp) nighttemp-L00KUP(C0LUMN( ) - 1 ,Lowtemp)  daytemp-LOOKUP(COLUMN()-1,Hight emp) nighttemp-LOOKUP(COLUMN()-1,Low temp)  IF(R[-3]C<0,0,-floorArea* L00KUP(C0LUMN()-1.Monthly Radiat ion)*LOOKUP(roofgla zing, Shortwavetrans)*0.7* 30.4*ESGradiat ionloss)  IF(R[-3]C<0.0,-floorArea*L00KUP (C0LUMN()-1,Month1yRadiation)*L 00KUP(roofglaz i ng,Shortwavetran s)*0.7*30.4*ESGradiationloss)  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,(surfaceare a-(Northwal1area+roofarea ) )*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) ) IF(R[-8]C<0,0,ESGOinfiItr ation*R[-8]C*(2.628*R[-58  IF(R[-4]C<0,0,roofarea*ESGUroof *R[-4]C*(2.628*R[-54]C/24))  101 102  Airchanges:  losses:"  loss:"  103  "CALCULATION OF NET ANNUAL HEATING OF ENERGY SAVING GREENHOUSE" "  104 105  "  106  "DeltaT  (day) deg C  107  "DeltaT  (night)  108 109  "DAYTIME LOSSES" "Q r a d i a t i o n (MJ/mo)  110  "0 r o o f  111  "0 N o r t h  wal1  112  "Q  walIs  113  "0 p e r i m e t e r  E.W.S  C  (MJ/month)  114 " Q i n f i 1 t r a t 1 o n ( s e n s )  LOAD  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 ion* R[-8]C*(2 .628*R[-58]C/24))  115  "Qinf1tration(latnt)|  116 117  "NIGHTTIME LOSSES" "0 r o o f ( M J / m o n t h )  118  "0  North  wal1  119  "0  E,W,S  walls  120  "0  perimeter  |"  I"  "Oinfi1tration(sens)|  122  "Oinf1tration(latnt)|  "-  124  "Monthly  125  totals  3 IF(R[-9]C<0,0,f1oorArea*LOOKUP( COLUMN()-1,MonthlyRadiation)*L0 OKUP(roofglazing,Shortwavetrans )*ESGradiationloss*0.7*30.4*0.5 )  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) )  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[-12]C<0,0,(surfacear ea-(Northwal1area+roofare a))*ESGUwal1s*IF(therma1c urtain=1,LOOKUP(9,ESMUwal 1val ) , 1 )*R[-12]C*(2.628*( 24-R[-63]C)/24))  121  123  2 IF(R[-9]C<0,0,floorArea*L 00KUP(C0LUMN()-1.MonthlyR adiation)*LOOKUP(roofglaz ing,Shortwavetrans)*ESGra diationloss*0.7*30.4*0.5)  |"  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 . 3 9 , 2 . 7 7 ) * R [ - 1 3 ] C * ( 2 . 6 2 8 [- 1 3 ] C * ( 2 . 6 2 8 * ( 2 4 - R [ - 6 4 ] C ) / 2 4 ) ) *(24-R[-64]C)/24)) IF(R[-14]C<0,0,ESGO i nf i11 r a t ion*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))  IF(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 rchanges/3600)*IF(thermalcurtai n=1,LOOKUP(9,ESMairchange),1)*v o1ume*(R[-61]C/1000))  I F ( - R [ - 1 5 ] C > S U M ( R [ - 1 4 ] C : R I F ( - R [ - 15 ]C>SUM(R[- 1 4 ] 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))  126  "Fuel  R e q u i r e d (MJ)  |"  127 128 129 130 131 132  "Cost  of Fuel  | "  133 134 135 136 137 138  "Qtotal "Costs @  2 IF(AND(COLUMN()>=startmon th+1,COLUMN()<=endmonth+1 ,COLUMN()<>offmonth),R[-2 ]C/heatingefficiency,0)  3 IF(AND(COLUMN() > = s t a r t m o n t h + 1 , C OLUMN()<=endmonth+1,COLUMN()<>o ffmonth),R[-2]C/heatingefficien cy,0)  R[-1]C*fuelcost  R[-1 ] C * f u e l c o s t  R [ - 3 ] C [ + -12]/f l o o r A r e a  MJ/sq m y r "  R[-2]C*fuelcost  $0.405/100MJ:"  n * * * * * * * * * * * * * * * * * * * * * * * CROP Y I E L D LYSIS ***********************"  "per  sq  m"  ANA  greendata.yield*f1oorArea "Annual Y i e l d ( c a s e s ) : " greendata.marketpr ice "Price per case:" . . * * * * * * * * * * * * * * * * * * ECONOMIC ANALYSIS *  "Crop V a l u e : "  ********************* I "E.  $'s  "Change  saved/year  :"  in Sales/yr:"  (refEcost-ESGEcost)  "Loan  Payment:"  yieldfactor*yearlySales  139 140 141 142 143  "YEAR"  "+/-  (currentyear)  (1-tax)*deltaSales  (1 - t a x ) * y e a r 1 y s a v i n g  144  (currentyear)  R[-1]C*(1+inflation)  R[-1]C*(1+esc)  145  (currentyear)  R[-1]C*(1+inflation)  R[-1]C*  1+esc)  146  (currentyear)  R[-1]C*(1+inflation)  R[-1]C*  1+esc)  147  (currentyear)  R[-1]C*(1+inflation)  R[-1 ]C*  1+esc)  148  (currentyear)  R[-1]C*(1 + i n f l a t i o n )  R[-1 ] C *  1+esc)  149  (currentyear)  R[-1 ] C * ( 1 + i n f l a t i o n )  R[-1 ] C *  1+esc)  150  (currentyear)  R[- 1]C*(1 + i n f l a t i o n )  R[-1 ]C*  1+esc)  151  (currentyear)  R[-1 ] C * ( 1 + i n f l a t i o n )  R[-1 ]C*  1+esc)  152  (currentyear)  R[-1 ] C * ( 1 + i n f l a t i o n )  R [ - 1 ]C*1+esc)  SALES"  "E $'s  SAVED"  153  (currentyear)  2 R[-1 ] C * ( 1 + i n f l a t i o n )  R[ -1 ] C * ( 1 + e s c )  154  (currentyear)  R[-1 ] C * ( 1 + i n f l a t i o n )  R[- 1 ]C*(1+esc)  155  (currentyear)  R[-1 ] C * ( 1 + i n f l a t i o n )  R[- 1]C*(1+esc)  156  (currentyear)  R[-1]C*(1+inflation)  R[-1 ] C * ( 1 + e s c )  157  (currentyear)  R [ - 1 ]C*(1 + inf1 a t i o n )  R[-1 ] C * ( 1 + e s c )  "NUMBER" "Glass 3 mm " "double g l a s s " "polyethy1ene" "double p o l y " "poly + glass" "fibrglas flat" "fibrglas ' "1 i n . s t y r f o a m " "Double a c r y l "  "U VALUE " 6 . 45 3.7 6.53 4.25 4 5.68 6.53 0.97 3. 12  1  158  159 160 161 162 163 164 165 166 167 168 169 170 171 172 173  "***************** LOOKUP TABLES BEGIN HERE * * * * * * * * * * * * * * * * * * * * "GLAZING T A B L E "GLAZING" 1 2 3 4 5 6 7 8 9  "U v a l u e s a r e o v e r a l l h e a t l o s s (W/m2 C ) b a s e d o n 25 km/hr w i n d " 174 " o u t s i d e a n d n o w i n d i n s i d e . Taken f r o m B l o m 1982. " 175 176 "WEATHER T A B L E Average weather data f or t h e Vancouver area." 177 » 178 179  "MONTH"  "Number"  "Avg Low"  180 181 182 183 184 185 186  "January "February "March "Apri1 "May "June "July  1 2 3 4 5 6 7  -0. 27 0.96 2. 3 4 .83 7.84 10. 74 12.47  187 188 189 190 191 192  "August "September "October "November "December  193 Values 194 195 196 197 198 199 200 201  202 203  "new "old "new "old  in  glass" glass" doublepoly" doublepoly"  " ESM TABLE s f o r ESM u s e d : "  1 2 3 4  "AGEcode" 0 1 2 3  "Airchange 0.75 2 0.8 1 .5  range"  Adjustment factor f r a c t i o n o f 1 o r $/m2"  "Number  'ESM  "  12.43 9.9 6.46 2.9 1 . 24  AIRCHANGE TABLE greenhouse a i r c h a n g e s / h r "  204 II R o o t Z o n e H e a t i n g " 205 II S t a c k H e a t R e c o v e r y " 206 11 I.R. H e a t i n g " 207 " C o m p u t e r C o n t r o l " 208 II R e d u c e d L e a k a g e " 209 " P o l y o n G l a s s " N Wal1 I n s u l a t i o n " 210 21 1 tl 1 m e t e r I n s u l a t i o n " Curtains" 212 It T h e r m a l 213 Heat S t o r a g e " 214 FUEL COST 215 " ($/Mu)" 216 11 217 1 218 2 219 3 220 CROP DATA TABLE 221 II 222 223 224 225 226 227  8 9 10 1 1 12  1 2 3 4 5 6 7 8 9 10 12  "Uwal1val"  98 68 74 47  TABLE  From  "TYPE " "Oil" " N a t u r a l Gas " E l e c t r i c i ty  "COST $/MJ" 0.00817132 0.00404907 0.014598  "CROP" "Cucumber" "Tomato"  "ck W a l l a c e ( B . C . H o t h o u s e u c t s ) 1982" "YIELD" "CASE/SO M" 4.12 2.45  Rick'  Prod  6  4  "(V)ariables" "Crop ( Y ) i e l d " " ( I ) n s t r u c t ions"  9 10 [ g r e e n d a t a 11 12  [greendata  name]  FIXED(floorArea,2)8."  currentyear]  m2"  F I X E D ( g r e e n d a t a . a g e , 0 ) &" years o l d " L 0 0 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 )  " * * * * * * * * * VARIABLES TRAN SFERRED FROM GREENDATA ** ************  13  LOOKUP(roofglazing,glazingt able)  greendata.roofglazing  Uni t s o f l e n g t h : "  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  Houselength  greendata.crop  Housew i d t h  "Night  Wal1 h e i g h t  15  L00KUP(crop,croptable)  16  greendata.int  17  greendata.esc  18  greendata.inf1 ation  19  greendata.tax  20  greendata.1oan  21  greendata.LoanlntRate  22  greendata.marketprice  23  cropyield.  24  instalcost .  25 26  maintcost. fuelcost.  Temp."  greendata.nighttemp  Day  "Day  Night  Temp."  temp. C  temp. C  greendata.daytemp  "ADJUSTMENTS"  Leakiness e s t .  7.  Or i e n t a t i on  Light'  -(1-ESGradiationloss) "% F u e l " -(heatingefficiency-0.7)/ 0.7 "% Y i e l d " yieldfactor  Fuel  Cost  $/MJ :"  "  Crop Type  :"  "  Marketprice Yield  :" :"  5  4 27 28 29  "Old House" refUnitEcost Qref  "With E Save ESGUni t E c o s t Ototal  "offmonth=  IF(startmonth-endmonth>0, startmonth-1,0)  6 Startmonth Endmonth  7  30 31 32  33  34  35 36  37 38 39 40 41 42 43 44  "  45  roofglazing  46 47 48 49  "g/cubic  M"  "g/cubic  M"  50 51 52  0=new  1=old"  vo «3  53  54 55  56 57  "Mar"  "Apr  1  12 14 LOOKUP(COLUMN()-1.Hightemp) LOOKUP(C0LUMN()-1.Hightem P) 58 LOOKUP(COLUMN()-1,Lowtemp) L00KUP(C0LUMN()-1,Lowtemp ) 59 d a y t e m p - R [ - 2 ] C daytemp-R[-2]C 60 n i g h t t e m p - R [ - 2 ] C n i g h t t e m p - R [ - 2 ]C 61 v a p I n N i g h t - ( L O O K U P ( C O L U M N ( ) v a p I n N i g h t - ( L O O K U P ( C O L U M N - 1 , h u m i d i t y t a b l e ) * ( ( 1322/(R ( ) - 1 , h u m i d i t y t a b l e ) * ( ( 1 3 2 [-3]C+273.2))*(10a((R[-3]C* 2/(R[-3]C+273.2))*(10a((R 7.5)/(R[-3]C+237.3))))) [-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 I F ( R [ - 4 ] C < 0 , 0 , - f l o o r A r e a * 0KUP(C0LUMN()-1,MonthlyRadi L00KUP(C0LUMN()-1.Monthly a t i o n ) * L O O K U P ( r o o f g l a z i ng,S 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,Shortwavetrans)*0.7* nortwavetrans)*0.7*30.4) 30.4) 64 I F ( R [ - 5 ] C < 0 , 0 , r o o f a r e a * U r o o I F ( R [ - 5 ] C < 0 , 0 , r o o f a r e a * U r f * R [ - 5 ] C * ( 2 . 6 2 8 * R [ - 8 ] C / 2 4 ) ) 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 e a I F ( R [ - 6 ] C < 0 , 0 , N o r t h w a l l a r *Uwalls*R[-6]C*(2.628*R[-9] ea*Uwal1s*R[-6]C*(2.628*R C/24)) t-9]C/24)) 66 I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e a - I F ( R [ - 7 ] C < 0 , 0 , ( s u r f a c e a r e (Northwal1area+roofarea))*U a-(Northwal1area+roofarea walls*R[-7]C*(2.628*R[-10]C ) )*Uwalls*R[-7]C*(2.628*R /24)) [-10]C/24)) 67 I F ( R [ - 8 ] C < 0 , 0 , p e r i m e t e r * I F ( I F ( R [ - 8 ] C < 0 , 0 , p e r i m e t e r * I L E N ( p e r i m e t e r i n s u l ) = 3 , 1 . 3 9 , F ( L E N ( p e r i m e t e r 1 n s u 1 ) = 3,1 2.77)*R[-8]C*2.628*R[-11]C/ .39,2.77)*R[-8]C*2.628*R[ 24) -11]C/24) 68 I F ( R [ - 9 ] C < O , O , 0 i n f i l t r a t i o n I F ( R [ - 9 ] C < O , O , 0 i n f i l t r a t i *R[-9]C*(2.628*R[-12]C/24) ) 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 * L 0 I F ( R [ - 1 0 ] C < 0 , 0 , f l o o r A r e a * 0KUP(C0LUMN()-1.MonthlyRadi L00KUP(C0LUMN()-1.Monthly a t 1 o n ) * L O O K U P ( r o o f g l a z i ng,S Rad i a t i o n ) * L O O K U P ( r o o f g 1 a hortwavetrans)*0.5*0.7*30.4 z i ng,Shortwavetrans)*0.5* ) 0.7*30.4) 70 71 IF(R[-11]C<0,0,roofarea*Uro IF(R[-11]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R of*R[-11]C*(2.628*(24-R[-15 [-15]C)/24)) ]C)/24))  "May"  "dun"  15.5 LOOKUP(C0LUMN()-1.Hightem P) LOOKUP(C0LUMN()-1.Lowtemp ) daytemp-R[-2]C nighttemp-R[-2]C vapInNight-(LOOKUP(COLUMN ()-1,humiditytable)*((132 2/(R[-3]C+273.2))*(1Oct((R [-3]C*7.5)/(R[-3]C+237.3)  16 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,humiditytable)*((132 2/(R[-3]C+273.2))*(10a((R [-3]C*7.5)/(R[-3]C+237.3)  ))))  ))))  IF(R[-4]C<0,0,-floorArea* L00KUP(C0LUMN()-1.Monthly Radi a t i o n ) * L O O K U P ( r o o f g l a 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)) [-91C/24)) IF(R[-7]C<0,0,(surfaceare IF(R[-7]C<0,0,(surfaceare a-(Northwal1area+roofarea a-(Northwa11area+roofarea ))*Uwalls*R[-7]C*(2.628*R ))*Uwalls*R[-7]C*(2.628*R [-10]C/24)) t-10]C/24)) IF(R[-8]C<0,0,perimeter*I 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 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[ .39,2.77)*R[-8]C*2.628*R[ -11 ]C/24) -11 ] C / 2 4 ) IF(R[-9]C<0,0,Qinfiltrati IF(R[-9]C<0,0,Oinfi1trati on*R[-9]C*(2.628*R[-12]C/ on*R[-9]C*(2.628*R[-12]C/ 24)) 24)) IF(R[-10]C<0,0,floorArea* IF(R[-10]C<0,0,floorArea* L00KUP(C0LUMN()-1.Monthly L00KUP(C0LUMN()-1.Monthly Rad1 a t i o n ) * L O O K U P ( r o o f g l a Rad i a t i o n ) * L O O K U P ( r o o f g l a z i ng,Shortwavetrans)*0.5* z i ng,Shortwavetrans)*0.5* 0.7*30.4) 0.7*30.4) IF(R[-4]C<0,0,-floorArea* 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  IF(R[-11]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R [-15]C)/24))  I F ( R [ - 11]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R [-15]C)/24))  IF(R[-12]C<0,0,Northwalla r e a * U w a l 1 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,peri meter* IF(LEN(perimeterinsul)=3, 1.39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) IF(R[-15]C<O,O,0infiltrat 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))  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) IF(R[-15]C<0,0,Oinfiltrat 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) ) * 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* IF(LEN(perimeterinsul)=3, 1 .39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) IF(Rt- 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 . 4 5 * ( a i r c h a n g e s / 3 6 0 0 ) * v olume*(R[-15]C/1000))  I F ( - R [ - 1 5 ] O S U M ( R [ - 14]C:R [-9]C),SUM(R[-7]C:R[-2]C) ,SUM(R[- 1 5 ] C : R [ - 2 ] C ) )  I F ( - R [ - 1 5 ] O S U M ( R [ - 14]C:R [-9 ] C ) , S U M ( R [ - 7 ] C : R [ - 2 ] C ) ,SUM(R[- 1 5 ] C : R [ - 2 ] C ) )  I F ( - R [ - 15]C>SUM(R[- 14]C:R [-9]C) , S U M ( R [ - 7 ] C : R [ - 2 ] C ) , SUM(R[- 1 5 ] C : R [ - 2 ] C ) )  IF(AND(C0LUMN()>=startmon IF(AND(C0LUMN()>=startmonth +1,C0LUMN()<=endmonth+1,C0L th+1,C0LUMN()<=endmonth+1 U M N ( ) < > o f f m o n t h + 1 ) , R [ - 2 ] C / 0 ,C0LUMN()<>offmonth+1),R[ .7.0) -2]C/0.7,0) R[-1 ] C * f u e l c o s t Rt-1]C*fuelcost  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  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[-16]C)/24)) 73 I F ( R [ - 1 3 ] C < 0 , 0 . ( s u r f a c e a r e a - (Northwal1area+roofarea))* 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 imeteri 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*(ai rchanges/3600)*volume* (R[-15]C/1000)) 77  78  79 80  81 82 83 84 85 86  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))  RC[-2]/10.8  "per  sq f t "  87 88  " i f Imeterinsul"  89  IF(onemeterinsul=1,L00KUP(8 ,ESMUwal1val)*RC[-1],RC[-1 } )  "W/sq  " i f Nwal1insul"  "  m  * C"  90  91 92 93  i f Imeterlns"  94  4 IF(Nwal1insul=1,L00KUP(7,ES Mairchange)*RC[-1],RC[-1 ])  5 IF(onemeter1nsu1=1,LOOKUP (8,ESMa i r c h a n g e ) * R C [ - 1],R C[-1])  " volumes/hr"  95 96 97 98  99 100  " i f IRheat" " i f computer" " i f heatstorage" 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 ] , R C [ - 1 ] ) M h e a t e f f i c i e n c y ) * R C [ - 1 ],R 0, E S M h e a t e f f i c i e n c y ) * R C [ C[-1]) 1].RC[-1]) "if Nwallinsul" " i f thrmlcurtn" I F ( N w a l 1 i n s u l = 1,L00KUP(7,ES I F ( t h e r m a 1 c u r t a i n = 1 , L O O K U " f r a c t i o n o f l i g h t t r a n s m M R t r a n s ) * R C [ - 1 ],RC[-1 ] ) P ( 9 , E S M R t r a n s ) * R C [ - 1],RC[ i t t e d w i t h e n e r g y c o n s e r v -1]) ation"  101 102 103  104 105  106 107 108 109  110  111  112  113  114  "Mar"  "Apr"  "May"  " Jun"  d a y t e m p - L 0 0 K U P ( C 0 L U M N ( ) - 1 , H daytemp-LOOKUP(COLUMN()-1 .Hightemp) ightemp) nighttemp-LO0KUP(COLUMN()-1 nighttemp-LOOKUP(COLUMN() -1,Lowtemp) , Lowtemp)  daytemp-LOOKUP(COLUMN()-1 .Hightemp) nighttemp-LOOKUP(COLUMN() -1,Lowtemp)  daytemp-L00KUP(C0LUMN( )-1 ,Hightemp) nighttemp-L00KUP(C0LUMN() - 1,Lowtemp)  IF(R[-3]C<0,0,-floorArea*LO OKUP(COLUMN()-1.MonthlyRadi ation)*LOOKUP(roofglazing,S hortwavetrans)*0.7*30.4*ESG rad1ationloss)  IF(R[-3]C<0,0,-floorArea* LOOKUP(COLUMN()-1.Monthly Radiation)*LOOKUP(roofgla zing,Shortwavetrans)*0.7* 30.4*ESGradiationloss)  IF(R[-3]C<0,0,-floorArea* L00KUP(C0LUMN()-1.Monthly Rad i a t i o n ) * L 0 0 K U P ( r o o f g l a z i ng,Shortwavetrans)*0.7* 30.4*ESGradiationloss)  IF(R[-3]C<0,0,-floorArea* L00KUP(C0LUMN()-1.Monthly Radi a t i o n ) * L O O K U P ( r o o f g l a zing,Shortwavetrans)*0.7* 30.4*ESGradi at i o n l o s s )  IF(R[-4]C<0,0,roofarea*ESGU roof*R[-4]C*(2.628*R[-54]C/ 24)) IF(R[-5]C<0,O,Northwallarea *ESGUnorthwal1*Rt-5]C*(2.62 8*R[-55]C/24) ) IF(R[-6]C<0,0,(surfacearea(Northwa11area+roofarea))*E SGUwalls*R[-6]C*(2.628*R[-5 6]C/24)) IF(R[-7]C<0,0,perimeter*IF( LEN(per imeter i nsul)=3,1.39, 2.77)*R[-7]C*(2.628*R[-57]C /24)) IF(R[-8]C<0,0,ESGQinfi1trat ion*R[-8]C*(2.628*R[-58]C/2 4))  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,(surfaceare a-(Northwal1area+roofarea ))*ESGUwalls*R[-6]C*(2.62 8*R[-56]C/24)) IF(R[-7]C<0,0,perimeter*I F(LEN(perimeterinsul)=3,1 .39,2.77)*R[-7]C*(2.628*R [-57]C/24)) IF(R[-8]C<0,0,ESGOinfi1tr ation*R[-8]C*(2.628*R[-58 ]C/24))  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,(surfaceare 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)) IF(R[-8]C<0,0,ESGQinfiltr ation*R[-8]C*(2.628*R[-58 ]C/24))  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,(surfaceare a-(Northwa11area+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 [-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 KUP(C0LUMN( ) - 1 . M o n t h l y 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,Sh ortwavetrans)*ESGradiationl o s s * 0 . 7 * 3 0 . 4 * 0 . 5)  IF(R[-9]C<0,0,f1oorArea*L 00KUP(C0LUMN()-1.MonthlyR a d i a t i on)*LOOKUP(roofglaz ing,Shortwavetrans)*ESGra diationloss*0.7*30.4*0.5)  IF(R[-9]C<0,0,f1oorArea*L IF(R[-9]C<0,0,floorArea*L 0 0 K U P ( C 0 L U M N ( ) - 1 , M o n t h l y R OOKUP(C0LUMN()-1.MonthlyR a d i a t i o n ) * L O O K U P ( r o o f g l az a d i a t i o n ) * L O O K U P ( r o o f g l a z ing,Shortwavetrans)*ESGra ing,Shortwavetrans)*ESGra diationloss*0.7*30.4*0.5)diationloss*0.7*30.4*0.5)  1 16 117 I F ( R [ - 1 0 ] C < 0 , 0 , r o o f a r e a * E S G I F ( R [ - 1 0 ] C < 0 , 0 , r o o f a r e a * E I F ( R [ - 1 0 ] C < 0 , 0 , r o o f a r e a * E I F ( R [ - 1 0 ] C < 0 , 0 , r o o f a r e a * E U r o o f * I F ( t h e r m a l c u r t a i n= 1 , L S G U r o o f * I F ( t h e r m a l c u r t a i n S G U r o o f * I F ( t h e r m a l c u r t a i n S G U r o o f * I F ( t h e r m a l c u r t a i n 0 0 K U P ( 9 , E S M U r o o f v a l ) , 1 ) * R [ - =1 , L O O K U P ( 9 , E S M U r o o f v a 1 ) , =1 , L O O K U P ( 9 , E S M U r o o f v a l ) , =1 , L O O K U P ( 9 , E S M U r o o f v a l ) , 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[)) 61]C)/24)) 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 rea*ESGUnorthwal1*IF(ther rea*ESGUnorthwa11*IF(ther rea*ESGUnorthwal1*IF( ther urtain=1,LOOKUP(9.ESMUwa11v malcurtain=1,LOOKUP(9,ESM malcurtain=1.L00KUP(9,ESM m a l c u r t a i n = 1 . L O O K U P ( 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 U w a l 1 v a 1 ) , 1 ) * R [ - 1 1 ] C * ( 2 . 6 Uwa11va1) , 1 ) * R [ - 1 1 ] 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  IF(R[-12]C<0,0,(surfacearea IF(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 e a - ( N o r t h w a l 1 a r e a + r o o f a r e e a - ( N o r t h w a l 1 a r e a + r o o f a r e ESGUwal 1 s * I F ( t h e r m a l c u r t a i n a ) ) * E S G U w a l 1 s * I F ( t h e r m a 1 c a ) ) * E S G U w a l 1 s * I F ( t h e r m a 1 c a) ) * E S G U w a l 1 s * I F ( t h e r m a l c = 1 , LOOKUP(9,ESMUwa11val ), 1 ) urtain=1,LOOKUP(9,ESMUwa1 urtain=1,LOOKUP(9,ESMUwa 1 u r t a i n = 1 , L O O K U P ( 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* IF(R[- 1 3]C<0.0,perimeter* ( L E N ( p e r i m e t e r i n s u 1 ) = 3,1.39 I F ( L E N ( p e r i m e t e r i n s u 1 ) = 3, 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 l )=3, ,2.77)*R[-13]C*(2.628*(24-R 1 . 39,2 . 77 ) * R [ - 13 ] C * ( 2 . 6 2 8 1 . 39 , 2 . 7 7 ) * R [ - 1 3 ] C * ( 2 . 6 2 8 1 . 39 , 2 . 7 7 ) * R [ - 13]C* ( 2.628 [-64]C)/24)) *(24-R[-64]C)/24)) *(24-R[-64]C)/24)) *(24-R[-64]C)/24))  121  IF(R[-14]C<0,0,ESG01nfiltra t ion*R[ - 14]C*IF(thermal c u r t ain=1 , LOOKUP(9,ESMairchange ), 1 ) * ( 2 . 6 2 8 * ( 2 4 - R [ - 6 5 ] C ) / 2 4 )) IF(R[-61]C<0,0,(3600*24*30. 4 1 6 7 * ( ( 2 4 - R [ - 6 6 ] C ) / 2 4 ) ) * 2 .4 5*(ESGairchanges/3600)*IF(t hernial c u r t a i n = 1 , L O O K U P ( 9 , E S Mai r c h a n g e ) , 1 ) * v o l u m e * ( R [ - 6 1]C/1000))  I F ( R [ - 14]C<0,0,ESGQinf111 r a t i o n * R [ - 14]C*IF(thermal curtain=1,LOOKUP(9,ESMa i r 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 ( t h e r m a l c u r t a i n=1,LOO KUP(9,ESMai r c h a n g e ) , 1 ) * v o lume*(R[-61]C/1000))  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))  I F ( - R [ - 15 ] O S U M ( R [ - 14]C:R I F ( - R [ - 15]C>SUM(R[- 14]C : R I F ( - R [ - 15]C>SUM(R[- 14]C:R t~9]C),.SUM(R[-7]C:R[-2]C) [-9]C) . S U M ( R [ - 7 ] C : R [ - 2 ] C ) [-9]C) ,SUM(R[-7]C:R[ - 2 ] C ) ,SUM(R[- 1 5 ] C : R [ - 2 ] C ) ) ,SUM(R[-15]C:R [ - 2 ] C ) ) , SUM(R[- 15]C:R[-2 ]C ) )  122  I F ( R [ - 1 4 ] C < 0 . 0 , E S G O i n f i 11 r a t i o n * R [ - 14]C*IF(therma1 curtain=1,LOOKUP(9,ESMair change),1 )*(2.628*(24-R[65]C)/24)) IF(R[-61]C<0,0,(3600*24*3 0 . 4 167*((24-R[-66]C)/24)) *2.45*(ESGai rchanges/3600 )* I F ( t h e r m a l c u r t a i n = 1 , L O O KUP(9,ESMa i r c h a n g e ) , 1 ) * v o 1ume*(R[-61]C/1000))  I F ( R [ - 14]C<0,0,ESGOinf1 I t r a t i o n * R [ - 14]C*IF(therma1 curtain=1,LOOKUP(9,ESMai r change),1 )*(2.628*(24-R[65]C)/24)) IF(R[-61]C<0,0.(3600*24*3 0. 4 1 6 7 * ( ( 2 4 - R t - 6 6 ] C ) / 2 4 ) ) *2.45*(ESGai rchanges/3600 )* I F ( t h e r m a 1 c u r t a i n = 1 , L O O KUP(9,ESMa i r c h a n g e ) , 1 ) * v o 1ume*(R[-61]C/1000))  123  124  125  126  4 IF(AND(COLUMN()>=startmonth +1,COLUMN()<=endmonth+1,COL UMN()oof fmonth),R[-2]C/hea 11ngeff i c i ency,0)  127 R [ - 1 ] C * f u e l c o s t 128 129 130 131 R C [ - 2 ] / 1 0 . 8 132  5 IF(AND(COLUMN()>=startmon th+1,COLUMN() < = endmonth+1 ,COLUMN( ) < > o f f m o n t h ) , R [ - 2 ]C/heat i ngeff 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 f m o n t h ) ,R[-2 , COLUMN ( ) o o f f m o n t h ) ,R[-2 ]C/heat1ngef f i c iency,0) ]C/heat i ngef f i c i e n c y . 0 )  R[-1]C*fuelcost  R[-1 ] C * f u e l c o s t  R[-1 ] C * f u e l c o s t  "INTEREST"  "PRINCIPAL"  "per  sq f t '  133 134 c r o p y i e l d * c a s e p r i c e 135 136 137 138  loan/((1-(1/(1+(LoanIntRate ))a15))/(LoanIntRate))  "annually"  139 140 141 142 143  "MAINT"  "LOAN BAL"  (1-tax)*maintcost  1 oan  144  R[-1]C*(1+inflatlon)  Rt-1 ] C - R [ - 1 ] C [ + 2 ]  145  R[-1]C*(1+inflation)  R[-1]C-Rt-1]C[+2]  146  R[-1]C*(1+inflation)  R[-1 ] C - R [ - 1 ] C [ + 2 ]  147  Rt-1]C*(1+inflation)  R [ - 1 ]C-R[- 1]C[+2]  148  R[-1]C*(1+inflation)  R[-1]C-R[ - 1 ]C[+2]  149  R[-1]C*(1+inflation)  R[-1 ] C - R [ - 1 ] C t + 2 ]  150  R[-1]C*(1+inflation)  R[-1 ] C - R [ - 1 ] C [ + 2 ]  151  R[-1]C*(1+inflation)  152  R[-1]C*(1+inflation)  tax)*(LoanIntRate)*RC[  1oanpayment x))  (RC[- 1 ] / ( 1 - t a  tax)*(LoanIntRate)*RC[  1oanpayment x))  (RC[- 1 ] / ( 1 - t a  tax)*(LoanIntRate)*RC[  1oanpayment •(RCt- 1 ] / ( 1 - t a x))  tax )*(LoanIntRate)*RC[  1oanpayment x))  tax)*(LoanIntRate)*RC[  1oanpayment •(RC[- l ] / ( 1 - t a x))  tax)*(LoanIntRate)*RC[  1oanpayment x))  (RC[- 1 ] / ( 1 - t a  tax)*(LoanIntRate)*RC[  1oanpayment x))  (RC[- 1 ] / ( 1 - t a  tax)*(LoanIntRate)*RC[  1oanpayment x))  (RC[- 1 ] / ( 1 - t a  R[-1]C-R[-1]Ct+2]  tax)*(LoanIntRate)*RC[  1oanpayment x))  (RC[- 1 ] / ( 1 - t a  R[-1]C-R[-1 ]C[+2]  tax)*(Loan!ntRate)*RC[  1oanpayment x))  (RC[- 1 ] / ( 1 - t a  (RC[- 1 ] / ( 1 - t a  o it.  153  4 R[-1]C*(1 + i n f l a t i o n )  5 R [ - 1 ] C - R [ - 1]C[  6 (1-tax)*(LoanIntRate)*RC[  7 1oanpayment-(RC[- 1 ] / ( 1 - t a x))  154  R[-1]C*(1 + i n f l a t i o n )  R [ - 1 ] C - R [ - 1 ]C[  (1 -1  tax)*(LoanIntRate)*RC[  1oanpayment-(RC[-1]/(1-ta x))  155  R[-1]C*(1 + i n f l a t i o n )  R [ - 1 ] C - R [ - 1]C[  (1 -1  tax)*(LoanIntRate)*RC[  1oanpayment-(RC[-1]/(1-ta x))  156  R[-1]C*(1+inflation)  R [ - 1 ] C - R [ - 1]C[  (1 -1  tax)*(LoanIntRate)*RC[  1oanpayment-(RC[- 1 ] / ( 1 - t a x))  157  R[- 1]C*(1 + i n f l a t i o n )  R [ - 1 ] C - R [ - 1 ]C[  (1 -1  tax)*(LoanIntRate)*RC[  1oanpayment-(RC[-1]/(1-ta x))  158  159  "Net  160 161 162 163 164 165 166 167 168 169 170 171 172 173  "Short 0. 88 0. 75 0. 89 0. 76 0. 75 0. 78 0. 78 0 0. 85  T"  174 175 176  1983  177  "Relative"  178 179  "Avg  180 181 182 183 184 185 186  5 .29 7 . 56 9. 65 13 .98 16 .83 19 .6 22 .09  High"  "Radiat ion " M J / s q m"  "Humidi t y "  3 . 182 4 . 354 7.829 14 . 36 19.593 20. 18 22.817  0.8 0.77 0.71 0.74 0. 73 0.72 0.73  Present  Value:"  4 187 188 189 190 191 192  21.74 18.47 13.74 9.06 6.61  0. 75 0.79 0.81 0.82 0.84  193 194 "Low" 195 0.75 196 2 197 0.2 198 0.5 199 200 201  202 203  "Uroofval"  204 205 206 207 208 209 210 211 212 213 214  1 1 1 1 0.98 0.68 1 1 0.47 1 0  215  "  1983"  "@ "@ "@  1.45 $/imp.ga1 .405 $ / t h e r m " .05 $/KWhr"  216 217 218 219 220 221 222 223 224 225 226 227  "MKT P R I C E / C A S E " 10 11.53  "Rtrans  1 1 0.95 1 1 0.86 0.95 0.99 0.96 1 0  |"  "Airchange"  1 1 1 1 0.5 0.3 0.93 0.98 0.9 1 0  8  10  1  3 4 5 6 7 8 9 10 1 1 12  13  [greendata  UNITS]  "Glazing  [greendata  glazing]  14  [greendata  houselength]  "Fuel  :"  [greendata  fuel type]  15  [greendata  housewidth]  "RoofGlazing:  [greendata  roofglazing]  16  [greendata  wallheight]  "INSULATION"  17  [greendata  daytemp]  "Per i meter  [greendata ul ]  perimeterins  18  [greendata  nighttemp]  "Age  ( c u r r e n t y e a r ) - y e a r b u i11  19  20  [greendata  21  [greendata  type  type  leakiness]  orientation]  "Year  built  [greendata  yearbuilt]  "InterestRate 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 :" able) 24 [ g r e e n d a t a c r o p ] "Escalation :  [greendata i n t ] [greendata inflation]  25 26  [greendata tax] [greendata loan]  22 23  [greendata [greendata  marketprice] yield]  "Tax R a t e : " " L o a n Amount:  [greendata esc]  9 "LoanlntRate:" "rootzone" "stackheat"  [greendata [greendata [greendata  10 LoanlntRate] rootzone] stackheat]  30 31 32  "IRheat" "computer" "reducedleak"  [greendata [greendata [greendata  IRheat] computer] reducedleak]  33  "polyonglass"  [greendata  polyonglass]  34  "Nwal1insul"  [greendata N w a l l i n s u l ]  35  "onemeterinsul"  [greendata 1 ] [greendata i ns ]  27 28 29  36  37 38 39 40 41 42 43 44 45  46 47 48 49  50 51 52  [greendata [greendata  8 startmonth] endmonth]  "thermalcurtains"  "heatstorage"  [greendata  onemeterinsu thermalcurta  heatstorage]  10  1 1  53  54 " J u l ' 55  "Aug"  "Sep"  56 57  15.5 L00KUP(C0LUMN()-1.Hightem P) L00KUP(C0LUMN()-1.Lowtemp ) d a y t e m p - 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) ))))  14 L00KUP(C0LUMN()-1.Hightem P) L 0 0 K U P ( C 0 L U M N ( ) - 1,Lowtemp ) daytemp-R[-2]C nighttemp-R[-2]C vapInNight-(LOOKUP(COLUMN ()-1,humiditytable)*((132 2/(R[-3]C+273.2))*(10a((R [-3]C*7.5)/(R[-3]C+237.3) ))))  12 10 L 0 0 K U P ( C 0 L U M N ( ) - 1 . M i g h t LOOKUP(C0LUMN()-1.Hightem emp) P) L00KUP(C0LUMN()-1,Lowte L00KUP(C0LUMN()-1,Lowtemp mp) ) daytemp-R[-2]C daytemp-R[-2]C nighttemp-R[-2]C nighttemp-R[-2]C vapInNight-(LOOKUP(COLU vapInNight-(LOOKUP(COLUMN MN()-1,humiditytable)*( ()-1.humiditytable)*((132 (1322/(R[-3]C+273.2))*( 2/(R[-3]C+273.2))*(10a((R 10a((R[-3]C*7.5)/(R[-3] [-3]C*7.5)/(R[-3]C+237.3) C+237.3))))) ))))  IF(R[-4]C<0,0,-floorArea* LOOKUP(COLUMN()-1.Monthly Radiation)*LOOKUP(roofgla zing,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)) IF(R[-7]C<0,0,(surfaceare a-(Northwal1area+roofarea ))*UwallS*R[-7]C*(2.628*R [-10JC/24)) IF(R[-8]C<0,0,perimeter*I F(LEN(perimeterinsul)=3,1 .39,2.77)*R[-8]C*2.628*R[ -1 1 J C / 2 4 ) IF(R[-9]C<0,0,01nfiItrati on*R[-9]C*(2.628*R[-12]C/ 24)) IF(R[-10]C<0,0,floorArea* L00KUP(C0LUMN()-1.Monthly Radiation)*LOOKUP(roofgla zing,Shortwavetrans)*0.5* 0.7*30.4)  IF(R[-4]C<0,0,-floorArea* 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]C/24)) IF(R[-7]C<0,0,(surfaceare a-(Northwal1area+roofarea ) )*Uwal1s*R[-7]C*(2.628*R [-10JC/24)) IF(R[-8]C<0,0,perimeter*I F(LEN(perimeterinsul)=3,1 .39,2.77)*R[-8]C*2.628*R[ -11]C/24) IF(R[-9]C<0,0,Q1nfiItrati on*R[-9]C*(2.628*R[-12]C/ 24)) IF(R[-10]C<0,0,floorArea* L00KUP(C0LUMN()-1.Monthly Rad i a t i o n ) * L O O K U P ( r o o f g 1 a z i ng,Shortwavetrans)*0.5* 0.7*30.4)  IF(R[-4]C<0,0,-floorAre 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 area))*Uwalls*R[-7]C*(2 .628*R[-10]C/24)) IF(R[-8]C<0,0,perimeter * IF(LEN(perimeterinsul) =3,1.39,2.77)*R[-8]C*2. 628*R[-11 ] C / 2 4 ) IF(R[-9]C<0,0,Oinfi1tra t1on*R[-9]C*(2.628*R[-1 2]C/24)) IF(R[-10]C<0,0.floorAre a*L00KUP(C0LUMN()-1,Mon thlyRadiation)*LOOKUP(r o o f g l a z i ng,Shortwavetra ns)*0.5*0.7*30.4)  58 59 60 61  62 63  64  65  66  67  68  69  70 71  IF(R[-11]C<0,0,roofarea*U IF(R[-1 1]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R roof*R[-11]C*(2.628*(24-R [-15]C)/24)) [-15]C)/24))  "Oct"  IF(R[-4]C<0,0,-floorArea* 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)) IF(R[-7]C<0,0,(surfaceare 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) IF(R[-9]C<0,0,QinfiItrati 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 I F ( R [ - 11]C<0,0,roofarea*U *Uroof*R[-11]C*(2.628*( roof*R[-11]C*(2.628*(24-R t-15]C)/24)) 24-R[-15]C)/24) )  72  73  74  75  76  8 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* IF(LEN(perimeterinsul)=3, 1.39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) IF(R[-15]C<0,0,Oinfi1trat ion*R[-15]C*2.628*(24-R[19]C)/24) IF(R[-15]C<0,0,(3600*24*3  10 IF(R[-12]C<0,0,Northwal larea*Uwalls*R[-12]C*(2 .628*(24-R[-16]C)/24)) IF(R[-13]C<0,0,(surface area-(Northwa1larea+roo farea))*Uwalls*R[-13]C* 2.628*(24-R[-17]C)/24) IF(R[-14]C<0,0,perimete r*IF(LEN(perimeterinsul )=3,1 . 3 9 , 2 . 7 7 ) * R [ - 1 4 ] C * 2.628*(24-R[-18]C)/24) IF(R[-15]C<0,0,OinfiItr 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 . 4 5 * ( a i r c h a n g e s / 3 600)*volume*(R[-15]C/10 00))  11 IF(R[-12]C<0,0,Northwalla r e a * U w a l 1 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(perimeterinsul)=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))  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))  I F ( - R [ - 15]C>SUM(R[- 14]C:R I F ( - R [ - 1 5 ] C > S U M ( R [ - 1 4 ] C [-9]C),SUM(R[-7]C:R[-2]C) :R[-9]C),SUM(R[-7]C:R[,SUM(R[- 15 ] C : R [ - 2 ] C ) ) 2]C),SUM(R[-15]C:R[-2]C ))  I F ( - R [ - 15 ]C>SUM(R[- 14]C:R [-9]C) , S U M ( R [ - 7 ] C : R [ - 2 ] C ) ,SUM(R[- 1 5 ] C : R [ - 2 ] C ) )  IF(AND(C0LUMN()>=startmon th+1,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth+1),R[ -2]C/0.7,0) R[-1]C*fuelcost  IF(AND(COLUMN()>=startmon th+1,C0LUMN()<=endmonth+1 ,C0LUMN()<>offmonth+1),R[ -23C/0.7.0) R[-1]C*fuelcost  9 IF(R[-12]C<0,0,Northwalla r e a * U w a l 1 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) I F ( R [ - 14]C<0,0,per i m e t e r * IF(LEN(perimeterinsul)=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 . 4 167* ( (24-R [ - 2 0 3 O / 2 4 ) ) 0 . 4 1 6 7 * ( ( 2 4 - R [ - 2 0 ] C ) / 2 4 ) ) * 2 . 4 5 * ( a i rchanges/3600)*v *2.45*(a 1rchanges/3600)*v olume*(R[-15]C/1000)) olume*(R[-15]C/1000))  77  78  79 80  81 82 83 84 85 86 87 88 89  90  91 92 93  IF(AND(COLUMN()>=startm IF(AND(C0LUMN()>=startmon onth+1,C0LUMN()< = endmon th+1 ,C0LUMN()< = endmonth+1 t h + 1 , C 0 L U M N ( ) o o f f m o n t h ,C0LUMN()<>offmonth+1),R[ +1),R[-2]C/0.7,0) -2]C/0.7,0) R[-1]C*fuelcost R[-1]C*fuelcost  11  10 94  95 96 97 98  99 100  101 102 103  104 " J u l 105  "Aug"  "Sep"  "Oct"  daytemp-LOOKUP(C0LUMN()-1 ,H i g h t e m p ) nighttemp-LOOKUP(COLUMN() - 1,Lowtemp)  daytemp-LOOKUP(COLUMN( )-1 .Hightemp) nighttemp-LOOKUP(COLUMN( ) -1,Lowtemp)  daytemp-L00KUP(C0LUMN() -I.Hightemp) nighttemp-LOOKUP(COLUMN ()-1,Lowtemp)  daytemp-LOOKUP(C0LUMN()- 1 .Hightemp) 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 zing,Shortwavetrans)*0.7* 30.4*ESGradiationloss)  IF(R[-3]C<0,0,-floorArea* LOOKUP(COLUMN()-1.Monthly Radi a t i on)*L00KUP(roofg1 a z i ng,Shortwavetrans)*0.7* 30.4*ESGradiationloss)  IF(R[-3]C<0.0,-floorAre a*L00KUP(C0LUMN()-1,Mon th1yRadiation)*L00KUP(r o o f g l a z i ng, S h o r twa v e t r a ns)*0.7*30.4*ESGradiati onloss) IF(R[-4]C<0,0,roofarea*ES IF(R[-4]C<0,0,roofarea*ES IF(R[-4]C<0,0,roofarea* GUroof*R[-4]C*(2.628*R[-5 G U r o o f * R [ - 4 ] C * ( 2 ,628*R[-5 E S G U r o o f * R [ - 4 ] C * ( 2 . 6 2 8 * 4]C/24)) R[-54]C/24)) 4]C/24)) I F ( R [ - 5 ] C < 0 , 0 , N o r t h w a l l a r I F ( R [ - 5 ] C < 0 . 0 , N o r t h w a l l a r IF(R[-5]C<0.0.Northwal1 ea*ESGUnorthwal1*R[-5]C*( ea*ESGUnorthwa11*R[-5]C*( area*ESGUnorthwal1*R[-5 ]C*(2.628*R[-55]C/24) ) 2.628*R[-55]C/24) ) 2.628*R[-55]C/24)) IF(R[-6]C<0,0,(surfaceare IF(R[-6]C<0,0,(surfaceare IF(R[-6]C<0,0,(surfacea a-(Northwal1area+roofarea a-(Northwal1area+roofarea rea-(Northwallarea+roof ))*ESGUwalls*R[-6]C*(2.62 ))*ESGUwalls*R[-6]C*(2.62 area))*ESGUwalls*R[-6]C *(2.628*R[-56]C/24)) 8*R[-56]C/24) ) 8*R[-56]C/24) ) IF(R[-7]C<0,0,perimeter*I IF(R[-7]C<0,0,perimeter*I IF(R[-7]C<0,0,perimeter F ( L E N ( p e r 1 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 l ) = 3 , 1 * I F ( L E N ( p e r i m e t e r i n s u l ) . 3 9 , 2 . 7 7 ) * R [ - 7 ] C * ( 2 .628*R . 3 9 , 2 . 7 7 ) * R [ - 7 ] C * ( 2 . 6 2 8 * R = 3 , 1 . 3 9 , 2 . 7 7 ) * R [ - 7 ] C * ( 2 .628*R[-57]C/24)) [-57JC/24)) t-57]C/24)) IF(R[-8]C<0,0,ESGOinfi1 I F ( R [ - 8 ] C < 0 , 0 , E S G Q i n f i I t r IF(R[-8]C<0,0,ESGOinf11tr ation*R[-8]C*(2.628*R[-58 ation*R[-8]C*(2.628*R[-58 tration*R[-8]C*(2.628*R t-58]C/24)) 1C/24)) 1C/24))  IF(R[-3]C<0,0,-floorArea* L00KUP(C0LUMN()-1.Monthly Rad i a t i o n ) * L 0 0 K U P ( r o o f g l a z i ng,Shortwavetrans)*0.7* 30.4*ESGradiationloss)  106 107  110  111  112  113  114  1  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))  10 IF(R[-9]C<0,0,floorArea *L00KUP(C0LUMN()-1.Mont h i y R a d i a t ion)*LOOKUP( r o o f g l a z i ng,Shortwavetran s)*ESGradiationloss*0.7 *30.4*0.5)  11 IF(R[-9]C<0,0,floorArea*L 00KUP(C0LUMN()-1.MonthlyR adiat ion)*LOOKUP(roofglaz i ng,Shortwavetrans)*ESGra diationloss*0.7*30.4*0.5)  IF(R[-10]C<0,0,roofarea*E IF(R[-10]C<0,0,roofarea SGUroof*IF(thermal c u r t a i n *ESGUroof*IF(thermal cur =1,L00KUP(9,ESMUroofval), tain=1,LOOKUP(9,ESMUroo 1)*R[-10]C*(2.628*(24-R[- fval),1)*R[-10]C*(2.628 61]C)/24)) *(24-R[-61 ] C ) / 2 4 ) ) IF(R[-11]C<0,0,Northwalla IF(R[-11]C<0,0,Northwal 118 rea*ESGUnorthwal1*IF(ther 1area*ESGUnorthwal1*IF( malcurtain=1,LOOKUP(9,ESM thermalcurtain=1.LOOKUP U w a l 1 v a l ) , 1 )*R[-1 1 ] C * ( 2 . 6 (9,ESMUwa11val ), 1 ) * R [ 28*(24-R[-62]C)/24)) 1 ]C*(2.628*(24-R[-62]C) /24)) 119 I F ( R [ - 1 2 ] C < 0 , 0 , ( s u r f a c e a r I F ( R [ - 1 2 ] C < 0 , 0 , ( s u r f a c e a r I F ( R [ - 1 2 ] C < 0 , 0 , ( s u r f a c e ea-(Northwal1area+roofare ea-(Northwal1area+roofare area-(Northwa1larea+roo a))*ESGUwal1s*IF(therma1c a))*ESGUwal1s*IF(thermal c farea))*ESGUwal1s*IF(th urtain=1,L00KUP(9,ESMUwal urtain=1,L00KUP(9,ESMUwa1 e r m a l c u r t a i n = 1 , L 0 0 K U P ( 9 1 v a l ) , 1 ) * R [ - 1 2 ] C * ( 2 . 6 2 8 * ( l v a l ) , 1)*R[-12]C*(2.628*( ,ESMUwa11val),1)*R[-12] 24-R[-63]C)/24) ) 24-R[-63]C)/24) ) C*(2.628*(24-R[-63]C)/2 4)) 120 I F ( R [ - 1 3 ] C < 0 , 0 , p e r i m e t e r * I F ( R [ - 1 3 ] C < 0 , 0 , p e r i m e t e r * I F ( R [ - 1 3 ] C < 0 , 0 , p e r i m e t e IF(LEN(perimeterinsul)=3, IF(LEN(perimeterinsul)=3, r*IF(LEN(perimeterinsul 1 . 39,2.77)*R[-13]'C*(2.628 1.39,2.77)*R[-13]C*(2.628 )=3,1.39,2.77)*R[-13]C* *(24-R[-64]C)/24)) *(24-R[-64]C)/24)) (2.628*(24-R[-64]C)/24) ) 121 I F ( R [ - 1 4 ] C < 0 , 0 , E S G O i n f i I t I F ( R [ - 1 4 ] C < 0 , 0 , E S G Q i n f i 1 1 I F ( R [ - 1 4 ] C < 0 , 0 , E S G O i n f i rat ion*R[- 14]C*IF(thermal rat1on*R[-14]C*IF(thermal 1tration*R[-14]C*IF(the curtain=1,LOOKUP(9,ESMai r curtain=1,LOOKUP(9,ESMair rmalcurtain=1,L00KUP(9, change),1)*(2.628*(24-R[- change),1 ) *(2.628*(24-R[- ESMairchange),1)*(2.628 65]C)/24)) 65]C)/24)) *(24-R[-65]C)/24)) 122 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 0.4167*((24-R[-66]C)/24) ) 0.4167*((24-R[-66]C)/24)) *30.4167*((24-R[-66]C)/ *2.45*(ESGa i rchanges/3600 *2.45*(ESGa i rchanges/3600 24))*2.45*(ESGairchange )*IF(thermalcurtain=1,LOO )*IF(thermalcurtain=1,LOO s/3600)*IF(thermal curta KUP(9,ESMairchange),1)*vo KUP(9,ESMairchange),1)*vo in=1,L00KUP(9,ESMaircha lume*(R[-61]C/1000)) lume*(R[-61 ]C/1000)) nge),1)*volume*(R[-61]C /1000)) 123  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 1U w a l l v a l ) , 1 ) * R [ - 1 1 ] C * ( 2 . 6 28*(24-R[-62]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 o n ) * L O O K U P ( r o o f g 1 a z i ng,Shortwavetrans)*ESGra d1ationloss*0.7*30.4*0.5) 1 16 117  124  125  9 IF(R[-9]C<0,0,floorArea*L 00KUP(C0LUMN()-1.MonthlyR adiation)*LOOKUP(roofglaz i ng,Shortwavetrans)*ESGra diationloss*0.7*30.4*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,Northwalla rea*ESGUnorthwal1*IF(ther malcurtain=1,LOOKUP(9,ESM Uwallval),1)*R[-11]C*(2.6 28*(24-R[-62]C)/24) )  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))  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[-12]C<0,0,(surfacear ea-(Northwal1area+roofare a))*ESGUwal1s*IF(therma1c urtain=1,L00KUP(9,ESMUwal lval),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 1 )=3, 1 .39,2.77)*R[-13]C*(2.628 *(24-R[-64]C)/24)) IF(R t - 1 4 ] C < 0 , 0 , E S G O i n f i 1 t ration*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)/24j) *2.45*(ESGa i 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,LOO KUP(9,ESMairchange),1)*vo lume*(R[-61]C/1000))  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))  126  127 128 129 130 131 132  8 9 IF ( AND(C0LUMN()> = s t a r t m o n I F ( A N D ( C O L U M N * ) > = s t a r t m o n th+1,C0LUMN()< = endmonth+1 th+1,C0LUMN( )<=endmonth+1 ,C0LUMN()<>offmonth),R[-2 ,C0LUMN()<>offmonth),R[-2 ]C/heat i n g e f f i c i e n c y , 0 ) ]C/heat i ngeff i c i e n c y , 0 ) R[-1]C*fuelcost  R[-1 ] C * f u e l c o s t  10 11 I F(AND(C0LUMN()> = s t a r t m IF(AND(C0LUMN()>=startmon onth+1,C0LUMN()< = endmon th+1 ,C0LUMN()<=endmonth+1 t h + 1 , C 0 L U M N ( ) o o f f m o n t h ,C0LUMN()<>offmonth),R[) , R [ - 2 ] C / h e a t i n g e f f i c i e ]C/heat i n g e f f i c i ency,0) ncy,0) R[-1 ] C * f u e l c o s t 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  "CASHFLOW" 1oan-i n s t a l c o s t R C [ - 7 ] + R C [ - 6 -RC[ - 5 ] - R C [ 3 ] - R C [ - 2 ] + R C -1]  "DISCOUNTED" 1oan-i n s t a l c o s t RCt-1]  144  R[-1]C*(1-0.1  RC[-7]+RC[-6 3]-RC[-2]+RC  -RC[ -1]  5]-RC[- RCt-1]*(1/(1+int a(RC[9]-(currentyear) )  145  R[-1]C*(1-0.1  RC[-7]+RC[-6 3]-RC[-2]+RC  -RC[ -1]  5]-RC[- RC[-1]*(1/(1+int a(RC[9]-(currentyear) )  146  R[-1]C*(1-0.1  RC[-7]+RC[-6 3]-RC[-2]+RC  -RC[ -1]  5]-RC[- RC[-1]*(1/(1+int a(RC[9]-(currentyear) )  147  R[-1]C*(1-0.1  RC[-7]+RC[-6 3]-RC[-2]+RC  -RC[ -1]  5]-RC[-  148 R [ - 1 ] C * ( 1 - 0 . 1  RC[-7]+RC[-6 3]-RC[-2]+RC  -RC[ -1]  5 ] - R C [ - R C [ - 1 ] * ( 1 / ( 1 + i n t <z(RC[9]-(currentyear) )  149  R[-1]C*(1-0.1  RC[-7]+RC[-6 3]-RC[-2]+RC  -RC[ -1]  5]-RC[- RC[-1]*(1/(1+int a(RC[9]-(currentyear) )  150  R[-1]C*(1-0.1  RC[-7]+RC[-6 3]-RC[-2]+RC  -RC[ -1]  5]-RC[-  151  R[- 1]C*(1-0.1  RC[-7]+RC[-6 3]-RC[-2]+RC  -RC[ -1]  5]-RC[- RC[-1]*(1/(1+int a(RC[9]-(currentyear) )  152  R[-1]C*(1-0.1  RC[-7]+RC[-6 3]-RC[-2]+RC  -RC[ -1]  5]-RC[- RC[-1]*(1/(1+int a(RC[9]-(currentyear) )  RCt-1 ]*•( 1/( 1 + i n t a ( R C [ 9]-(currentyear) )  RC[-1]*(1/(1+1nt a(RC[9]-(currentyear) )  "YEAR NUMBER" IF(AND(SUM(R142C10:RC[)>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",1 ) IF(AND(SUM(R142C10:RC[)>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",2) IF(AND(SUM(R142C10:RC[)>0,SUM(R142C10:R[-1JC[ ])<0),"Break-Even",3) IF(AND(SUM(R142C10:RC[)>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",4) IF(AND(SUM(R142C10:RC[)>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",5) IF(AND(SUM(R142C10:RC[)>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",6) IF(AND(SUM(R142C10:RC[)>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",7) IF(AND(SUM(R142C10:RC[)>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",8) IF(AND(SUM(R142C10:RC[)>0.SUM(R142C10:R[-1]C[ ])<0),"Break-Even",9) IF(AND(SUM(R142C10:RC[)>0,SUM(R142C10:R[-1]C[ ])<0),"Break-Even",10)  153  8 R[-1]C*(1-0.1)  9 RC[-7]+RC[-6]-RC[-5]-RC[3]-RC[-2]+RC[-1]  154  R[-1]C*(1-0.1)  RC[-7]+RC[-6]-RC[-5]-RC[3]-RC[-2]+RC[-1]  155  R[-1]C*(1-0.1)  RC[-7]+RC[-6]-RC[-5]-RC[3]-RC[-2]+RC[-1]  156  R[-1]C*(1-0.1)  RC[-7]+RC[-6]-RC[-5]-RC[3]-RC[-2]+RC[-1]  157  R[-1]C*(1-0.1)  RC[-7]+RC[-6]-RC[-5]-RC[3]-RC[-2]+RC[-1]  158  159  1 1 IF(AND(SUM(R142C10:RC[-1] )>0,SUM(R142C10:R[-1]C[-1 ])<0),"Break-Even",11) R C [ - 1 ] * ( 1/( 1 + i n t ) c t ( R C [ - I F ( A N D ( S U M ( R 1 4 2 C 1 0 : R C [ - 1 ] 9]-(currentyear))) )>0,SUM(R142C10:R[- 1 ] C [ - 1 ])<0),"Break-Even",12) RC[-1 ]*( 1/( 1 + i n t ) a ( R C [ - I F ( A N D ( S U M ( R 1 4 2 C 1 0 : R C [ - 1 3 )>0,SUM(R142C10:R[-1]C[-1 9]-(currentyear))) ])<0),"Break-Even",13) R C [ - 1 ] * ( 1 / ( 1 + i n t ) a ( R C [ - IF(AND(SUM(R142C10:RC[-1] )>0,SUM(R142C10:R[-1]C[-1 9]-(currentyear))) ])<0),"Break-Even",14) R C [ - 1 3 * ( 1 / ( 1 + i n t ) a ( R C [ - IF(AND(SL)M(R142C10:RC[-1 ] 9]-(currentyear))) )>0,SUM(R142C10:R[-1]C[-1 3)<0),"Break-Even",15) "brk e v e n y r : " IF(SUM(R[-15]C:R[-13C)=12 0, " n e v e r " ,120-SUM(R[- 15] C:R[-1 ] C ) ) 10 RC[-1]*(1/(1+int)a(RC[9]-(currentyear)))  NPV(i n t , f 1 o w ) - ( i n s t a l c o s t -loan)+R[-16]C  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  t— 1  4^  10 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201  "Per s q u a r e  202 203  "Inst "  204 19 205 12000 206 21 207 2 0 0 0 0 208 3 209 10 210 3 2113 212 12 213 17 214 0 215 216 217 218 219 220 221 222 223 224 225 226 227  "Change  meter"  Cost" "  "Maintenance "  "Yield  0.2 100 0. 1 3 200 0.04 2 0.15 0.15 0.12 50 0  0.02 0 -0.03 0.05 0 -0. 1 -0.005 0 -0.01 0 0  in"  (%)"  "Heating"  "Efficiency"  1.15 1.12 1.25 1.12  1 .25  13  " S C R A T C H Instalcost"  15  14  PAD"  1  "Yld  "Ma i n t e n a n c e "  I F ( R C [ - 1 1 ] = 1 ,LOOKUP(RC[-2 I F ( R C [ - 1 2 ] = 1 LOOKUP(RC[-3], ],ESMi n s t a l c o s t ) * f 1 o o r A r e ESMma i n t c o s t * f l o o r A r e a , 0 ) a,0) I F ( R C [ - 1 1 ] = 1,LOOKUP(RC[-2 I F ( R C [ - 1 2 ] = 1 L 0 0 K U P ( R C [ - 3 ] , ESMma i n t c o s t ,0) ],ESMinstalcost),0) I F ( R C [ - 1 1 ] = 1 ,LOOKUP(RC[-2I F ( R C [ - 1 2 ] = 1 LOOKUP(RC[-3], ] , E S M i n s t a l c o s t ) * f 1 o o r A r e ESMma i n t c o s t * f l o o r A r e a , 0 ) a,0) I F ( R C [ - 1 2 ] = 1 LOOKUP(RC[-3] , I F ( R C [ - 1 1 ] = 1 , LOOKUP(RC[-2 ESMma i n t c o s t ,0) ],ESMinstalcost),0) I F ( R C [ -11]=1,L00KUP(RC[-2 IF(RC[-12]=1 ],ESMi n s t a l c o s t ) * f l o o r A r e ESMma i n t c o s t a,0) I F ( R C [ -11]=1,L00KUP(RC[-2 IF(RC[-12]=1 ],ESMi n s t a l c o s t ) * s u r f a c e a ESMma i n t c o s t rea,0) IF(RC[ -11]=1,L00KUP(RC[-2 IF(RC[-12]=1 ],ESMi n s t a l c o s t ) * N o r t h w a l ESMma i n t c o s t .0) 1 a r e a ,0 ) I F ( R C [ - 11] = 1,L00KUP(RC[-2 I F ( R C [ - 1 2 ] = 1 ],ESMi n s t a l c o s t ) * p e r i m e t e ESMma i n t c o s t r ,0) I F ( R C [ -1 1 ] = 1 ,L00KUP(RC[-2 I F ( R C [ - 1 2 ] = 1 ],ESMi n s t a l c o s t ) * f 1 o o r A r e ESMma i n t c o s t a,0) I F ( R C [ - 11] = 1,L00KUP(RC[-2 I F ( R C [ - 1 2 ] = 1 ].ESMi n s t a l c o s t ) * f l o o r A r e ESMma i n t c o s t a,0) SUM(R[-11]C:R[-1 ]C)  L00KUP(RC[-3] , *f1oorArea,0) L00KUP(RC[-3], *f1oorArea,0) L00KUP(RC[-3] , *Northwal1 a r e a L00KUP(RC[-3] , *per i meter,0) L00KUP(RC[-3], *floorArea,0) L00KUP(RC[-3], ,0)  SUM(R[-11]C:R[-1]C)  factor"  I F ( R C [ - 13]=1,LOOKUP (RC[-4] ,ESMyield),0 ) I F ( R C [ - 13]=1.LOOKUP (RC[-4] ,ESMyield),0 ) I F ( R C [ - 13]=1,LOOKUP ,ESMyield),0 (RC[-4] ) 13]=1.LOOKUP IF(RC[,ESMyield),0 (RC[-4] ) 13]=1,LOOKUP IF(RC[- ,ESMyield),0 (RC[-4] ) 13]=1.LOOKUP IF(RC[- ,ESMyield),0 (RC[-4] ) 13]=1,LOOKUP IF(RC[- ,ESMyield),0 (RC[-4] 13]=1.LOOKUP ) I F ( R C [ - ,ESMy i e l d ) , 0 (RC[-4] 13]=1.LOOKUP ) IF(RC[- ,ESMyield),0 (RC[-4] 13]=1.LOOKUP ) IF(RC[- ,ESMyield),0 (RC[-4] ) SUM(R[-11]C:R[-1]C)  UI OI OI ro  O  A 4^J^ cooD^cn  ^ 01  ^  ^  ^  ^  ^ u  C  O ^  C o  O  C  O  IDOD^J  CO  CO  CO  CO  UCJ'CO  MfOfO  cn  cn  A  CO  ro  CO CO -J  O  ro  co  Z.TT  "-  54 55  "Nov" "  56 57  64  65  ))))  IF(R[-4]C<0,0,-floorArea* LOOKUP(COLUMN()-1.Monthly Radiat ion)*LOOKUP(roofgla zing,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 [-93C/24))  66  "Dec"  'Year  total'  8  L00KUP(C0LUMN()-1.Hightem P) 58 L 0 0 K U P ( C 0 L U M N ( ) - 1 . L o w t e m p ) 59 d a y t e m p - R [ - 2 ]C 60 n i g h t t e m p - R [ - 2 ] C 61 v a p I n N i ght-(LOOKUP(COLUMN ()-1,humiditytable)*((132 2/(R[-3]C+273.2))*(10a((R [-3]C*7.5)/(R[-3]C+237.3) 62 63  14  13  12 53  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.humiditytab1e)*((132 2/(R[-3]C+273.2))*(10a((R [-3]C*7.5)/(R[-3]C+237.3)  ))))  IF(R[-4]C<0,0,-floorArea* L00KUP(C0LUMN()-1.Monthly Rad i a t i o n ) * L O O K U P ( r o o f g 1 a z i ng,Shortwavetrans)*0.7* 30.4) I F ( 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)) IF(R[-7]C<0,0,(surfaceare a-(Northwal1area+roofarea ))*Uwalls*R[-7]C*(2.628*R [-10JC/24)) IF(R[-8]C<0,0,perimeter*I F(LEN(perimeterInsul)=3,1 :39,2.77)*R[-8]C*2.628*R[ -11]C/24) IF(R[-9]C<0,0,0infi1trati on*R[-9]C*(2.628*R[-12]C/ 24)) IF(R[-10]C<0,0,floorArea* L00KUP(C0LUMN()- 1.Monthly Radiation)*LOOKUP(roofgla z i ng,Shortwavetrans)*0.5* 0.7*30.4)  IF(R[-7]C<0,0,(surfaceare a-(Northwal1area+roofarea ) )*Uwal1s*R[-7]C*(2.628*R [-10]C/24)) 67 I F ( R [ - 8 ] C < 0 , 0 , p e r i m e t e r * I F(LEN(perimeterinsul)=3, 1 .39,2.77)*R[-8]C*2.628*R[ -11]C/24) 68 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)) 69 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 o n ) * L 0 0 K U P ( r o o f g 1 a zing,Shortwavetrans)*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 IF(R[-11]C<0,0,roofarea*U roof*R[-11]C*(2.628*(24-R roof*R[-11]C*(2.628*(24-R [-15JO/24)) [-15JO/24))  SUM(RC[-12]:RC[-1])  SUM(RC[- 1 2 ] : R C [ - 1 ] )  SUM(RC[- 1 2 ] : R C [ - 1 ] )  SUM(RC[- 12] :RC[- 1 ] )  SUM(RC[-12]:RC[-1])  SUM(RC[ •12] : R C [ - 1 ] )  SUM(RC[ •12] : R C [ - 1 ] )  SUM(RC[- - 1 2 ] : R C [ - 1 ] )  72  73  74  75  76  12 IF(R[-12]C<0,0,Northwal l a r e a * U w a l 1 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(perimeterinsul)=3, 1.39,2.77)*R[-14]C*2.628* (24-R[-18]C)/24) IF(R[-15]C<0,0,Qinfiltrat 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))  13 IF(R[-12]C<0,0,Northwalla r e a * U w a l 1 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(perimeterinsul)=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 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))  14 SUM(RC[-12]:: R C [ - 1 ] )  SUM(RC[-12]:RC[-1])  S U M ( R C [ - 1 2 ] : R C [ - 1])  S U M ( R C [ - 1 2 ] : R C [ - 1])  S U M ( R C [ - 1 2 ] : R C [ - 1])  77  78  79 80  81 82 83 84 85 86 87 88 89  90  91 92 93  15  I F ( - R [ - 1 5 ] C > S U M ( R [ - 1 4 ] C : R I F ( - R [ - 15]C>SUM(R[- 14]C:R SUM(RC[- 1 2 ] : R C [ - 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[- 1 5 ] C : R [ - 2 ] C ) )  I F ( A N D ( C 0 L U M N ( ) > = s t a r t m o n I F ( A N D ( C 0 L U M N ( ) > = s t a r t m o n SUM(RC[-12]:RC[-1 ] ) th+1,C0LUMN()<=endmonth+1 th+1,C0LUMN()<=endmonth+1 , C 0 L U M N ( ) < > o f f m o n t h + 1 ) , R [ ,C0LUMN()<>offmonth+1),R[ -2]C/0.7,0) -2]C/0.7,0) R[-1]C*fuelcost S U M ( R C [ - 1 2 ] : R C [ - 1]) R[-1 ] C * f u e l c o s t  (R[-7]C[-1]+RC[-1]) / S U M ( R [ - 1 2 ] C [ - 1 ] :RC [-1])  12  14  13  94  95 96 97 98  99 100  101 102 103  104 105  "Nov" "  106  daytemp-L00KUP(C0LUMN()-1 daytemp-L00KUP(C0LUMN()- 1 .Hightemp) .Hightemp) nighttemp-LOOKUP(COLUMN() nighttemp-L00KUP(C0LUMN( ) -1, Lowtemp) -1,Lowtemp)  107 108 109  110  111  112  113  114  "  "Dec" "  "  "Year "  total" 1  IF(R[-3]C<0,0.-floorArea* L00KUP(C0LUMN()-1.Monthly Radiat ion)*L00KUP(roofgla zing,Shortwavetrans)*0.7* 30.4*ESGradiat ionloss)  I F ( R [ - 3 ] C < 0 , 0 , - f 1 o o r A r e a * SUM(RC[- 1 2 ] : R C [ - 1]) LOOKUP(C0LUMN()-1.Monthly Radiat ion)*LOOKUP(roofgla zing,Shortwavetrans)*0.7* 30.4*ESGradiat ionloss)  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,Northwa11ar ea*ESGUnorthwal1*R[-5]C*( 2.628*R[-55]C/24)) IF(R[-6]C<0,0,(surfaceare a-(Northwal1area+roofarea ))*ESGUwal1s*R[-6]C*(2.62 8*R[-56]C/24)) IF(R[-7]C<0,0,perimeter*I F(LEN(perimeterinsul)=3,1 .39,2.77)*R[-7]C*(2.628*R [-57]C/24)) IF(R[-8]C<0,0,ESGOinfi1tr ation*R[-8]C*(2.628*R[-58 ]C/24))  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,Northwa11ar 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(LEN(perimeterinsu1)=3,1 .39,2.77)*R[-7]C*(2.628*R [-57]C/24)) IF(R[-8]C<0,0,ESGQinf1 1 t r ation*R[-8]C*(2.628*R[-58 ]C/24))  SUM(RC[- 12]:RC[-1 ])  SUM(RC[- 1 2 ] : R C [ - 1 ])  SUM(RC[-12]:RC[- 1 ] )  SUM(RC[- 1 2 ] : R C [ - 1 ])  SUM(RC[- 1 2 ] : R C [ - 1])  115  12 13 14 IF(R[-9]C<0,0,f1oorArea*L IF(R[-9]C<0,O,f1oorArea*L SUM(RC[- 1 2 ] : R C [ - 1]) 0 O K U P ( C O L U M N ( ) - 1 . M o n t h l y R OOKUP(COLUMN()-1.MonthlyR adiation)*LOOKUP(roofglaz adiation)*LOOKUP(roofglaz 1ng,Shortwavetrans)*ESGra ing,Shortwavetrans)*ESGra diationloss*0.7*30.4*0.5) diationloss*0.7*30.4*0.5)  116 1 17 I F ( R [ - 1 0 ] C < 0 , 0 , r o o f a r e a * 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)) 1 18 I F ( R [ - 1 1 ] C < 0 , 0 , N o r t h w a l l a rea*ESGUnorthwall*IF(ther malcurtain=1,LOOKUP(9,ESM Uwal1 v a l ) , 1 ) * R [ - 11 ] C * ( 2 .6 28*(24-R[-62]C)/24) )  I F ( R [ - 1 0 ] C < 0 , 0 , r o o f a r e a * E SUM(RC[-12]:RC[-1]) SGUroof*IF(thermalcurtai n = 1 , L 0 0 K U P ( 9 , E S M U r o o f v a l ), 1)*R[-10]C*(2.628*(24-R[61]C)/24)) SUM(RC[-12]:RC[-1]) IF(R[-11]C<0,0,Northwalla rea*ESGUnorthwal1*IF(ther malcurta1n=1,LOOKUP(9,ESM U w a l 1 v a l ) , 1 )*R[-1 1 ] C * ( 2 . 6 28*(24-R[-62]C)/24))  119  IF(R[-12]C<0,0,(surfacear ea-(Northwal1area+roofare a))*ESGUwal1s*IF(thermalc urtain=1,L00KUP(9,ESMUwal Ival),1)*R[-12]C*(2.628*( 24-R[-63]C)/24) )  I F ( R [ - 1 2 ] C < 0 , 0 , ( s u r f a c e a r SUM(RC[-12]:RC[-1]) ea-(Northwal1area+roofare a))*ESGUwal1s*IF(therma1c urtain=1,LOOKUP(9,ESMUwal 1 val),1)*R[-12]C*(2.628*( 24-R[-63]C)/24) )  120  IF(R[-13]C<0,0,perimeter* IF(LEN(perimeterinsul)=3, 1.39,2.77)*R[-13]C*(2.628 *(24-R[-64]C)/24) )  I F ( R [ - 13]C<0,0,perimeter* SUM(RC[-12]:RC[-1]•) IF(LEN(perimeterinsu1)=3, 1.39,2.77 ) * R [ - 1 3 ] C * ( 2 . 6 2 8 *(24-R[-64 ] C )/24))  121  IF(R[-14]C<0,0,ESGQinfilt ration*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*(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 SUM(RC[-12]:RC[-1]) ration*R[-14]C*IF(thermal curtain=1,LOOKUP(9,ESMa1r change),1 ) *(2 . 628*(24-R[65]C)/24)) IF(R[-61]C<0,0,(3600*24*3 SUM(RC[-12]:RC[-1]) 0.4167*((24-R[-66]C)/24)) *2.45*(ESGa i rchanges/3600 )*IF(thermalcurtain=1,LOO KUP(9,ESMai r c h a n g e ) , 1 ) * v o lume*(R[-61]C/1000))  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) )  I F ( - R [ - 15]C>SUM(R[- 14]C:R SUM(RC[- 12]:RC[ •1]) [-9]C),SUM(R[-7]C:R[-2]C) ,SUM(R[- 15 ]C : R [ - 2 ] C ) )  122  123  124  125  15  (R[-7]C[-1]+RC[-1]) /SUM(R[-12]C[-1]:RC [-1])  126  12 IF(AND(COLUMN()>=startmon th+1,C0LUMN()<=endmonth+1 ,C0LUMN( ) o o f f m o n t h ) , R [ - 2 ]C/heat i ngeff 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  14 13 I F ( A N D ( C O L U M N ( ) > = s t a r t m o n SUM(RC[-12] :RC[- 1]) th+1,C0LUMN()<=endmonth+1 ,C0LUMN( ) < > o f f m o n t h ) , R [ - 2 ]C/heatingefficiency,0) R[-1 ] C * f u e l c o s t  15  SUM(RO[-12]:RC[-1])  133 134 135 136 137 138 139 140 141 142 143  144  145  146  147  148  149  150  151 to  152  APPENDIX E: USER'S MANUAL  123  1 24  GREENSIM v e r s i o n Copywrited  1.0 DRAFT  by B a r r y S h e l l ,  COPY  September  1984  USER'S MANUAL  Introduction Most  growers  conservation  recognize  the  increasing  i n g r e e n h o u s e s . However, when  money on e n e r g y c o n s e r v a t i o n  equipment,  of  the  alternatives  can  GREENSIM c a p i t a l advisors  in  associations improve  budgets  like  to determine  GREENSIM  a list  profitability  makes p r e d i c t i o n s  greenhouse  energy  Bellevue,  template that  Development p r o c e s s . The associated calculate  of  model with  a must  each  agricultural  and h o r t i c u l t u r a l to  operations.  also  used  on a c h o i c e  c o n s e r v a t i o n measures A l l that  equiped with Washington).  most p o p u l a r s p r e a d s h e e t programs spreadsheet  by  by  New  lending  potential.  based  and page 5 f o r d e s c r i p t i o n s ) .  Microsoft,  used  of energy c o n s e r v a t i o n  are  investment  number  c h o i c e d i f f i c u l t . The  greenhouse  GREENSIM  GREENSIM i s a m i c r o c o m p u t e r by  of  energy  i t comes t o s p e n d i n g  co-ops  to analyse the p o t e n t i a l  institutions  popular  be  marketing  for  the overwhelming  correct  b u d g e t i n g model c a n  governments,  the  equipment  make  need  f r o m among  (see t a b l e  i s required  Multiplan Multiplan  available  and  10  1for t o use  (copywrited i s one o f t h e  GREENSIM  is  a  c a n be l o a d e d by M u l t i p l a n . new  equipment  include energy  the e x p e c t e d changes  budget  c a n be a  a l l revenues saving  and  alternative.  i n energy consumption  complex expenses It  as w e l l  must as  1 2 5  t h e e f f e c t s on c r o p y i e l d expectations rapidly,  about  the i n i t i a l  finalized.  With  these  and  conservation  factors  can  vary  widely  b u d g e t may c h a n g e many t i m e s  Multiplan  model, however, you can quickly  over a c e r t a i n p e r i o d of time.  easily.  and  build  the  and  modify  In the process  technique  i s best  GREENSIM  f o r your  and change  before  i t is  energy  budget  you w i l l  Because  planning  projections  l e a r n which energy  individual  greenhouse.  expenditure  depends  Princ iples The two  profit  p o t e n t i a l o f any new c a p i t a l  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 s e x p e c t e d t o f l o w o u t . The depends selling  on  the  conservation  expects  of  that  affect  of  i n a n d how much  inflows  and  units sold, unit price,  p r i c e and so on. L e t ' s  relationships  By  number  level  take  a  the  look  at  unit cost,  profitability  important of  energy  a  grower  i n greenhouses.  implementing t o lower  energy  conservation  measures,  the u n i t cost of p r o d u c t i o n .  e n e r g y u s e t h a t w o u l d be e x p e c t e d .  grower's planned choice  GREENSIM u t i l i z e s  of energy c o n s e r v a t i o n  translated into dollar  the  decrease  The m o d e l i s b a s e d on t h e  p h y s i c a l p r o p e r t i e s o f h i s g r e e n h o u s e . The then  outflows  some  a d e s c r i p t i o n o f t h e grower's greenhouse t o model in  on  equipment and t h e  energy  savings  are  amounts a n d i n c l u d e d i n a c a s h  flow  budget a n a l y s i s . The  cash flow a n a l y s i s includes  extra  annual  c o s t s b r o u g h t a b o u t by t h e new e n e r g y c o n s e r v a t i o n addition crop  yield  the  p o s i t i v e or negative  effect  i s i n c l u d e d . Energy c o n s e r v a t i o n  a number o f ways, w h i c h may  include  maintenance equipment. I n  on t h e o v e r a l l  annual  can a f f e c t y i e l d s i n  reduced  light  levels  or  1 26  increased  humidity.  on  yield  crop  In g e n e r a l t h e e f f e c t  of energy  i s e s t i m a t e d as a percentage  conservation  of e x p e c t e d  annual  yield. The  cash  borrowing All  cash  money flows  Inflation  analysis  to are  also  install shown  allows  the energy  in  goal  o f t h e model  equipment,  discounted  expressed  in  investment  today's  p r e s e n t v a l u e o r NPV. The<= b r e a k - e v e n  is  also  calculated.  alternatives,  of  into  choosing  tests  of  dollars.  greenhouse world  relationship,  to predict  This  year  conservation  i s known a s t h e  of t h e  among e n e r g y  vegetable  investment  conservation v a l u e and t h e option.  growing  a l l inter-relationships  the model. U n f o r t u n a t e l y  a l l those  the v a l u e of f u t u r e  be t h e most p r o f i t a b l e  although  not  even  the exact  very  would be  t h e GREENSIM model  a i d to decision cash  is  Multiplan  d i s c u s s e d a b o v e . Keep i n mind t h a t  i s meant t o be an i n t e r a c t i v e designed  costs  tax  the highest net present  will  In an i d e a l  include every  include  year  business  incorporated  When  t h e one w i t h  break-even  complicated.  can  after  i n energy  dollars.  net  The  the  c o n s e r v a t i o n equipment.  i s t o determine  f l o w due t o t h e f a r m e r ' s  lowest  for  and d e p r e c i a t i o n a r e a l s o i n c l u d e d .  The cash  flow  t h e model  making. I t i s not  f l o w s , though  i t came w i t h i n 20% o f g r o w e r s ' a c t u a l  does  in preliminary  values.  U s i n g The M o d e l The  model e x i s t s a s two s e p e r a t e s p r e a d s h e e t s : GREENSIM and  GREENDAT. GREENSIM i s a d e p e n d a n t that  does a l l t h e c a l c u l a t i o n s  simulation.  GREENDAT  is  a  spreadsheet  linked  t o GREENDAT  and d i s p l a y s  the r e s u l t s  spreadsheet  in  the  form  of  the  of  a  1 27  questionnaire  t h a t makes d a t a  entry easier f o rthe user.  GREENDAT GREENDAT i s d e s i g n e d first.  The u s e r  fills  f o r data  e n t r y and  i n t h e answers t o q u e s t i o n s ,  a r e m a r k e d by a d a s h e d l i n e . A l l o t h e r are to  locked,  be  loaded  a l l of which  p a r t s of t h e  spreadsheet  t h e r e f o r e y o u may u s e t h e " n e x t - u n l o c k e d - c e l l " k e y  move t h r o u g h  the questionaire  "arrow-direction" the next.  should  keys  The q u e s t i o n s  may  quickly.  Alternatively  be u s e d t o move f r o m one a n s w e r t o  a l l h a v e d e f a u l t a n s w e r s a n d may be  u n c h a n g e d i f d e s i r e d . A t a n y t i m e y o u may go back a n d c h a n g e entry  simply  the  by u s i n g t h e d i r e c t i o n  left an  k e y s t o move t o t h e 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 elaboration  of  the  i s given  questions  are  self  explanatory  but  some  here:  Y o u r Name Fifteen characters are allowed  f o r t h e g r o w e r ' s name.  Dimensions You  may t y p e  i n 'meters' o r ' f e e t ' i n t h e f i r s t  blank.  The n e x t  t h r e e m e a s u r e m e n t s must a l l be i n t h e u n i t s y o u h a v e c h o s e n . The wall height have and  gables.  I f you  a m u l t i u n i t g u t t e r connected greenhouse complex, the width length dimensions should  length roof  i s measured from t h e ground t o t h e  i s the  dimension  be o v e r a l l  of the b u i l d i n g w a l l p a r a l l e l  r i d g e . I f y o u have s e v e r a l s e p a r a t e  g r e e n h o u s e c o m p l e x e s i t i s recommended simulations  t o t a l measurements.  result  in a total  tothe  detached greenhouses or that  you  do  f o r e a c h o n e . A l t e r n a t i v e l y y o u may e n t e r  figures that w i l l  The  f l o o r area  separate dimension  equivalent  t othe  128  sum may  of the f l o o r a r e a s  i n a l l of your  n o t be a s a c c u r a t e  adequate f o r rough  greenhouses.  as separate  This  method  s i m u l a t i o n s b u t may be  comparisons.  Temperature S e t p o i n t s T h e s e must be i n C e n t i g r a d e t a b l e t o h e l p you  CONV^ION  Separate  C * ~f  p*  |  -*?  M  available.  "twinwall" polycarbonate Estimated  40  (0  M  IM  roof are a l l o w e d . There are 9  Acrylic  type  SDP i s s i m i l a r  to  the  glazing.  Leakiness  i f you  weatherstripping relatively  i n the c o r r e c t u n i t s .  Type  types  Example:  conversion  JW»WWr^H#Wf'  c h o i c e s f o r s i d e w a l l s and  glazing  H e r e i s a handy  enter the temperature  T F M P P R A T I IDC  Glazing  degrees.  had  around  a  double-poly  a l l vents  t i g h t . Therefore  you  greenhouse  and doors  it  with  would  be  w o u l d e n t e r a 1 o r a 2.  Orientation The  roof l i n e  i s the  ridgeline.  Energy C o n s e r v a t i o n  Measures  1. R o o t zone h e a t i n g i s an a t t e m p t the  plants  more  effectively.  to  distribute  To b e n e f i t  from  this  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 Hot  water  i s delivered  s m a l l e r tubes near  where i t may  is  to  form of changed.  t o t h e p l a n t e d a r e a v i a hundreds of  that are placed along the f l o o r  theplants'  heat  r o o t s . Heat i s t h e r e b y  of the  greenhouse  brought to the p l a n t s  be n e e d e d m o s t .  2. A s t a c k h e a t  recovery unit  i s simply  a  gas-to-liquid  129  heat  exchanger  smokestack  that  reclaims  some  the  heat  from  the  of t h e 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 natural  of  gas  in  such  r e l e a s e d as r a d i a t i o n  a  way  is  that  accomplished the  by  burning  bulk of i t s energy i s  i n t h e l o n g wave i n f r a - r e d . T h e s e  burners  a r e t y p i c a l l y p l a c e d i n t h e peak o f a g r e e n h o u s e  and t h e r a d i a n t  energy  I n t h i s way t h e  is  reflected  onto  the . plant  p l a n t s themselves a r e heated d i r e c t l y transfer  media  such  as  water  canopy.  w i t h no  and a i r — a  intervening much more  heat  efficient  process. 4. E n e r g y greenhouse by  savings resulting  environment  t h e model.  5.  management s y s t e m c a n a l s o  saves energy,  Infiltration  increases y i e l d ,  resulting  and  decreases  i n f i l t r a t i o n and heat Energy  Typically  with a  layer  of  plastic  f a n i s a n o t h e r method t h a t  film  reduces  loss.  saving  r a d i a t i v e heat l o s s e s  between  sealant.  i s i n f l a t e d with a small  walls.  simulated  l o s s e s c a n be r e d u c e d by c a u l k i n g  6. C o v e r i n g t h e g r e e n h o u s e  7.  be  costs.  glass laps with s i l i c o n e  that  microcomputer  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  from t h e s e systems labour  from t h e use of a  measures  involve  the  north  f o r d e c r e a s i n g c o n d u c t i v e and  insulating wall  is  some o f t h e insulated  greenhouse  with  p o l y u r e t h a n e o r p o l y s t y r e n e foam. I n Canada t h e  sun  always  direct  i n t h e s o u t h e r n p a r t o f t h e sky so l i t t l e  opaque  is  almost  sunlight  is blocked using t h i s technique. 8.  A  similar  method  is  the  installation  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 o f one m e t e r . T h i s  is  130  the an  usual ideal  location  place  for effective  9. A t h e r m a l which  is  of hot water h e a t i n g  across the  Materials  from l i g h t  fabric.  range These  conduction,  10.  In  this  drawn heat  reduce  B.C. w i t h technique  through  a  from  of  plastic  a  film heat  flexible gutter  by  Remember measure w i l l  a  heated  but  t o have  heat-recovery  be  a i r inside  that  Several  heating  because w i t h  storage  system.  the greenhouse thereby  this  in  is  storing  heat  can  be  requirements. an  energy  techniques  reasonable:  infra-red  unit  energy  indicates  be s i m u l a t e d .  simultaneously unlikely  "1"  convection,  of experiments  b u r i e d beneath the f l o o r ,  t o h e l p decrease  laminated  recently.  i n t h e e a r t h under t h e b u i l d i n g . A t n i g h t  reclaimed  night.  and have t h e r e f o r e come  t h e J a p a n e s e wet e a r t h h e a t  pipes  at  t o d a r k heavy  i s b a s e d on t h e r e s u l t s  hot s o l a r  material  t o g u t t e r and  greenhouse  loss  and r a d i a t i o n  investigation  storage  is  roof  walls  infiltration  Heat  Saanich,  side  coverings  under c o n s i d e r a b l e  blanket  the  sometimes a r o u n d  and i s t h e r e f o r e  insulation.  c u r t a i n or  pulled  pipes  for  be  example  installed  IR h e a t  can  conservation  there  along  simulated  i t would be with  a stack-  i s no b o i l e r .  ECONOMIC VARIABLES This  s e c t i o n a s k s you t o e s t i m a t e  change. stand. 12%  may e n t e r  Use d e c i m a l  a s .12.  later vary  You  at  initial  fractions  expected  values  rates  of  economic  or l e t the d e f a u l t  when i n p u t i n g % v a l u e s :  e.g.  values Enter  These v a r i a b l e s a r e 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  any  time.  As y o u c o n d u c t  some o f t h e economic  change c o u l d a f f e c t  s e v e r a l s i m u l a t i o n s you can  i n d i c a t o r s t o l e a r n how  their  your greenhouse's p r o f i t p i c t u r e .  future  131  Interest The  first  Rate  variable,  "interest  rate"  is  also  known  as the  "discount rate".  I t i s the r a t e a t which f u t u r e net annual  flows  discounted  will  present  be  dollars.  conservation you  If  so  that  instead  equipment,  of  savings  discount  the  energy  the  investment  investment  discount rate i s set higher  savings  bonds.  investment analysis  investments  in of  By the  including discount  profitablility.  p e r c e i v e no Tax  This  of about  rate  bonds, (1984).  investment,  o f money.  The  investment  this reason  a t a minimum  lost  to  invest  why t h e d e f a u l t v a l u e o f  i s t h a t most p e o p l e  the  energy  10%  a s b e i n g c o n s i d e r a b l y more risk you  view  energy  risky  than  of energy c o n s e r v a t i o n get  I f you f e e l  may s e t t h e d i s c o u n t r a t e l o w e r you  on  into savings  the o p p o r t u n i t y you  i n s a v i n g s b o n d s . The r e a s o n  conservation  money  riskless  conservation  l e v e l o f 10% i s t o r e f l e c t t h a t money  spending  bonds a r e a r e l a t i v e l y  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 we  c a n be e x p r e s s e d i n  y o u p u t t h a t money  w o u l d have a r e t u r n on y o u r  Since  they  cash  a  there  more  realistic  i s l e s s r i s k you  ( t o 16% f o r e x a m p l e , o r  10% i f  risk).  Rate  represents  the  marginal  f e d e r a l and p r o v i n c i a l  tax rate.  Investment tax c r e d i t s a r e not i n c l u d e d i n t h e model. L o a n 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  number o f s i m u l a t i o n r u n s . A f t e r t h e f i r s t the e s t i m a t e d c o s t of your b a s e d on y o u r be  chosen  greenhouse dimensions.  easier to decide  energy With  how much t o b o r r o w .  and f i l l  i t in after a  run you w i l l conservation this  f i n d out scheme  knowledge i t w i l l  132  Crop Expected Annual There a r e s e v e r a l it  t o cases/m2  conversion  Variables  Yield  ways o f e x p r e s s i n g  (where  a case  yield.  i s about  We have  20 p o u n d s ) . The f o l l o w i n g  t a b l e may h e l p y o u d e t e r m i n e y o u r y i e l d To g e t y i e l d MULTIPLY  BY  pounds t o m a t o e s / s q f t  0. 5376  pounds c u c u m b e r s / s q f t  0. 5988  tons  tomatoes/acre  0. 025  tons  cucumbers/acre  0. 0275  pounds t o m a t o e s / p l a n t  0. 1 344  pounds  0. 1 497  cucumbers/plant  When y o u have a n s w e r e d  old  save version  which  copies  i n cases/m2.  i n cases/m2,  0. 5988  cucumbers/sq f t  must  standardized  the l a s t  i t w i t h your answers  question  on d i s k  on  GREENDAT  you  thereby o v e r w r i t i n g the  o f GREENDAT i n t h e p r o c e s s . Next  you  t h e d a t a f r o m GREENDAT a s i t l o a d s .  load  GREENSIM  1 33  GREENSIM When y o u f i r s t of  variables.  simulation  l o a d GREENSIM t h e s c r e e n w i l l  These v a r i a b l e s  display  a page  give you a concise report  on t h e  y o u h a v e s e t up a n d i t s e f f e c t on t h e e n e r g y u s e a n d  economics  of  your  greenhouse.  GREENSIM  m o d e l l i n g p r o g r a m . T h a t means y o u c a n a l t e r variables,  recalculate  change b e f o r e your variables  contains  the  controls, alter  a  to  (see figure  do  simulation. car's  1)  variables.  the  this  The  These  program  easilly  unshaded  the  area  like  the  They show t h e c u r r e n t  state  The  other  are  variables  gas pedal and s t e e r i n g  any o f t h e s e , r e c a l c u l a t e  indicators  order  o r f u e l gauge o f a c a r .  like  of  s p r e a d s h e e t , and watch t h e r e s u l t s  In  indicating  r e s u l t s of the  17  interactive  i n t o two g r o u p s : c o n t r o l l i n g v a r i a b l e s a n d  variables.  speedometer and  eyes.  are divided  indicating  the  i s an  the spreadsheet  are the  w h e e l . You c a n and  watch  the  change.  INDICATING VARIABLES Some  of  these  are  simply  copied  over  from  the  s p r e a d s h e e t t o r e m i n d y o u o f t h e s i z e a n d age o f t h e or  the  based  length  on  the  1. A d j u s t m e n t s 1. This  of  GREENDAT  greenhouse  the heating season. Others a r e c a l c u l a t i o n s  data  input.  There  2. H e a t l o s s summary  are 3.  three  sections:  Results.  Adjustments  section  lists  the  calculated  weighting factors  that a r e  used i n t h e s i m u l a t i o n . %  light  This i s the expected reduction  in light  available  b r o u g h t a b o u t by u s e o f e n e r g y  conservation.  to  the  crop  1 34  % The  fuel  predicted  indicated % The  savings  in  fuel  use  or b o i l e r  efficiency  are  here.  yield  p e r c e n t i n c r e a s e or d e c r e a s e  in  yield  expected  is  shown  here. 2. H e a t L o s s Summary A  quick  comparison  characteristics presented  before  in this  is  a  and  your  line  comparison  of  heat  heat-loss  conservation  l o s s b e f o r e and  after  of m e g a j o u l e s / m / y r .  the  Above  2  heat  loss  is  component  k e e p t r a c k of h e a t i n g c o s t s  2  so  energy  total  i n terms  d o l l a r s / f o o t / y r . Many g r o w e r s way  greenhouse's  after  s e c t i o n . The  i s shown on t h e b o t t o m this  of  by p r e s e n t i n g h e a t l o s s a s d o l l a r s ,  i t s h o u l d be  in this  easier  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 t h e c a s h f l o w a n a l y s i s a r e summarized Annual  here.  L o a n Payment  I f t h e u s e r i n d i c a t e d t h a t money w o u l d  be b o r r o w e d  for  e q u i p m e n t , t h e a n n u a l payment o f i n t e r e s t and p r i n c i p a l here.  I t i s c a l c u l a t e d based  on t h e l o a n i n t e r e s t  l o a n amount o v e r a f i f t e e n y e a r t e r m w i t h a n n u a l  the  new  i s shown  r a t e , and  the  payments.  Change I n 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 energy  c o n s e r v a t i o n . Parentheses around  a l o s s . The  change i n s a l e s i s  % y i e l d adjustment  or  lost  a number i n d i c a t e  calculated  f a c t o r by t h e c r o p y i e l d  by  i t is  multiplying  times the crop  by  the  price.  1 35  Annual This  Energy  Saving  i s the annual  dollar  value of t h e f u e l  saved  by e n e r g y  from  the  conservation. Net  Present  Value  This i s the primary flow a n a l y s i s . for  the  financial  I t i s t h e n e t economic b e n e f i t i n c u r r e n t  life  of  the  including a l l risks, The  indicator derived  energy  saving  costs, inflation,  investment  n e g a t i v e NPV i m p l i e s a p o o r  dollars  (15 y e a r s ) ,  depreciation  h i g h e r t h e NPV t h e b e t t e r t h e i n v e s t m e n t  cash  and  potential.  taxes. Zero o r  investment.  Break Even Year This  i s t h e p o i n t i n time a t  f u t u r e cash  which  the cumulative  flow i s equal t o the i n i t i a l  discounted  investment.  CONTROLLING VARIABLES All  of t h e v a r i a b l e s  user a t any  time.  spreadsheet. h a v e made w i l l of  the  accuracy. by  are  be r e f l e c t e d  linked  to  parts  of the  key i s p r e s s e d , changes y o u  When GREENSIM i s f i r s t  ( t h e shaded a r e a  other  i n the i n d i c a t i n g variables  section  loaded, the c o n t r o l l i n g  i n f i g u r e 2) s h o u l d  be  checked  for  Some a r e s i m p l e e s t i m a t e s t h a t may n e e d t o be c h a n g e d  t h e u s e r . The c o n t r o l l i n g  Energy  s e c t i o n c a n be c h a n g e d by t h e  When t h e r e c a l c u l a t i o n  screen.  variables  They  i n this  variables are  in  3  sections:  1.  S a v i n g M e a s u r e s , 2. P h y s i c a l S t a t e o f t h e G r e e n h o u s e , 3.  Financial Variables. 1. E n e r g y S a v i n g As  Measures  i n GREENDAT y o u c h o o s e a t e c h n i q u e by t y p i n g  a  "1" to the  1 36  right  of  the  energy saving  detailed description choose  more  than  measure d e s i r e d ,  of t h e energy one,  but  saving  (see  page 5 f o r a  measures).  You  may  p l e a s e d o n ' 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 o f m u l t i p l e  energy saving  measures a r e  cumulative not a d d i t i v e . 2.  Physical  Six variables the  State  Of The G r e e n h o u s e  c a n be c h a n g e d t o s i m u l a t e p h y s i c a l  alterations to  greenhouse. N i g h t Temp And Day Temp  These a r e t h e n i g h t greenhouse.  To  t h e s e . No y i e l d Glazing  code  see  numbers  set points  Fuel  Type  are indicated to  proper  There  the  t h e e f f e c t on t o t a l h e a t l o s s y o u may v a r y  the  changes.  C r o p Grown i n english  b u t have  corresponding  e x t r e m e r i g h t . To c h a n g e t h e s e , y o u must  c h a n g e t h e c o d e numbers. Use t h e f o l l o w i n g the  inside  e f f e c t s a r e l i n k e d t o these temperature  Type  These v a r i a b l e s  and day t h e r m o s t a t  table  to  determine  numbers: MATERIAL  CODE  Glass double glass polyethylene double poly poly + glass fibreglas (flat) f i b r e g l a s (corrugated) 1 inch styrofoam double a c r y l i c (twinwall)  1 2 3 4 5 6 7 8 9  oil n a t u r a l gas electricity  1 2 3  cucumber tomato  1 2  i s no  n e e d t o t y p e i n t h e name, j u s t t y p e t h e c o d e  number i f a c h a n g e 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 total  of e l e v e n  Interest Also  parameters are a v a i l a b l e f o r  Rate  known a s t h e " d i s c o u n t  varied  to  simulate  equivalent Average the  o f one  risk  sensitivity  rate", i t i s this  r i s k . A no r i s k  y e a r government  w o u l d be s i m u l a t e d  historical  experimenting  experimentation.  average with  of each  risk  this  p r o j e c t would have the  securities  of  of  ( a t 11%  by a 20% d i s c o u n t the  variable  method  parameter t h a t  stock  you  energy  in  is risk  1984).  r a t e based  on  market.  By  can d e t e r m i n e t h e  risk  conservation  for  your  greenhouse. F u e l E s c a l a t i o n And Historically  fuel  Inflation  p r i c e s have c l i m b e d  r e s t o f t h e economy and t h e r e to  change.  checking plan to  Rate  sensitivity  of  f a s t e r than the  i s no r e a s o n t o e x p e c t t h i s  However, you can s i m u l a t e the  slightly  other  outcomes t o o ,  a particular  energy  trend thereby  conservation  inflation.  Tax  Rate  T h i s a l l o w s y o u t o compare, what w i l l you change t a x b r a c k e t .  happen t o your c a s h f l o w i f  I t must i n c l u d e t h e p r o v i n c i a l  as  well  a s t h e f e d e r a l p o r t i o n o f income t a x . L o a n Amount And Loan This and  Interest  c o r r e s p o n d s t o t h e money y o u e x p e c t t o b o r r o w installation  interest  of energy c o n s e r v a t i o n  r a t e t h a t you f e e l  l o a n amount h i g h e r C r o p P r i c e And These  you w i l l  than the i n s t a l l e d  equipment.  f o r purchase You  set  the  h a v e t o p a y . Do n o t s e t t h e cost  (see b e l o w ) .  Yield  c a n be m o d i f i e d  t o match your market  p r i c e and  greenhouse  1 38  productivity.  The e f f e c t o f l e a n y e a r s o r bumper  f u t u r e c a s h f l o w c a n a l s o be m o d e l l e d t h i s I n s t a l l e d C o s t And M a i n t e n a n c e  crops  on  the  way.  Cost  If  y o u know what t h e a c t u a l c o s t o f e n e r g y c o n s e r v a t i o n w i l l  for  your  values  s i t u a t i o n you can shown  fill  in  the  correct  values.  be The  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 b a s e d on t h e a r e a o f  your greenhouse.  Similarly  i f maintenance  c o s t s seem t o o l o w  or  h i g h , t h e y may be c h a n g e d t o y o u r own e x p e c t e d v a l u e s . Fuel This  is  Cost the  fuel  c o s t i n B r i t i s h Columbia  h a v e a n o t h e r s o u r c e o r pay a d i f f e r e n t  price  a s o f 1984. I f y o u f o r energy you  may  change t h i s v a l u e , t o o .  LOOKUP TABLES The  user  has  control  d a t a on p r i c e s , w e a t h e r , factors are contained program.  GREENSIM  p r o p e r t i e s of m a t e r i a l s , and a d j u s t m e n t  in  lookup  searches  s i m u l a t i n g your greenhouse. local  weather  local  160.  tables  here  at  for  By c h a n g i n g  the  end  information  these  values  o r p r i c e s , t h e program can e a s i l l y  of  the  used  in  to  suit  be a d a p t e d t o  needs. There  are  o v e r a n o t h e r a r e a o f GREENSIM. A l l  a r e 4 t a b l e s of i n t e r e s t  located The  at  first  "customize"  weather  These  tables  of the spreadsheet s t a r t i n g a t l i n e  time  run  you  GREENSIM,  you  may  want  i t by s e t t i n g up t h e l o o k - u p t a b l e s t o r e f l e c t  weather data  user.  the bottom  c o n d i t i o n s . T h i s i s e a s y t o do w i t h The  to the  for  table  currently  Abbotsford,  to local  Multiplan. contains  B.C. o b t a i n e d  40 from  year  average  Environment  1 39  Canada. T h i s c o n s i s t s of a v e r a g e d a i l y relative  humidity,  and s o l a r  i n f o r m a t i o n c a n be o b t a i n e d Simply  type  The  from  ESM  (energy  saving  factors  and  techniques  in  program.  the  the t o t a l  your  Similar  local  office.  prices These  weather  ones. Temperature  i s i n MJ/m /day. 2  measures)  unit  temperature,  f o r each month.  the existing  Solar radiation  weighting  estimate  radiation  t h e new v a l u e s o v e r  must be i n C e n t i g r a d e .  l o w and h i g h  table  contains  f o r t h e 10 e n e r g y unit  prices  are  screen a f t e r  in this  in  a n d 9. A l l v a l u e s a r e i n $/m  heat  8  used  the  look-up  t a b l e . These p r i c e s a r e 2  except  total  installed  a f u n c t i o n of area price.  The  2  The  Fuel  average annual Cost  table  the price given  values  ( c o l u m n 9) a r e i n $ / m / y r , e x c e p t which a r e t o t a l  main  located  f o r "stack-  r e c o v e r y " and "microcomputer c o n t r o l " . S i n c e these  are not d i r e c t l y  to  t h e y have been c a l c u l a t e d o r y o u c a n u p d a t e  the u n i t v a l u e s columns  saving  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 m e a s u r e s . Y o u c a n c o r r e c t t h e s e c o s t s on values  the  measures  is  average  i n the Maintenance  f o r stack-heat  and  column  computer  maintenance c o s t s .  and  Crop  Data  table  c h a n g e d , o r y o u c a n c h a n g e t h e s e v a r i a b l e s on t h e  c a n a l s o be main  "values  s c r e e n , " and r e c a l c u l a t e . If  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  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 o f rates i n b u i l d i n g s , these  f o r the p r o p e r t i e s of  glazings  or  a i r change  t a b l e s c a n a l s o be u p d a t e d .  GENERAL USER HINTS In  normal use you would p r o b a b l y  l o a d GREENDAT, a n s w e r t h e  q u e s t i o n s , l o a d GREENSIM, make a few c h a n g e s a n d r e c a l c u l a t i o n s ,  1 40  and  finally  look  at  required.  p r i n t o u t t h e v a l u e s p a g e . I n some c a s e s a  t h e energy  balance  Use M u l t i p l a n s areas  of  GREENSIM  example t o see t h e cash for  (g)oto  (n)ame  g e t t i n g around  (i)nstructions). Multiplan's  f l o w a n a l y s i s m i g h t be  have  single  (f)inancial  o r by t y p i n g :  analysis).  like  this.  letter  flow a n a l y s i s type: g n f  the spreadsheet  the s p r e a d s h e e t ,  cash  GOTO command t o a c c o m p l i s h  1  important  or  detailed  The  names. F o r  (this  stands  Instructions f o r  t h i s a r e given a t the t o p of  g n i  (that  i s , (g)oto  (n)ame  S i m i l a r l y t h e s e a r e a s c a n be p r i n t e d o u t u s i n g  PRINT command.  To p r i n t t h e m a i n " v a l u e s s c r e e n " t y p e : p p . The p r o g r a m already  s e t up t o p r i n t t h e v a l u e s s c r e e n . I f y o u w i s h t o p r i n t  out o t h e r a r e a s o f t h e s p r e a d s h e e t , PRINT  is  OPTIONS  command.  y o u must  use  Multiplan's  The most commonly p r i n t e d a r e a s o f t h e  s c r 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  cash-flow  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>.  Similarly  t h e energy  balances  can  be  printed  by r e p l a c i n g  " c a s h f l o w " i n t h e s e q u e n c e a b o v e by one o f t h e v a r i a b l e s following  table:  VARIABLE NAME  REPRESENTS  refebalance  Reference  esgebalance  Energy  cashflow  Cashflow  values  Values  Certain reloading  i n the  variables  on  Greenhouse Energy  S a v i n g Greenhouse Energy  simulation.  that  typically  Balance  Table Screen  GREENSIM  GREENDAT: a r e a , a g e , a n d  variables  Balance  cannot growing  a r e n o t changed  be c h a n g e d w i t h o u t season. during  These a r e a  single  141  When r e c a l c u l a t i n g at  a time. Also write  t h e v a l u e s page a f t e r easier  to interpret  GREENSIM t r y c h a n g i n g o n l y one down r e s u l t s  every  frequently  recalculation.  the r e s u l t s  afterwards.  or else  This  will  variable p r i n t out make  i t  142  TABLE I : ENERGY SAYING MEASURES INCLUDED IN THIS STUDY INSTALLED COST ($/M)  ENERGY SAVING MEASURE  $19.00  1• Root Zone Heating 2. Stack Heat Recover  12,000 e a  3. Infra-red Heating  21.00  4. Microcomputer Control  20,000 e a  POSSIBLE SIDE EFFECTS  ANNUAL ENERGY SAVED  3  15%  2  12%  2  25% 4  Uneven heat, f r u i t s e t problems High maint, cost, low operating temp. 2  Less l i g h t , f r u i t s e t problems  12%  3  D i f f i c u l t to master  5. Sealing Glass Laps  8.50  20%  1  High humidity  6. Second Layer of Glazing  8.00  40%  1  Less l i g h t , high humidity  7. North Wall Insulation  4.00  3  10%  1  Less light  8. Meter Height Wall Insulation  4.00  3  10%  1  9. Thermal Curtains  30.00  1  35%  3  Less l i g h t , high humid, maint. probs.  10.Heat Storage  17.OO  25%  3  High cost, maintenance unproven  1  1  5  —  badger 1979, Blom 1982, Bryenton 1983, V r i v a 1984, Staley 1983. 2  3  5  COMPUTER GREENHOUSE ENERGY SAVING SIMULATION FOR:John  *•* ENERGY BcWJN© MEASURE© *•* O URhfaafc r^duce-Cil©**;  Kl^sl 1I rts-u-1 tanfemefce r i h.£u 1 t n er maj c: W t& i n-5  h e ^ t s i or-age  O O  J D O 1  0  o  A r e a and Age' ©lazing Wade Of •SXaxiog an Roo-f F u e l Typ-e Lte-e-d Crap Gr'tJlMtt .Jirfcer&st R-at-eFuel  Q.  Farmer  G l a s s 3 -ram N a t u r a l Gas Tomato  eswral at t o n  I n * 1st: i-on R a t e  T&y.  Loan AfttfJurtfe « Loan Interest- ; Crop-Price Y i e l d ftaae&/yr3  97, 7% 30X. $1-1-.5-4  ** "INDICATORS AND RESULTS * * Annual Loan Payment: *S„47r4.7B $0. O O I f t s - t A l l e d C o s t i C h a n g e i n S a l e s / y r .: $0. O O •:Mtfi:nte™a'Pee::$Vyrt Annual Energy S a v i n g $2,519.71 F u e l Cost * / r w ? O. 00-40490/' Net P r e s e n t V a l u e : $12,681.21 HEATLOSS SUMMARY Old House Break-Even Year : Heat C o s t $/-ft2 $1.03 •leating f r o m month 2 t o month 10: T o t L o s s e s MJ/m2 2,736 Figure 1: Values Screen Display  1984  35' year's''01 ti'  mmmmmm 0-ay  17  Temp,  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  APPENDIX F: USER'S CHECKLIST  143  1 44  G R E E N S I M v e r s i o n 1 .0 d r a f t c o p y Copywrited by B a r r y S h e l l , S e p t e m b e r USER C H E C K L I S T  1.  Insert  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  3.  disk  T y p e : on (selects  in drive  1984  A.  load)  <return> (o)ption:  (n)o auto r e c a l c u l a t i o n )  4.  Type: t 1 greendat <return> ( ( t ) r a n s f e r s a n d ( l ) o a d s t h e GREENDAT d a t a e n t r y p r o g r a m ) D i s k s m i g h t h a v e t o be swapped i n o r d e r t o l o a d GREENDAT.  5.  I f necessary press of t h e p r o g r a m . YOUR NAME: press the <return>  6.  <Next  <Next  Unlocked  key once  UNITS: You t y p e : ' f e e t ' o r ' m e t e r s ' d e p e n d i n g what w i l l use f o r the greenhouse dimensions. Cell>  the  start  name.  8.  Unlocked  t o move  your  Press  <Next  Cell>  and type  to find  7.  press  Unlocked  Cell>  t o the next  question.  units, you  9.  Again  .  10.  Enter wall height. This ground t o the gables.  11.  P r e s s <Next U n l o c k e d C e l l > a n d 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 in this manner until you h a v e come t o t h e e n d . F u r t h e r d i s c u s s i o n a n d e x p l a n a t i o n o f t h e s e q u e s t i o n s c a n be found in the User's Manual.  12.  After filling i n a l l t h e b l a n k s p r e s s <Cancel> command mode. T h e n 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)es)  13.  To l o a d GREENSIM, t h e s i m u l a t i o n m o d e l , t y p e : t 1 greensim <return> D i s k s w a p p i n g may b e n e c e s s a r y . I t w i l l t a k e Greensim 2 to 3 minutes to load and recalculate the spreadsheet.  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 . U s e t h e " a r r o w - d i r e c t i o n " k e y s t o move around the screen and s i m p l y t y p e i n new v a l u e s w h e r e d e s i r e d . I t i s recommended t h a t y o u 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 point) to have the spreadsheet recalculated. After approximately o n e m i n u t e new r e s u l t s w i l l h a v e a p p e a r e d .  i s the height  of the w a l l  from  the  to enter  145  16.  Repeat  17.  To v i e w o t h e r p a r t s o f GREENSIM t y p e : ( ( g ) o t o (n)ame (i)nstructions) The i n s t r u c t i o n s for moving about 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 balance (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 (V)ariables and (Y)ield  18.  steps  14  and  15  as  many  times  as g  necessary. n  I  the  spreadsheet  are  To print parts of the 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 the main v a l u e s screen type: pp T o 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 <return> <return>. To print 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 > <return>.  For  more  information  see  the  user's  manual.  

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.831.1-0096280/manifest

Comment

Related Items