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Thermal Effects of UBC Webber House Hydronic Hot Water Heating Wang, Haifeng Apr 22, 2016

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 UBC Social Ecological Economic Development Studies (SEEDS) Student ReportHaifeng WangThermal Effects of UBC Webber House Hydronic Hot Water HeatingCEEN 596April 22, 201614142116University of British Columbia Disclaimer: “UBC SEEDS Program provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or a SEEDS team representative about the current status of the subject matter of a project/report”.       Thermal Effects of UBC Webber House Hydronic Hot Water Heating Haifeng Wang       CEEN 596 April 22, 2016   1  Executive summary  The new Webber House development in Wesbrook Village for UBC Properties Trust consists of a six story, 4,233 m2 residential building with 36 suites over two parking levels. The energy model for this building was established to explore the optimal thermostat temperature range with the ultimate goal of saving energy and achieving occupants’ thermal comfort. Besides, the energy consumption end use was estimated.  The energy model was developed using Sketch up, Open studio and energy plus with ASHRAE 90.1-2010 Energy Standard.  Based on the results of the energy model, Scenario A not only can achieve highly thermal comfort for occupants, but also can provide up to 51% energy saving compared with Scenario B (upper bond).  Future work needs to be focused on the energy model adjustment. Some factors such as infiltration rate, lighting power density would have some impacts on heating consumption. Thus, to make the energy model more accurate, these numbers needs to be verified after operation.    2  Table of Contents 1. Introduction .................................................................................................................................... 5 1.1 Background ................................................................................................................................. 5 1.2 Purpose and Objectives .......................................................................................................... 6 1.3 Literature Review ..................................................................................................................... 7 2. Modelling Methodology .............................................................................................................. 9 2.1 Modelling Software .................................................................................................................. 9 2.2 Building Envelope ................................................................................................................. 10 2.2.1 Building orientation ............................................................................................................. 10 2.3 Space Types ............................................................................................................................. 12 2.3.1 Energy standard template .................................................................................................. 12 2.4 Thermal Zones ........................................................................................................................ 14 2.5 HVAC System ........................................................................................................................... 15 2.6 Climate Data............................................................................................................................. 18 3. Simulation ..................................................................................................................................... 19 3.1 Scenario A ................................................................................................................................. 20 3.1.1 Inputs ......................................................................................................................................... 20 3.1.1.1 Internal Loads .................................................................................................................... 20 3.1.1.2 Schedules ............................................................................................................................. 21 3.1.2 Results ........................................................................................................................................ 25 3.2 Scenario B ................................................................................................................................. 27 3.2.1 Inputs ......................................................................................................................................... 27 3.2.1.1 Internal Loads .................................................................................................................... 27 3.2.1.2 Schedules ............................................................................................................................. 27 3.2.2 Results ........................................................................................................................................ 28 3.3 Scenario C ................................................................................................................................. 29 3.3.1 Inputs ......................................................................................................................................... 29 3.3.1.1 Internal Loads .................................................................................................................... 29 3.3.1.2 Schedules ............................................................................................................................. 30 3.3.2 Results ........................................................................................................................................ 31 3.3.3 Scenario C results and WE modelling results comparison .................................... 32 3.4 Nobel House ............................................................................................................................. 33 3  3.4.1 Comparison among Nobel House, Scenario A, Scenario B and WE modelling results 34 4. Sensitivity Analysis .................................................................................................................... 35 4.1 Lighting ...................................................................................................................................... 35 4.2 Infiltration rate ....................................................................................................................... 36 5. Scenario A feasibility analysis ............................................................................................... 37 5.1 Thermal comfort analysis................................................................................................... 38 5.2 Energy saving analysis ........................................................................................................ 40 6. Conclusion ..................................................................................................................................... 41 7. Future Work ................................................................................................................................. 42 8. Acknowledgement ..................................................................................................................... 43 9. References ..................................................................................................................................... 43  Table of Figures   Figure 1 UBC LOT 3 Location ............................................................................................................. 5 Figure 2 Model top view .................................................................................................................... 11 Figure 3 Model rendering by space types .................................................................................. 14 Figure 4 Typical unit type H floor plan (left); Model rendering by thermal zones (right) .............................................................................................................................................. 15 Figure 5 Radiant hot water loop .................................................................................................... 16 Figure 6 Ventilation loop .................................................................................................................. 17 Figure 7 Climate data ......................................................................................................................... 18 Figure 8 Typical schedule set for apartment ............................................................................ 21 Figure 9 Scenario A Occupancy and heating setpoint schedules ...................................... 24 Figure 10 Annual energy consumption end use of scenario A ........................................... 25 Figure 11 Annual energy consumption end use of scenario A for dwelling spaces only .................................................................................................................................................. 26 4  Figure 12 Scenario B heating setpoint schedule ...................................................................... 27 Figure 13 Annual energy consumption end use of scenario B ........................................... 28 Figure 14 Scenario C heating setpoint schedule ...................................................................... 30 Figure 15 Annual energy consumption end use of scenario C ........................................... 31 Figure 16 Sensitivity analysis of lighting .................................................................................... 35 Figure 17 Sensitivity analysis of infiltration rate .................................................................... 36 Figure 18 January 1st Hourly Operative Temperature Simulation ................................... 38 Figure 19 Relative humidity simulation ..................................................................................... 39 Figure 20 Thermal comfort analysis ............................................................................................ 40 Figure 21 Future work process ...................................................................................................... 42 Table of Tables  Table 1 Window to wall ratio .......................................................................................................... 12 Table 2 Space types ............................................................................................................................. 13 Table 3 Internal loads of scenario A ............................................................................................. 20 Table 4 Annual end use and energy use of scenario A .......................................................... 25 Table 5 Annual end use and energy use of scenario B .......................................................... 28 Table 6 Internal loads of Williams Engineering simulation ................................................ 29 Table 7 Annual end use and energy use of scenario C ........................................................... 31 Table 8 Comparison between scenario C and WE modelling ............................................. 32 Table 9 EUI comparisons .................................................................................................................. 34 Table 10 Sensitivity analysis ........................................................................................................... 37 Table 11 Energy saving analysis .................................................................................................... 41  5  1. Introduction 1.1 Background The new building Wesbrook Lot 3 Webber House (formerly Lot 45 Village Green) will be a 6-storey 4,233 m2 Faculty/Staff Rental Housing in Wesbrook Place with 36 units on levels 1-6 ranging from 1 bedroom units to 4 bedroom units.  (Nine types of units total). There will be two levels of underground parking. Levels P1 & P2 are of concrete construction and levels 1-6 are of wood frame construction. This project is located in Wesbrook Village at 3388 Webber Lane and now is still being processed and construction has not yet begun.  Figure 1 UBC LOT 3 Location The new building will have a hydronic hot water heating system serving living space connected to district energy system being constructed by Corix. The spaces such as, the lobby, corridors and stairs will be served by electrical force flow heater. The heating water supply temperature will decrease from160 °F (71°C) (the 6  temperature from district energy system) to 120 °F after crossing the heat exchanger to pre-heat heating water return from 120°F to 140°F for further domestic hot water supply use. The detailed hot water schematic is attached as appendix A.  There will be 6 radiant heating risers serving 36 dwelling units (9 types) through the building. The capacity of radiant heating system is 582400 BTU/HR, and 159 BTU/HR/M2). The system was designed based on the worst scenario case, being the coldest day of the year at night with no heat input from any other sources (solar, people, equipment, etc.).Therefore, the actual condition would be better when internal loads, solar gains are considered. The detailed radiant heating riser schematic is attached as appendix B.  1.2 Purpose and Objectives The purpose of this project is to establish the optimal thermostat temperature range to achieve energy saving while providing occupants’ comfort. In order to achieve the objectives, an energy model was developed using Sketch Up, Open Studio and Energy Plus. To verify the reliability of the energy model, the actual building operation data from UBC Lot 22 Nobel House which has a similar radiant heating system was introduced. This study contains the following objectives:  Review design drawings and understand the mechanical system  Understand the effects of building envelope and architecture   Note all factors as designed that impact the temperature level (such as materials, building orientation, internal loads, flow rate, etc.)  7   Establish optimal thermostat temperature range to save energy and achieve occupants’ thermal comfort    Simulate the annual energy consumption end use(not include domestic hot water), and compare with the actual operation data of Nobel House  Develop the sensitivity analysis to verify the effects from lighting and infiltration rate on heating consumption 1.3 Literature Review To research the thermal effects of hydronic hot water radiant floor, the location of building, the building exposure, internal loads, building construction materials, water flow rates, supply and return temperature, average heat load flux, indoor room temperature and other parameters need to be taken into account. In terms of pipes, previous research shows that pipe type, diameter and the number of pipes do not have remarkable effect on radiant floor heating system performance. The more important design parameters are thickness and type of the cover due to dominance of radiation.(Sattari & Farhanieh, 2006) The spacing between pipes also affects the thermal performance. The average temperature of the floor surface increases with the decrease of pipe spacing, and the average heat flux surface of the floor would increase.(Du, 2014) For this project, the structure of living floor is wood frame. A research shows that the latent heat of the wood frame flooring is better than that of PVC flooring.  The good latent heat capacity would contribute to maintaining a relatively high temperature for a long period once heating source is removed. (Seo, Jeon, Lee, & Kim, 2011) 8  Low temperature radiant system performance can also be affected by other parameters. A recent study shows that low temperature radiant model is sensitive to both construction parameters and system parameters. These input parameters include:   Specification of the radiant system set point temperatures  Scheduling of internal heat gains  Specification of building element thermal properties Energy plus can accurately predict low temperature radiant performance once these parameters can be accurately specified. (Chantrasrisalai, 2001)  To optimize thermostats’ temperature setpoint and energy saving, not only the standard in accordance with ASHRAE needs to be met but also operative temperature, floor surface temperature, radiant temperature asymmetry and control system needs to be considered. Operative temperature can be simply approximated with average air and mean radiant temperature, and they are equally important in terms of thermal comfort.  In the international standards, the recommended maximum floor surface temperature for heating is 29°C (84°F) in the occupied zone for rooms with sedentary and/or standing occupants wearing normal shoes.(Frank & Wright, 2002) Another research found that a night setback control strategy can be utilized as a means of saving energy even with the thermal mass to reheat. (Good, Ugursal, & Fung, 2005) In this project, the optimal thermostats’ temperature setpoint was developed to both satisfy the occupants and to save energy.  9  2. Modelling Methodology 2.1 Modelling Software  The energy model was created using Sketch Up (version 2016), Open Studio (version 1.11.0) and Energy Plus (8.5.0). Sketch Up is a 3D modelling computer program for a wide range of drawing applications such as architectural, interior design, mechanical engineering. Open Studio is  cross-platform (Windows, Mac, and Linux) collection of software tools to support whole building energy modeling using Energy Plus and advanced daylight analysis using Radiance. Energy Plus is a whole building energy simulation program that engineers, architects, and researchers use to model both energy consumption—for heating, cooling, ventilation, lighting, and plug and process loads—and water use in buildings. Its development is funded by the U.S. Department of Energy Building Technologies Office. However, Energy Plus is code based, while Open Studio has a friendlier interface. Users can easily assign and design schedules, HVAC systems, and constructions, space types in Open Studio by dragging components or modules from the library. Besides, Open Studio can be easily plugged into Sketch Up, allowing users to quickly create geometry which is needed for simulation in Energy Plus, and therefore, there is a better interaction between Sketch Up and Open Studio.  From new construction program’s energy modelling guideline released by BC Hydro, they required to use the following software to run the simulation for hydronic radiant heating: a. IES VE and Energy Plus 10  b. Others: ESP-r, TRANSYS/TRNFLOW-acceptable, but not used in B.C. Thus, Energy Plus as a professional building modelling software can ensure the accuracy. (BC.HYDRO, 2016) The simulation was based on a detailed set of inputs that includes the following:  Building envelope (building orientation, wall and window materials, window to wall ratio)  Spaces types  Internal loads(Occupants, lighting, plug loads) and schedules   Thermal zones   HVAC system  Heating setpoint schedules   Climate Data 2.2 Building Envelope  2.2.1 Building orientation  The building orientation has an impact on the solar gain. The units faced to south have more solar gain than the units faced to north. The north axis of the new UBC LOT 3(Webber House) is 30 degree, read from architectural drawings.  11   Figure 2 Model top view 2.2.2 Building materials  Different building materials have different thermal resistance. The higher the R-value is, the better the building insulation will achieve. Therefore, building materials decide the how much heat the building can keep within the units and the how much heat will loss through window, wall and roof to some extent. The building materials information was gained from architectural drawings and the construction contractors. For the roof construction, it was 4.5” rigid insulation roof, and R- value was 28.8 ft2·°F·hr/Btu; For the wall construction, it was 2 x 6 wood framed batt insulation wall, and R-value was17.4 ft2·°F·hr/Btu; For the glazing properties, low-e argon-filled double glazed vinyl windows were applied, the U-value was 0.266 BTU/(h °F ft2). The window to wall ration is listed in the table below.    North 12  Table 1 Window to wall ratio Description Total (%) North (%) East (%) South (%) West (%) Gross Window-Wall Ratio 22.36 23.4 21.21 24.61 19.25 Gross Window-Wall Ratio (Conditioned) 30.08 32.17 26.87 35.08 24.9  2.3 Space Types  2.3.1 Energy standard template   Before a space type was assigned to each model block, an energy standard template needed to be decided first.  Open Studio has three ASHRAE energy standard templates. They are ASHARE 189.1-2009 (Standard for the Design of High-Performance Green Buildings), ASHARE 90.1-2007(Energy Standard for Buildings except Low-Rise Residential Buildings) and ASHARE 90.1-2010.  Based on the energy modelling guideline from BC Hydro, ASHRAE 90.1 2010 was set in this simulation study. (BC.HYDRO, 2016) The reason we need to choose the energy template first is that once the template is confirmed, occupants, lighting, internal loads schedules will be assigned to each space type automatically by Open Studio, and these schedules are in line with the energy standard chosen. 2.3.2 Space types  In Open Studio, there are several building types, and under each building type, there are different space types.  13  Table 2 Space types Building type Space type Rendering colour Midrise Apartment Apartment Blue Midrise Apartment Corridor Red Office Stair Pink Large Hotel Lobby Purple Large Hotel Storage(Parking) Yellow  From the table above, it can be found that there are Midrise Apartment, Office and Large Hotel building types. The reason is that in Open Studio, there are only three space types under the midrise apartment building type, which are apartment, corridor, and office. For other space types, it is needed to find them under other building types. Therefore, stair was found under office, lobby and storage under large hotel.  In open studio there is no space type for parking, so the most similar space type is storage, standing for parking space in this project. As mentioned before, once energy standard template and space types are decided, Open Studio will assign occupants schedule, lighting schedule, internal equipment schedule, and infiltration rate.  For parking space, all schedules can be adjusted manually to match the actual situation.   14   Figure 3 Model rendering by space types 2.4 Thermal Zones  In energy modelling process, the most critical simulation objects are thermal zones.  Thermal zones are required to analyze the heat loss, heat gain, air temperature, mean radiant temperature, operative temperature, and relative humidity.  Moreover, thermal zones as foundations are essential to establish the HVAC system.  There were dominantly two kinds of thermal zones in this study, namely the conditioned space and the unconditioned space. For open spaces such as parking, corridors, lobby and stairs, they were considered as the unconditioned space, while all blocks under apartment space types were considered as the conditioned space.  15     Figure 4 Typical unit type H floor plan (left); Model rendering by thermal zones (right) Figure 4 shows that how thermal zones were assigned. The left graph is a typical unit type H floor plan. As shown, there are four areas in unit H, three bedrooms, one living/dining room. Each area has a radiant heating layout under floor slab, and each area is exposed to different orientation. To simulate more accurately, four conditioned thermal zones were created to match four areas in unit H. However, in the mechanical design, there were only two thermal zones, one for all bedrooms, and the other for dining/living space.  For all public space blocks, they were considered as one unconditioned space. For two parking spaces, they were considered as two unconditioned spaces as well. After creating all thermal zones, it showed like the right graph of Figure 4. There were 127 thermal zones in total, where 124 thermal zones were conditioned and 3 zones were unconditioned.   2.5 HVAC System  HVAC systems decide how to provide thermal comfort and acceptable indoor air quality. Different systems have different ways to transfer heat. In this project, a 16  hydronic radiant heating system was designed. The benefits of radiant floor heating are listed as follows:  Provide high level of thermal comfort(radiant systems engage with the body’s dominant means of thermal transfer)  Quiet operation  Can be easily zoned   Pipes are much smaller than ducts of forced air heating systems  No interference with furniture placement  One major drawback of radiant floor heating systems is slow response time. It needs to take a while to warm up the space and reach the setpoint.   Figure 5 Radiant hot water loop Setpoint manager 17  As shown on Figure 5, on the supply side, hot water came from district heating system. Before hot water flowed into the building, a setpoint manager can control the incoming flow temperature to maintain and the temperature at 50 °C constantly. After entering into the building, hot water was distributed to each conditioned thermal zone. The return water went back to district heating center, forming a closed loop.   Figure 6 Ventilation loop For ventilation design, each unit had a supply fan that sucked outdoor air from balcony and blew into rooms.  However, it is very difficult to quantify the natural ventilation impact and it depends on personal behavior as well. Thus, to simply the simulation, a ventilation loop was introduced (Figure 6). The outdoor air passed 18  through a cooling/heating coil to be cooled down/warmed up to 20 °C constantly. The loop did not have any function for heating or cooling spaces. It would only supply 20°C air to maintain the ventilation requirements.  Once all HVAC systems were designed, it was needed to go back thermal zones to assign zone equipment (here was radiant heating), and air loop to each thermal zone and link them to the HVAC loop. By doing that, a connection between thermal zones and HVAC system was established.  2.6 Climate Data  Since Open Studio is designed based on Energy Plus, it has access to download worldwide weather files on the Energy Plus site.  According to ASHRAE climate zones, Vancouver is in climate zone 5C. (Ab, 2007) Design days can also be obtained through the library.   Figure 7 Climate data  19  3. Simulation  For this study, there were three scenarios to analyze radiant heating thermal effects.   Scenario A: The ideal heating setpoint schedule with ASHRAE Energy standard 90.1-2010 (Base case)  Scenario B: The upper bond heating setpoint schedule with ASHRAE Energy standard 90.1-2010  Scenario C: Using Williams Engineering modelling assumptions  The only difference between scenario A and scenario B was the heating setpoint. Scenario A was aimed to see whether the ideal heating setpoint could provide occupants’ thermal comfort and achieve energy saving. Scenario B was aimed to set the upper bond of energy consumption, and compare the difference of energy consumption between scenario A and B.  For scenario C, Williams Engineering also did an energy modelling for UBC Lot 3, but for a different purpose. They were aimed to provide the necessary documentations to meet REAP Energy and Atmosphere Credit 1.10. Besides, they used EE4 (version 1.7 build 2), a program developed by Natural Resources Canada (NRCan) for energy modelling. The intent of scenario C was to use inputs from WE modelling but run in the model by Energy Plus to see whether the result could match each other or not.      20  3.1 Scenario A  3.1.1 Inputs  3.1.1.1 Internal Loads   Internal loads consist of occupants’ activity, interior lighting power density and plug load. The numbers are listed in the Table 3 below. All these numbers are in line with ASHRAE 90.1-2010 energy standard based on space types assigned. The only number that was changed manually was interior lighting power density of storage (parkade). Open studio assumed 6 W/m2 for storage space, but it was quite high for parkade. According to ASHRAE energy standard 90.1-2010, the power density of parkade is 2 W/m2. (BC.HYDRO, 2010) Table 3 Internal loads of scenario A Internal Loads   Occupants (people/m2) Interior Lighting Power Density (W/m2) Plug loads (W/m2) Apartment  0.028 4.090286 3.875009 Corridor N/A 7.104181 N/A Stair  N/A 7.427098 N/A Lobby  0.33 11.409745 N/A Storage(Parkade) N/A 2 N/A  21  3.1.1.2 Schedules  There were various schedules in the simulation such as occupancy schedule, people activity schedule, lighting schedule, equipment schedule (plug loads schedule), and infiltration rate.   Figure 8 Typical schedule set for apartment Open studio can automatically assign these schedules and loads once the energy template and space type are chosen.  However, these schedules and loads can also be adjusted manually like internal loads to match the real operation situation.  22   (a)Apartment space weekdays occupancy schedule  (b)Apartment space weekdays heating setpoint schedule 23    (c)Apartment space weekends occupancy schedule 24  (d) Apartment space weekdays heating setpoint schedule Figure 9 Scenario A Occupancy and heating setpoint schedules From Figure 9, one set schedule was assigned for weekdays, another for weekends. The schedule type between occupancy and heating setpoint was different. Occupancy schedule used friction type, 1 standing for 100% occupied, 0.2 standing for 20% occupied, while heating setpoint schedule used temperature type, 21 standing for 21°C. The ideal setpoint range for Scenario A during weekdays was when units were occupied, it maintained the temperature at 21°C, and when units were unoccupied, it dropped the temperature down to 16 °C. Furthermore, from previous literature review, a 3°C setback could achieve energy saving when people sleep at night. For weekends, it is assumed that people would like to stay at home, and therefore the temperature maintained at 21°C except for sleeping time.            25  3.1.2 Results    Figure 10 Annual energy consumption end use of scenario A   Table 4 Annual end use and energy use of scenario A End Use Annual Consumption (kBtu) Energy use  Heating 395,581 District heating Cooling 0  Interior Lighting 282,990 Electricity  Interior Equipment 282,535 Electricity Fans 48,832 Electricity Pumps 171 Electricity Total  1,010,109 Mixed   Heating39%Cooling0%Interior Lighting28%Interior Equipment28%Fans5%ENERGY CONSUMPATION END USE26  As shown on the Figure 9 and Table 4, heating accounted for 39% of total energy consumption. Interior lighting and interior equipment were the same, 28%. The reason why lighting was high is because when run the simulation, only heating in apartment spaces was considered, but lighting of units, public spaces and parking spaces were all taken into account.  Another simulation was run to analyze the energy consumption end use for dwelling units(apartment spaces) only.   Figure 11 Annual energy consumption end use of scenario A for dwelling spaces only From Figure 11, heating increased from 39% to 47%, and lighting decreased by 11%. This pie chart is closer to common energy consumption end use of residential buildings in North America, however, it would be more accurate if domestic hot water heating can be  taken into consideration.  Heating47%Cooling0%Interior Lighting17%Interior Equipment31%Fans5%ENERGY CONSUMPATION END USE27  3.2 Scenario B 3.2.1 Inputs  3.2.1.1 Internal Loads   Internal loads of scenario B could refer to that of scenario A.  The only difference between scenario B and scenario A was heating setpoint.   3.2.1.2 Schedules  Figure 12 Scenario B heating setpoint schedule In scenario B, to define the upper bond of energy consumption, 23 °C was assigned 24/7 for the whole year.  28  3.2.2 Results   Figure 13 Annual energy consumption end use of scenario B Table 5 Annual end use and energy use of scenario B End Use Annual Consumption (kBtu) Energy use  Heating 599,864 District heating Cooling 0  Interior Lighting 282,990 Electricity  Interior Equipment 282,535 Electricity Fans 48,794 Electricity Pumps 66 Electricity Total  1,214,249 Mixed   It can be noted that from Figure 13 and Table 5 the increase of heating setpoint did have a huge impact on heating consumption. In scenario B, the setpoint went up to Heating50%Cooling0%Interior Lighting23%Interior Equipment23%Fans4%ENERGY CONSUMPATION END USE29  23°C, and there was a growth of 204,283 kBtu for district heating. It was approximately 51% more of heating consumption than that of scenario A. 3.3 Scenario C   3.3.1 Inputs  3.3.1.1 Internal Loads   The aim of scenario C was to use the inputs from Williams Engineering’s model to run the simulation in the model established by Energy Plus, and compare the difference between simulation results by Energy Plus and results from WE report using EE4.  Table 6 Internal loads of Williams Engineering simulation Internal Loads   Occupants (people/m2) Interior Lighting Power Density (W/m2) Plug loads (W/m2) Apartment  0.028 7 2.5 Corridor N/A 7 N/A Stair  N/A 7 N/A Lobby  0.33 7 N/A Storage(Parkade) N/A N/A N/A  30  Williams Engineering’s model assumed one space type only, and therefore all spaces were assumed the same number for interior lighting power density of 7 W/m2.  For interior equipment, 2.5 W/m2 was assigned to apartment spaces.  3.3.1.2 Schedules  The indoor heating setpoint of Williams Engineering’s model was 72°F (22.2°C)   Figure 14 Scenario C heating setpoint schedule   31  3.3.2 Results   Figure 15 Annual energy consumption end use of scenario C Table 7 Annual end use and energy use of scenario C End Use Annual Consumption (kBtu) Energy use  Heating 537,062 District heating Cooling 0  Interior Lighting 341,157 Electricity  Interior Equipment 182,284 Electricity Fans 49,040 Electricity Pumps 47 Electricity Total  1,109,590 Mixed   Because of a lower heating setpoint and a higher lighting power density than those of scenario B, the results from Figure 15 and Table 7 show that scenario C consumed less energy for heating, but more for lighting. In this case, lighting occupied 31% of Heating48%Cooling0%Interior Lighting31%Interior Equipment16%Fans5%ENERGY CONSUMPATION END USE32  total energy consumption end use, being a little high. The reason leading to this result was that in Williams Engineering’s model, all spaces were assigned one lighting density of 7 W/m2, but in ASHRAE standard, for example, 2 W/m2 is the minimum requirements in parking spaces. The difference of 5 W/m2 would make the discrepancy of end use.   3.3.3 Scenario C results and WE modelling results comparison   The comparison of two simulation results is shown as Table 8 below.  Table 8 Comparison between scenario C and WE modelling  Scenario C WE Modelling Lighting (kWh) 99,983 117,556 Plug Loads(kWh) 53,422 31,902 [Heating(kWh) 157,397 240,142 Fans(kWh) 14,372 9,717 EUI for heating(mj/m2) 154.76 204.23 EUI for heating(kWh/m2) 43 56.7  All results of WE modelling were from the REAP energy modelling report prepared by Williams Engineering. It can be found that even if the same inputs were assigned, the simulation results cannot match each other. EUI (energy use intensity) for heating of WE modelling is much higher than EUI of Scenario C by 31%. Here are some reason that could contribute to this gap.  33  1. When the geometry was established, the difference had already been induced in the different models. Besides, each setting step would create some differences as well. These slightly differences kept accumulating, leading to the gap.  2. Two energy modelling software (Energy Plus and EE4) have their own calculation methods to run the simulation, which means the key factors in each calculation process would be different.  3. Different simulation processes would cause the result difference. In energy plus, space types, energy standard, thermal zones must be assigned before running simulation, while EE4 works in a different way. These possibilities would have combined effects, contributing to the significant gap between two simulations.  3.4 Nobel House  Nobel house is also located at Wesbrook Place, having a similar hydronic radiant heating system like Webber House.  It is a 6-story residential building and went into operation since 2015.The operation data of Nobel House can be collected.  Because of the same location, building function and similar heating system, EUI of Webber house should be close to EUI of Noble. To verify the reliability of Webber House energy model, it is worth comparing EUI among Nobel house, scenario A, scenario B, scenario C and WE modelling results. 34  3.4.1 Comparison among Nobel House, Scenario A, Scenario B and WE modelling results  Table 9 EUI comparisons  EUI for heating (mj/m2) EUI for heating (kWh/m2) Scenario A  113.86 31.63 Scenario B 172.86 48.01 Scenario C 154.76 43 WE modelling 204.23 56.7 Nobel House  154 42.78  As shown in Table 9, EUI of Noble House is 42.78 kWh/m2, being calculated from the operation data. Once 42.78 kWh/m2 is set as a baseline data, it can be noted that the EUI of scenario A and scenario B can cover the baseline data, which means in the future operation, actual EUI of Webber is likely to be in the range between scenario A and B.  Moreover, compared with Nobel House, EUI of WE modelling is much higher, while scenario C is close enough.   As a conclusion, scenario A consumed the least energy, and scenario B was a reasonable upper bond. EUI of WE modelling was too high, perhaps because it considered the worst situation, and it established the model for a different purpose, achieving REAP credits.   35  4. Sensitivity Analysis  To explore how lighting and infiltration rate would affect the heating consumption, a sensitivity analysis is introduced.  4.1 Lighting   To analyze the sensitivity of lighting, three cases were assumed.  1. Scenario A (Base case) 2. No lighting(compared to base case, using 100% less lighting ) 3. LED(compared to base case, using 30% less lighting)  Figure 16 Sensitivity analysis of lighting It can be seen by Figure 16, lighting dropped by 100%, and heating would increase by 25%.  For LED, 30% less lighting led to a 7.8% growth in heating. Using less 36  lighting means there is less internal gain as well, and therefore more heating is needed to offset the heat loss from lighting.  4.2 Infiltration rate  To analyze the sensitivity of infiltration rate, two cases were assumed.  1. Scenario A (Base case) 2. 130% infiltration rate of scenario A  Figure 17 Sensitivity analysis of infiltration rate It can be seen by Figure 16, 30% increase of infiltration rate caused an 18.5% growth in heating.  37  Table 10 Sensitivity analysis  EUI for heating (mj/m2) EUI for heating (kWh/m2) Percentage difference (%) Ratio Scenario A(Base case) 113.86 31.62   No lighting 143.03 39.73 +25% 0.25 LED 122.78 34.11 +7.8% 0.26 130% Infiltration rate 134.98 37.49 +18.5% 0.61  Ratio in Table 10 was calculated by the following equation: Ratio =𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑓𝑜𝑟 𝑙𝑖𝑔ℎ𝑡𝑖𝑛𝑔 𝑜𝑟 𝑖𝑛𝑓𝑖𝑙𝑡𝑟𝑎𝑖𝑜𝑛 𝑟𝑎𝑡𝑒𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒 𝑜𝑓 ℎ𝑒𝑎𝑡𝑖𝑛𝑔  Taking 130% infiltration rate as an example, ratio=30%/18.5%=0.61. The ratio reflects the degree of influence. The greater ratio is, the more influence degree will be. Therefore, compared with lighting, infiltration rate has a greater influence on heating consumption. In the other words, infiltration rate should be carefully assumed to make the simulation more accurate.  5. Scenario A feasibility analysis   As analyzed above, Scenario A had the lowest heating consumption. However, it still needs to be verified whether scenario A can provide occupants’ thermal comfort and achieve energy saving.   38  5.1 Thermal comfort analysis  To analyze thermal comfort, operative temperature and relative humidity for conditioned spaces are necessary.   Figure 18 January 1st Hourly Operative Temperature Simulation Energy plus can run the simulation for hourly operative temperature of each unit. In this case, January 1st was chosen because it was one of the coldest day in the whole year. If thermal comfort can be provided on this date, for other days tenants would feel comfortable as well. The average temperature for the occupied period (17:00-24:00) was approximately 22 °C after calculation.  19.52020.52121.52222.5231 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241.1 Hourly Operative Temperature Simulation39   Figure 19 Relative humidity simulation Not only operative temperature, but also relative humidity can be simulated by Energy Plus. (Figure 19) The average relative humidity of conditioned spaces was 38.5%. Once average operative temperature and relative humidity were calculated, thermal comfort can be analyzed by online tool.  40   Figure 20 Thermal comfort analysis As shown on Figure 20, once metabolic rate and clothing level were assigned, the tool generated a comfort zone (blue area) on the psychrometric chart based on ASHRAE 55-2013. After inputting operative temperature and humidity calculated before, the tool showed a red dot on chart as well. From the chart, it can be found that occupants would feel highly comfortable since red dot is in the middle of the comfort zone.  5.2 Energy saving analysis   41  Table 11 Energy saving analysis  Annual Energy Consumption For  Heating (kWh) Scenario A 115,933 Scenario B 175,802 Difference 59,869  Compared with the upper bond Scenario B, Scenario A can save 51% energy by simply decreasing the setpoint.  6. Conclusion  Based on the analysis above, it can be found that the heating setpoint had a significant influence on heating consumption.  Scenario A consumed the least among all simulations. Meanwhile, it provided high thermal comfort and achieved energy saving. Therefore, it is worth encouraging tenants to go with scenario A for their heating setpoint.  From sensitivity analysis, it should be noticed that infiltration rate does affect the thermal performance to a great extent. Thus, after operation, the actual situation, especially for infiltration rate, should be verified.    42  7. Future Work  Figure 21 Future work process Since scenario A is the best option for heating setpoint, next step is to encourage the potential tenants to set their thermostats’ setpoint with scenario A. After Webber house being in use, the operation data can be collected from building performance software. It is important to verify the model using actual performance data.  If the actual data cannot match the simulation results, the inputs need to be adjusted to match the real situation. The new model after adjustment can try some new scenarios, forming a cycle to improve the building performance.  Apart from that, adding domestic hot water heating and natural ventilation into energy modelling is also valuable to make the simulation more accurate and comprehensive.  Finally, the disadvantage of radiant heating is slow response time. Thus, it would be a progress if the time period reaching the setpiont can be simulated.  43  8. Acknowledgement   This research was supported by mentors as follows: CEEN 596 Instructor  Dr. Vladan Prodanovic Development Manager of Lot 3 Emer Byrne and Megan Pohanka Community Energy Manager Ralph Wells Project Manager  Liska Richer  Green Buildings Manager  Penny Martyn  9. References Ab, A. (2007). International Climate Zone Definitions, (cm), 1–4. BC.HYDRO. (2010). Reference Guide for Lighting Calculator, 1–25. BC.HYDRO. (2016). New Construction Program ’ s Energy modelling guideline, (March). Chantrasrisalai, C. (2001). Experimental Validation of the EnergyPlus Low-Temperature Radiant Simulation, 1–13. Du, Y. (2014). Feasibility Analysis of Radiant Floor Cooling and Heating System Applications. Applied Mechanics and Materials, 716-717, 428–430. http://doi.org/10.4028/www.scientific.net/AMM.716-717.428 Frank, A., & Wright, L. (2002). Radiant Floor Heating In Theory and Practice, (July), 19–24. Good, J., Ugursal, V. I., & Fung, A. (2005). SIMULATION STRATEGY AND SENSITIVITY ANALYSIS OF AN IN-FLOOR RADIANT HEATING MODEL Dalhousie University , Halifax , Canada, 341–348. Sattari, S., & Farhanieh, B. Ã. (2006). A parametric study on radiant floor heating system performance, 31, 1617–1626. 44  http://doi.org/10.1016/j.renene.2005.09.009 Seo, J., Jeon, J., Lee, J. H., & Kim, S. (2011). Thermal performance analysis according to wood flooring structure for energy conservation in radiant floor heating systems. Energy and Buildings, 43(8), 2039–2042. http://doi.org/10.1016/j.enbuild.2011.04.019  DOMESTIC HOT WATER SCHEMATIC2015.Nov.27Copyright. All rights reserved. Reproduction in whole or in part isprohibited. This drawing as an instrument of service is the propertyof the architect and may not be used in any way without the writtenpermission of this office.UBC Properties Trustfor3388 Webber LaneWesbrook Village,University of British ColumbiaSouth Campus, B.C.WILLIAMSRevisionsAugust 21, 2015 Issued For Preliminary CostingDATE:JOB NUMBER:DRAWN BY:REVISION NO.:33619.00TM4NOVEMBER 20151October 1, 2015 Issued For Foundation Permit2October 7, 2015 Issued For Class B Pricing3October 30, 20154 Issued For Structural Coord. - ParkadeNovember 23, 20155 Issued For CoordinationNovember 30, 20156 Issued For Full BP and Class A IFT from gradeRisers & SchematicsMechanicalN.T.S. M705Appendix ARADIANT HEATING RISERS2015.Nov.27Copyright. All rights reserved. Reproduction in whole or in part isprohibited. This drawing as an instrument of service is the propertyof the architect and may not be used in any way without the writtenpermission of this office.UBC Properties Trustfor3388 Webber LaneWesbrook Village,University of British ColumbiaSouth Campus, B.C.WILLIAMSRevisionsAugust 21, 2015 Issued For Preliminary CostingDATE:JOB NUMBER:DRAWN BY:REVISION NO.:33619.00TM4NOVEMBER 20151October 1, 2015 Issued For Foundation Permit2October 7, 2015 Issued For Class B Pricing3October 30, 20154 Issued For Structural Coord. - ParkadeNovember 23, 20155 Issued For CoordinationNovember 30, 20156 Issued For Full BP and Class A IFT from gradeN.T.S. M702Risers & SchematicsMechanicalAppendix B

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