Development and performance evaluation of a methodology, based on distributed computing, for speeding EnergyPlus simulation

Transcription

1 Development and performance evaluation of a methodology, based on distributed computing, for speeding EnergyPlus simulation Vishal Garg a*, Kshitij Chandrasen a, Jyotirmay Mathur b, Surekha Tetali a, Akshey Jawa a a Centre for IT in Building Science, International Institute of Information Technology, Hyderabad, India b Centre for Energy and Environment Malaviya National Institute of Technology, Jaipur, India Vishal Garg Head & Associate Professor, Centre for IT in Building Science, IIIT Hyderabad, India. vishal@iiit.ac.in Kshitij Chandrasena Student, B.Tech Final Year, IIIT Hyderabad, India. chandrasen@students.iiit.ac.in Jyotirmay Mathur Co-coordinator, Centre for Energy and Environment, Associate Professor, Mechanical Engineering Department Malaviya National Institute of Technology Jaipur, India jyotirmay.mathur@gmail.com Surekha Tetali Student, MS by Research, Centre for IT in Building Science IIIT Hyderabad, India. surekha.tetali@research.iiit.ac.in Akshey Jawa Student, MS by Research, Centre for IT in Building Science IIIT Hyderabad, India. aksheyjawa@ research.iiit.ac.in Corresponding Author: vishal@iiit.ac.in This paper presents an approach for speeding EnergyPlus simulations. The computing run time of an energy simulation depends on several variables and is directly proportional to the simulation RunPeriod. In the proposed approach, data parallelization is achieved by breaking an annual simulation into several segments of smaller RunPeriod, each handled by a separate computer/processor. The speed gain achieved by running 12 one-month RunPeriod segments in parallel as compared to single simulation of twelve months is between 3 to 6 times. Segmentation of simulation has resulted in minor deviations between the results obtained through segmented simulations and annual

2 simulations. Methods for reducing these deviations on annual and monthly basis are presented in this paper using twelve benchmark models each simulated for five cities. On annual basis, a maximum deviation of 0.06% was observed in cooling, heating, and lighting consumption. In a month-to-month comparison between the segments and annual simulation, the maximum deviation was 1.7% for heating and 0.8% for cooling. Keywords: EnergyPlus, energy simulation, simulation run time, parallel simulation, simulation speed up 1. Introduction There has been an increased effort among Architects, HVAC engineers and designers to implement energy conservation features in buildings. This has resulted in an increased use of energy simulation software in the design process. Energy simulation programs can help in achieving energy efficient and cost effective designs. EnergyPlus [1] is a new-generation building energy simulation program based on DOE-2 [2] and BLAST [3], with numerous added capabilities. EnergyPlus includes many innovative simulation capabilities such as time steps of less than an hour, modular systems and plant integrated with heat balance-based zone simulation, multizone air flow, thermal comfort, water use, natural ventilation, and photovoltaic systems. Though EnergyPlus has these innovative capabilities, its major limitation is that it runs slower than DOE-2. According to a study conducted by Tianzhen H., et al [4], at a 15-minute time step, EnergyPlus runs much slower than DOE-2.1E by a factor of 105 for a large office building to 196 for a hospital building. At a 60-minute time step, EnergyPlus still runs even slower than DOE-2.1E by a factor of 25 for the large office building to 54 for the hospital building. According to EnergyPlus run time analysis report [5] prepared by the Simulation Research Group, EETD, LBNL, simulation settings that can have significant impacts on EnergyPlus run time include the length of RunPeriod, Number_of_Timesteps_per_Hour for loads calculations, heat balance solution algorithm, solar distribution and reflection calculation algorithm, system convergence

3 limits, shadow calculation interval and the length of the warm up period. This analysis shows that the longer the run period, longer is the EnergyPlus run time. Some efforts to use parallel computing for reducing simulation time for a group of simulations have been reported. Zhang Y [6] developed a Java based tool to run EnergyPlus on parallel machines specifically for the parametric analysis where multiple design alternatives have to be analyzed simultaneously. GenOpt [7] is an optimization program, used to carry out the parametric analysis using multiple computers / processors. Both these tools help in speeding up the parametric simulations but do not address speeding individual simulation runs. In this paper, an approach that uses data parallelization paradigm has been proposed to increase the simulation speed of single simulation run. In this approach, annual simulation is segmented into smaller multiple simulations, each of which can run on a dedicated CPU in a computer cluster or on different cores on the same computer. This segmentation reduces the simulation run time and increases the speed of simulation. Segmentation of simulation results in minor deviations between the results obtained through segmented simulations and annual simulations. To achieve accurate results, effect of the number of warm up days and shadow calculation days were analyzed to arrive at different alternatives. This method was tested on 13 models and 5 cities covering various climatic conditions. Speed gains of 3.2x to 5.8x were achieved by running 12 one-month RunPeriod segments in parallel as compared to annual simulations. On an annual basis, a maximum deviation of 0.06% was observed in cooling, heating, and lighting consumption of buildings. In a month-to-month comparison between the segmented and annual simulation, this deviation in the worstcase scenario was 1.7% for heating and 0.8% for cooling energy consumption. 2. Approach

4 One parameter that significantly affects the runtime of a single simulation run is the RunPeriod of that simulation. RunPeriod is the object in EnergyPlus which contains several fields, including information on the begin date and end of the simulation. This information is assigned by the user in the EnergyPlus Input Data File (.idf). In the approach presented in this paper the annual simulation was divided into twelve segments (splitting the single annual.idf into 12 monthly.idf files) with smaller RunPeriod (monthly) that were then run in parallel. This monthly simulation took significantly less time in comparison to the annual simulation. These monthly simulations were independently run in parallel on several computers. Results of all the parallel runs were then collated. This results in increase in simulation speed. This approach is explained in Figure 1. In this study, simulations have been performed on a cluster of computers and the results of these segmented simulations were compared with the results of the respective months in the annual simulation. It was observed that segmentation of simulation resulted in minor deviations between the results obtained through segmented simulations and annual simulations. The causes of these deviations and the alternatives to reduce them are discussed in the following sections. Figure Deviations Observed When the annual simulation was segmented into twelve monthly simulations it was observed that there were some deviations in the monthly cooling and heating energy consumption of the segmented simulations as compared to the corresponding months of the annual simulation. This was observed in all the 65 cases (13 models x 5 cities) that were simulated (details given in Section 3- Simulation Runs). Table 1 gives the

5 percentage deviations in monthly heating and cooling for one of the 65 cases ( ElemSchool model for Chicago climate) Table 1 Besides the deviations observed on the monthly and annual values, large deviations were also observed in the hourly values of heating and cooling. Figure 2 shows the percentage deviations in the hourly values for cooling and heating energy consumption obtained in the segmented simulations with the corresponding hourly values of annual simulation of ElemSchool model using New York weather file. Figure2a Figure2b 2.2 Causes of Deviations The deviations discussed in the previous section were due to the following factors: (1) Mismatch between the day of week for the dates in segmented simulation and corresponding dates in annual simulation: The field Day_of_Week_for_Start_DayDay, can be set in the simulation model. The value given in this field can be used to override the day of the week indicated in the weather file. Valid days of the week (Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, and Saturday) can be entered. When weekdays are used, each subsequent day in the period will be incremented. If this field is filled with a valid day of the week, all the segmented simulations will take that day of the week as a static value for the Day_of_Week_for_Start_Day. This will result in different day of week for the same date in the annual simulation and the segmented simulation. This difference can further lead to a difference

6 in the number of total working days in the annual simulation and the sum of all working days in the segmented simulation, which will therefore affect the results. For example, if 1st of July is Monday in the annual simulation and the field Day_of_Week_for_Start_Day is set to Sunday, then the start day for the seventh segment (July) will be Sunday and will not match with the day of week in the annual simulation. Further, there will be four Sundays in the month of July in the annual simulation whereas in the segmented simulation for July there will be five Sundays. A difference of even one working day can cause a significant difference in the energy consumption for that month. (2) Inadequate warm up if the first day of segment is a holiday: EnergyPlus performs a warm up on the first day of simulation period in order to set the values of certain variables. Convergence of the simultaneous heat balance/hvac solution is reached when the criteria for either the loads or temperature is satisfied. In case of an annual simulation, the simulation starts on 1st January, and the warm up calculations are carried out for first day i.e. 1st January. However, for individual segments, the warm up takes place on the first day of the segment i.e. the first day of each month. In case, the first day of the month happens to be a holiday, then the warming up of the model for that segment will be done according to non-operational conditions. This will result in deviated results for the next few working days of that segment and hence affect the total monthly consumption. (3) Dates of shadow calculation in annual simulation not matching with the dates for segmented simulations: To determine the amount of solar radiation entering a building and to calculate the amount of cooling or heating load required to maintain the set point temperature, shadow calculations (sun

7 position, etc) are performed by EnergyPlus. By default, EnergyPlus performs these calculations for a frequency of 20 days throughout the RunPeriod. The shadowing Calculation_Frequency variables in the idf specify the number of days after which the next shadowing calculation period starts. The dates for which shadowing calculation is performed depends on this frequency. There can be deviations in the results of simulations if the shadowing calculation dates in the segmented simulation do not align with those in the annual simulation. Therefore, it is important to align the shadowing calculation dates of the segmented simulations to map with the shadowing calculation dates in the annual simulation. 2.3 Alternatives Proposed to Decrease the Deviations The deviations mentioned may be reduced by changing some of the simulation settings and variables. The solutions proposed are: (1) Selecting Day_of_Week_for_Start_Day from the weather file: If the Day of week for Start Day is chosen as UseWeatherFile, the day of week for all the dates in the annual simulation will be in synchronization with the day of week for the corresponding dates in the segmented simulation, hence ensuring the same number of working days between the segmented and annual simulations. By default, EnergyPlus specifies a particular day (such as Monday) as the Day of week for Start Day, due to which the start day of the each segmented simulation will be this day specified. However, this same date in the annual simulation would be some other day, due to which there would synchronization problem of the day and dates in annual and segmented simulations. UseWeatherFile option picks the calendar day for that particular date from the weather file.

8 (2) Ensuring first day of the segment is a working day for adequate warming up: If the first day of the month happens to be a holiday then some days from the previous month can be added to the segment to ensure adequate number of working days between the first day of the simulation and the first the of the month. This increases the RunPeriod of the segment and the first day of segment would not be the 1 st of the month. These extra number of days added to each segment would be referred to as warmupplus days in this paper. (3) Synchronizing shadow calculation dates of the segments with the annual shadow calculation dates: If the shadowing calculation dates of the annual simulations and those of each segmented simulation are kept in synchronization with each other, the effects of unaligned shadow calculations can be minimized. This is achieved by adding some more days from the previous month to the segment so that the start date of the shadow calculation period of the segmented simulation align with the annual shadowing dates. This extra number of days added before each segment will be referred to as shadowsync days in this paper. The following measures were taken to implement these solutions: (1) The Day_of_Week_for_Start_Day element in the RunPeriod class was changed to UseWeatherFile. This ensured that the total number of working days for all the months in the segments were the same as the total number of working days in the annual simulation. (2) To every segment 7 warmupplus days of simulation were added before the simulation days of the segment. This ensured adequate warm up especially if the 1 st day of the month happened to be a holiday.

9 (3) Some more days were added in the beginning of the segment to ensure that the shadow calculation dates in the segment mapped with that of the annual simulation. Before arriving at this proposed solution, some more variants were analyzed. In addition to the 7 warmupplus days, simulations were done with 0, 14 and 21 warmupplus days. Two more variants with synchronized shadow calculation days were also analyzed. This resulted in six different alternatives, which were based on the combination of warmupplus days and shadowsync days. These six combinations were experimented on to find out an alternative that results in the least deviation in cooling, heating, lighting and equipment consumption when compared with the annual simulation. The six alternatives and their names as used in the paper are: (1) W0: 0 warmupplus days (2) W7: 7 warmupplus days (3) W14: 14 warmupplus days (4) W21: 21 warmupplus days (5) W0Sync: shadowsync days with minimum allowed value as 0 (6) W7Sync: shadowsync days with minimum allowed value as 7 For all the six alternatives, the Day_of_Week_for_Start_Day is selected as UseWeatherFile 3. Performance of the alternatives As discussed in earlier sections warm up and synchronization of shadow calculation dates have a significant impact on the simulation runtime and the accuracy of the results. Simulations were conducted to evaluate the performance of the six alternatives proposed in the previous section in terms of accuracy and speed up.

10 ElemSchool model has been simulated for the five cities (Chicago, San Francisco, Tampa, New York, and Houston) with all the six alternatives. Effect of shadow sync and warm up days on accuracy of results and the speed gain are discussed in Sections 3.1 and Accuracy of the alternatives The six alternatives were simulated to analyze the deviations in results of the segmented simulations in comparison with corresponding results of the annual simulation. Large deviations in monthly consumption values were observed when ElemSchool model was simulated for Chicago weather data. Table 2 provides the percentage deviations in Cooling Electricity consumption for various months between the segmented simulations and annual simulations for all the six alternatives. The maximum deviation in monthly cooling consumption was 0.098% for W21 alternative in the month of July. Table 3 shows the percentage deviations in the heating energy consumption. The maximum deviation in monthly heating consumption was 1.87% for W0 and W0Sync alternative in month of April. In both the tables, the minimum deviation values for each month are highlighted in bold. Table 2 Table 3 Some of the observations from Tables 3 and 4 are: For cooling, W0Sync and W7Sync show least deviations For heating, W7Sync shows the least deviations W14 shows better accuracy for both heating and cooling amongst W0, W7, W14 and W21.

11 In the annual results, W7Sync show significantly less deviations compared to the other methods. These observations clearly indicate that the alternatives W0Sync and W7Sync are more accurate and are proved to be better by a significant difference when compared to the other alternatives. As observed in the Table 2 the deviations in cooling consumptions are higher in summer. Since the cooling values are of lower orders for a city like Chicago, a small deviation in the cooling consumption will give a greater percentage difference. Another interesting observation is that W14 shows better results than W21, even when W21 has more days of simulation before the first day of the segmented simulation. The more accurate results of W14 are due to the shadowing frequency of 15 days in this model. Extra 14 days synchronizes the shadowing periods better than the extra 21 days taken in W21. Tables 3 and 4 show the comparison of the six alternatives based on their performance on monthly and annual basis. W0Syn and W7Sync both give fairly accurate results for monthly and annual values. Deviations in the hourly values were then checked to compare the two alternatives. Large deviations in hourly consumption values were observed when ElemSchool model was simulated for New York weather data. Figure 3a and 3b show percentage deviations in the hourly cooling and heating consumption between the segmented simulations and the annual simulations for the W0Sync alternative. The maximum deviation in hourly value of cooling was 54% and heating was 18.1%. It can be observed from the graphs that even when W0Sync is accurate on the monthly and the annual values, there are big deviations on the hourly values.

12 Figure 3a Figure 3b Figure 4a and 4b show the percentage deviation in the hourly cooling and heating consumption between the segmented simulations and the annual simulations when applying the alternative W7Sync.The maximum deviation in hourly value of cooling was 0.01% and heating was 0.04%. Hence, W7Sync is not only accurate on the monthly and the annual values; it is also very accurate on hourly values. Figure 4a Figure 4b The graphs clearly indicate that the deviations observed in the hourly data for W0Sync were reduced in the W7Sync alternative. The deviations in the graph also strengthen the proposition that the addition of warmupplus days is important. April, for instance shows heavy deviations in both cooling and heating. This can be attributed to the fact that 1 st April is start date for one of the shadow calculation periods. Hence in the W0Sync setup, there are 0 warmupplus days leading to deviations. While in the W7Sync setup, there are 15 warmupplus days (since the shadowing frequency is 15 days) which results in negligible errors. Since the shadowing frequency for this simulation set is 15 days, other months are able to align appropriately and result in fewer deviations. However, if the frequency is some other number, for example 20, which causes less alignment, then these deviations will

13 increase. Due to this, the W7Sync algorithm will work efficiently for any value of shadow frequency and will provide accurate results. 3.2 Speed gain of the alternatives To evaluate the performance of all the alternatives in terms of simulation run time, the model ElemSchool with Chicago weather file was used and the time taken by each simulation was observed. Table 4 shows the simulation run time (in seconds) and speed gain of monthly segments, the time taken by the annual simulation (in seconds) and the effective speed gain for the six alternatives. Speed gain is obtained by dividing the time of annual simulation by the time taken by segmented simulation. Variation can be observed in the time taken by simulation by the segments for different months. This is due to the difference in number of shadow calculations and the time taken for convergence of various HVAC calculations. It is observed that in colder months such as January and December the time taken is more due to an increased number of iterations in the HVAC. The segment that takes the maximum time governs the effective speed gain for that alternative. Hence, the minimum speedup value obtained for that set of segments is highlighted in bold. It is clear from the table that the overall speed gain decreases with the number of warmupplus days. W0 is fastest but not very accurate and W7Sync is slower but more accurate. Due to this accuracy, W7Sync is selected for demonstrating the proposed approach of speeding up EnergyPlus using parallel computing. Table Performance Analysis of W7sync After selecting W7sync alternative for demonstrating the proposed approach, this alternative has been further analysed to observe the effect of the following on the

14 speed gain: Number_of_Timesteps_per_Hour, number of processing units, and overheads with respect to extra time taken in transferring files over the network and collating the output. (1) Number_of_Timesteps_per_Hour: The ElemSchool model for Chicago climate with varying time steps has been simulated to study the impact of time steps on speed up. It has been observed that the simulation speed gain increased with the increase in the value of Number_of_Timesteps_per_Hour. Speed gain with the value of Number_of_Timesteps_per_Hour as 1 is 2.73 Speed gain with the value of Number_of_Timesteps_per_Hour as 4 is 3.15 Speed gain with the value of Number_of_Timesteps_per_Hour as 20 is 4.12 (2) Number of processing units: To demonstrate the effect of number of processing units on the speed gain we simulated ElemSchool model for Chicago on 2, 3, 4, 6 and 12 processing units. It has been observed that the speed gain decreases with the decrease in number of processing units. Speed gain with the number of processing units is shown in Table 5 (3) Overheads with respect to extra time taken: This whole process has some overheads with respect to extra time taken for pre-processing.idf file, sending the.idf files to processors, collecting the results from processors and collating the results to form a single file. The overhead depends largely on network speed, the size of.idf file and the size of the output file which in turn depends on the number of variables and their reporting frequency. In the simulations which were performed it was observed that the overhead was close to 5% of the time taken by the slowest segment. For example, for ElemSchool model,

15 the overall overhead was about 15 seconds, and the time taken by the slowest segment was 422 seconds, so the total overhead is 4.02 %. 4. Simulation and results In order to demonstrate the approach over the selected alternative W7Sync, simulations were performed using EnergyPlus V 4. Thirteen different building models were simulated across five different cities. Details of the simulation models, weather data and the results are provided in the following sections. 4.1 Building Prototypes The U.S. Department of Energy (DOE) through three of its national laboratories has developed a set of standard benchmark building models for new and existing buildings [8]. These models are used in the paper to test the proposed approach. The revised and latest models for EnergyPlus v5 are now referred as commercial reference building models for new construction [9]. The changes between the standard benchmark model used in this paper and the new reference building models are listed in summary of changes from v1.2_4.0 to v1.3_5.0 document [10].These models help in providing consistent standardized models for the analysis of the results. To analyze the performance of the proposed alternative over different models and climates, the benchmark buildings that come along with the EnergyPlus installation were used for the simulations. Thirteen different building models- twelve benchmark buildings for new construction and one example model from EnergyPlus were used. The example file of EnergyPlus installation is a 5 Zone VAV model with daylight sensors and is used to check the concept and the accuracy of results for a

16 building with daylight sensors. Some important characteristic features of these models are listed in Table 6. Table Climate Zones All the thirteen different models were simulated for five different cities of USA which belong to four different climate zones as shown in Table 7. Table Performance of the proposed alternative- W7Sync To evaluate the performance of the proposed alternative, 65 cases (13 models x 5 cities) were simulated. Results show that W7Sync results are reasonably accurate for all the cases, as observed in the earlier section. To show a compact summary of the results achieved for all the 65 cases, the deviations in annual cooling, heating, equipment and lighting energy consumption and the speed gains are as listed in the Table 7.However, analysis has been performed on all the cases and the deviations in hourly consumptions and the monthly consumptions were noted to be very minor, in similar limits as discussed in the previous sections. Table Results From the simulations performed over thirteen different models using five different cities, it was observed that the speed up achieved varied from 3.15x to 5.84x. The minimum speed gain was in model ElemSchool for Chicago, which was 3.15x. The maximum gain was achieved for the MidApt and Chicago weather, which was 5.84x. The average gain observed for all the 65 cases was 4.77x. One interesting observation

17 made was that the speed gain for a model depends on the climate for which the simulation is run. The maximum deviation in cooling consumption was noted in SmallOffice model and in heating consumption it is noted in MidApt model. The maximum and minimum deviations annually across all models are as shown in Table 9. Table 9 Table 10 shows the maximum percentage deviations that occurred in the monthly consumption of heating, cooling and lighting, out of all the 65 cases that are simulated. The deviations in case of equipment electricity consumption were found to be 0 for all the cases. Table Conclusion The approach of dividing a simulation into segments and running them in parallel decreases the simulation run time, and is accurate enough to be used practically for increasing the speed of an EnergyPlus simulation. The proposed approach has been applied over 13 different models and five different weather files, to check the accuracy of the results achieved when compared to the annual simulation results. On annual basis, a maximum deviation of 0.06% was observed in cooling, heating, and lighting consumption. In a month-to-month comparison between the segments and annual simulation, the maximum deviation was 1.7% for heating and 0.8% for cooling. The speed gain in the simulation would range between 3x to 6x. For performing huge number of simulations during the conceptual design stage and for the parametric analysis, the proposed approach can be very useful. And, for the final

18 analysis a single run simulation as per conventional approach can be performed to get precise results. Further study would include the development of a tool that will use the described algorithm on a cluster of computers or processors. References [1] EnergyPlus Energy Simulation Software, Available from: [2] DOE-2, Available from: [3] BLAST-Building Load Analysis and System Thermodynamics, Available from: [4] Tianzhen H, Fred B, Philip H, Stephen S, Michael W Comparing Computer Run Time of Building Simulation Programs. Building Simulation, 1 (3), [5] Hong, Tianzhen. (2009). EnergyPlus Run Time Analysis [online]. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. LBNL Paper LBNL-1311E. Available from: [6] Yi Z Parallel EnergyPlus and the development of a parametric analysis tool. In: Eleventh International IBPSA Conference, July 2009 Glasgow, Scotland: [7] GenOpt-Generic Optimization Program, Available from: [8] P. Torcellini, et al DOE Commercial Building Benchmark Models. In: ACEEE Summer Study on Energy Efficiency in Buildings, August 2008 Pacific Grove, California. [9] Commercial Reference Building models for New Construction, Available from: [Accessed 4 October 2010]

19 [10] Summary of Changes from v1.2_4.0 to v1.3_5.0, Available from: gs_changes_v40tov50.pdf [Accessed 4 October 2010]

20 Table 1: Percentage deviation in monthly cooling and heating consumption between segmented and annual run % Deviation Months Cooling Heating Jan Feb March April May June Jul Aug Sep Oct Nov Dec Annual

21 Table 2: Deviations in cooling electricity consumption for the six different alternatives that were simulated for the ElemSchool and Chicago climate. The maximum deviation was 0.098% for W21 alternative in the month of July Alternatives Months W0 W7 W14 W21 W0Sync W7Sync Jan Feb March April May June Jul Aug Sep Oct Nov Dec Annual

22 Table 3: Deviations in heating consumption for the six different alternatives that were simulated for the ElemSchool and Chicago climate. The maximum deviation was 1.87% for W0 and W0Sync alternative in month of April. Alternatives Month W0 W7 W14 W21 W0Sync W7Sync Jan Feb March April May June Jul Aug Sep Oct Nov Dec Annual

23 Table 4: Simulation run time (in seconds) and speed gain of monthly segments, the time taken by the Annual simulation (in seconds) and the effective speed gain for the six alternatives. Month W0 W7 W14 W21 W0Sync W7Sync Jan Feb March April May June Jul Aug Sep Oct Nov Dec Annual Effective speed gain

24 Table 5: Speed gain achieved for ElemSchool model simulated for Chicago climate, with varying number of processing units Number of Speed Gain processing units

25 Floor Area (thousand m 2 ) Number of Floors Inter zone Surfaces People Lights Windows Daylight Zonal Equipment Central Air Handling Equipment System Equipment Autosize Coils Pumps Boilers Chillers Towers Table 6: Characteristics of the thirteen building models used for testing the proposed approach of speeding up EnergyPlus Zone Definition HVAC System HVAC Plant Model Name ElemSchool x x x x x x x x Fastfood x x x x x x x x HighSchool 24 2 x x x x x x x x x x x Hospital x x x x x x x x x x x LargeHotel x x x x x x x x x x x LargeOff x x x x x x x x x x x MedOff x x x x x MidApt x x x x x x x x Retail x x x x SitdownRestrau x x x x SmallHotel x x x x SmallOffice x x x x 5ZoneVAV x x x x x x x x x x x x

26 Table 7: Climate data used for running the simulation models City Climate zone Climate type Weather file name Chicago 5A Cool Humid USA_IL_Chicago- OHare.Intl.AP _TMY3.epw San Francisco 3C Warm- Marine USA_CA_San.Francisco.Intl.AP _TMY3.epw Tampa 1A Very hot- USA_FL_Tampa.Intl.AP _TMY3.epw humid New York 5A Cool Humid USA_NY_New.York- J.F.Kennedy.Intl.AP _TMY3.epw Houston 2A Hot- Humid USA_TX_Houston- D.W.Hooks.AP _TMY3.epw

27 Table 8: Percentage annual deviations for Cooling, Heating, Equipment and Lighting consumption and effective speed up for 13 models and 5 cities Model Chicago San Francisco Tampa New York Houston ElemSchool Cooling Heating Equipment Lighting Speed Gain 3.15x 3.68x 3.76x 3.33x 3.5x Fastfood Cooling Heating Equipment Lighting Speed Gain 4.3x 4.86x 5.05x 4.54x 4.79x HighSchool Cooling Heating Equipment Lighting Speed Gain 4.92x 5.43x 5.46x 5.37x 5.4x Hospital Cooling Heating Equipment Lighting Speed Gain 5.61x 5.63x 5.7x 5.2x 5.44x LargeHotel Cooling Heating Equipment Lighting Speed Gain 4.84x 5.22x 5.77x 5.23x 5.58x LargeOff Cooling Heating Equipment Lighting Speed Gain 5.23x 5.47x 5.18x 5.31x 5.04x MedOff Cooling Heating Equipment Lighting Speed Gain 4.16x 4.4x 4.3x 4.31x 4.29x

28 MidApt Cooling Heating Equipment Lighting Speed Gain 5.84x 5.42x 5.8x 4.92x 5.62x Retail Cooling Heating Equipment Lighting Speed Gain 4.36x 4.5x 4.44x 4.53x 4.34x SitdownRestrau Cooling Heating Equipment Lighting Speed Gain 4.33x 4.84x 5.07x 4.46x 4.69x SmallHotel Cooling Heating Equipment Lighting Speed Gain 3.4x 3.51x 3.51x 3.42x 3.5x SmallOffice Cooling Heating Equipment Lighting Speed Gain 4.18x 4.58x 4.58x 4.53x 4.55x 5ZoneVAV Cooling Heating Equipment Lighting Speed Gain 5.45x 5.47x 5.44x 5.61x 5.43x

29 Table 9: Maximum and Minimum percentage deviations in annual heating, cooling, lighting and equipment consumption Heating Cooling Lighting Equipment Minimum Deviation Maximum Deviation

30 Table 10: Maximum percentage deviations in monthly heating, cooling and lighting consumption Cooling Heating Lighting Maximum Value Model Medium Office Small Office 5ZoneVAV City New York New York Tampa

31 Figure1: Flow diagram of the entire process Figure2a: Percentage deviation in hourly cooling electricity between segmented simulation and the annual simulation for ElemSchool model with Chicago city. The maximum hourly deviation is 100% Figure2b: Percentage deviation in hourly heating consumption between segmented simulation and the annual simulation for ElemSchool model with Chicago city. The maximum hourly deviation observed in an hour is as high as 1340 %.( Instead of the peak a general range in which the percentage deviations exist has been considered to scale the plot) Figure 3a: Percentage deviation in hourly cooling electricity between segmented simulation and the annual simulation for ElemSchool model with New York city weather. The maximum hourly deviation is 54%. Figure 3b: Percentage deviation in hourly heating consumption between segmented simulation and the annual simulation for ElemSchool model with New York city weather. The maximum hourly deviation is 18.1%. Figure 4a: Percentage deviation in hourly cooling electricity between segmented simulation and the annual simulation for ElemSchool model with New York city weather. The maximum hourly deviation is 0.01%. Figure 4b: Percentage deviation in hourly heating consumption between segmented simulation and the annual simulation for ElemSchool model with New York city weather. The maximum hourly deviation is 0.04%.