Energy Consumption Analysis: Application. A Case Study in Predicted Versus Actual Energy Consumption. Brandon Ophoff

Transcription

1 CCEE Fung Scholarship University Campus LEED Building Energy Consumption Analysis: Application A Case Study in Predicted Versus Actual Energy Consumption Brandon Ophoff Construction Engineering Brandon Ophoff (Undergraduate Student) Dr. Kristen Cetin (Assistant Professor) Mechanical Emphasis Civil, Construction, and Environmental Engineering Iowa State University December 16, 2016 Iowa State University

2 Acknowledgements This report was created to fulfill of the requirements for completion of an undergraduate honors thesis within the Department of Civil, Construction and Environmental Engineering at Iowa State University. A poster presentation visual was also created and is located in Appendix A. Dr. Kristen Cetin, the faculty mentor for this project, has performed similar building energy studies at other universities, and provided guidance in the building energy analysis performed in this project. I would like to thank Dr. Kristen Cetin my advisor for her expertise and guidance throughout the project. This research project has been a very positive experience and beneficial to my personal learning. I would also like to thank Kerry Dixon, Wendy Kisch, Scott Anderson, and Dean McCormick with ISU FP&M for providing me with access to the background information required to complete this project.

3 Table of Contents Table of Figures... 3 Table of Equations... 3 I. ABSTRACT... 4 II. INTRODUCTION... 5 Problem Statement... 6 Objectives... 6 III. METHODOLOGY... 7 Energy Consumption... 7 Degree Day Calculations... 7 Weather Normalization... 8 Utility Cost... 8 Energy Usage Trends... 8 IV. RESULTS... 9 Energy Consumption... 9 Utility Cost Energy Usage Trends V. DISCUSSION Energy Consumption Plant Capacity Utility Cost University Budget Implications Energy Usage Trends Energy Intensity VI. CONCLUSIONS Future Direction Works Cited APPENDIX A Poster Presentation APPENDIX B Yearly Energy Data Table APPENDIX C Monthly Energy Data Table pg. 2

4 Table of Figures Figure 1: BRL Location... 4 Figure 2: Biorenewables Research Laboratory... 4 Figure 3: Yearly Energy Usage from the Energy Model and Actual Consumption... 9 Figure 4: Normalized Annual Energy Usage from the Energy Model and Actual Averaged Consumption from Figure 5: Yearly Utility Costs from the Energy Model and Actual Consumption Figure 6: Normalized Utility Costs from the Energy Model and Actual Averaged Consumption from Figure 7: Monthly Chilled Water Use from , Indicating Increased Use Over Time Figure 8: Monthly Electric Use from , Indicating Slight Increase Over Time Figure 9: Monthly Electric Use from , Indicating Slight Increase Over Time Figure 10: Monthly Steam Use from , Indicating Slight Decrease Over Time Figure 11: Average Monthly Temperatures of the TMY2 and Des Moines Airport Weather Station Figure 12: Utility Use Rates of the ISU Campus from the Energy Model, and from , Indicating an Increase Over Time Figure 13: Poster Presentation Figure 14: Yearly Energy Data Table Figure 15: Monthly Energy Data Table Table of Equations (Equation 1)... 8 pg. 3

5 I. ABSTRACT Iowa State University Facilities, Planning, and Management is concerned with new LEED (Leadership in Energy & Environmental Design) buildings on campus consuming more energy than originally predicted from the building energy models created as part of LEED certification requirements. The objective of this research project is to compare the actual and predicted energy consumption of the Biorenewables Research Laboratory (BRL), a 2011 LEED Gold building, to determine the magnitude and cause(s) of this discrepancy. This will lead to re-evaluation of energy model assumptions to improve future models of campus buildings. This objective is accomplished through analysis of energy model and utility billing data, using historical weather data to weather-normalize energy use. The preliminary results of this research project conclude, when weather normalized, BRL is consuming roughly 4% less energy, but spending 25% more on utility bills than the model originally predicted. Utility rates have been much higher than those used to calculate costs in the energy model. As utility costs are not exclusively influenced by energy consumption alone, future energy models should improve how utility rates are incorporated. Further investigation into the actual operation of the building systems is needed to determine specific system energy consumption discrepancies. Figure 1: BRL Location Figure 2: Biorenewables Research Laboratory pg. 4

6 II. INTRODUCTION Beginning in 2009, Iowa State University (ISU) Facilities, Planning, and Management (FP&M) has had a goal to obtain Leadership in Energy Efficient Design (LEED) Gold certification for all new and renovated buildings. Currently there are six LEED buildings including one silver, three gold, and two platinum certifications, with eight additional LEED projects planned for the near future. 1 The purpose of this program is to incorporate sustainable design and construction practices into the ISU campus buildings. However, simply constructing a building with the goal of LEED certification is not necessarily enough to ensure its sustainability and energy efficiency. FP&M realizes this, and not only shows concern with the initial green construction of buildings, but also wants to track the buildings performance over time to determine their actual performance. FP&M wants to know if buildings on campus are consuming a similar level of predicted energy consumption from the originally created LEED energy models. While FP&M has not yet investigated this, there is a belief within some members of FP&M that these LEED buildings are using significantly more energy than the models have predicted. 2 This challenge and its implications extend beyond just the ISU campus. The challenge of accurate prediction of energy consumption of buildings using building energy modeling has been highly cited in the recent literature. 3,4,5 There have been significant improvements to building energy modeling methods in recent years, including highly complex physics and thermodynamics-based methods that have been heavily tested and verified; 6,7,8 however, there are still thousands of variables and other relevant input information required to produce an accurate energy model. Some modeling variables are known, and others must be assumed based on previous findings and recommended values typically used for different building types. 9 Yet even though some variables may be known, for example a thermostat set-point, there may still be inherent variability in their values when the building is occupied. In addition, while some building types have been well-studied, such as office buildings, other building types including university campus buildings, are not a standard building type and thus have less general assumptions available in the creation of energy models. Given the possible uncertainties associated with many variables such as building occupancy, space usage predictions, lab placement, lab use, and plug loads, particularly in campus buildings, it is understandable to assume a degree of uncertainty in the resulting building energy model. All of these factors and considerations present challenges in the creation of an accurate energy model. 1 "Design & Construction." Office of Sustainability. Iowa State University. 2 Kisch, Wendy. "Energy Usage at Iowa State University." Personal interview. 1 Dec Reddy, T. Agami. "Literature Review on Calibration of Building Energy Simulation Programs Schwartz, Yair, and Rokia Raslan. "Variations in results of building energy simulation tools." Wang, Liping, Paul Mathew, and Xiufeng Pang. "Uncertainties in energy consumption." EnergyPlus. "Testing and Validation." U.S. Department of Energy's Building Technologies Office. 7 Hirsch, James J. "EQUEST." EQUEST. DOE-2, ASHRAE Standard: Method of Test for Evaluation of Building Energy Analysis Computer Programs "Commercial Reference Buildings." Department of Energy. Office of Energy Efficiency & Renewable Energy. pg. 5

7 Regardless, of these challenging factors, the intention of a LEED building is, in part, to consume less energy than it would otherwise consume if just following building code requirements. A graduated system is used to award points toward the LEED certification, based on what percent energy savings the building energy model predicts the building will achieve over the baseline code-compliant buildings. A number of studies have been conducted to compare LEED and non-leed buildings, and have produced conflicting conclusions. LEED buildings have been found to consume both more and less energy than predicted by models when compared to their non-leed counterparts. 10,11,12,13,14 Through a better analysis of the performance of campus buildings, it can better be determined what challenges are currently faced in the creating of accurate building energy models of these buildings. This can lead to recommendations of procedures and standard information sharing between universities and those creating energy models for LEED compliance, facilitating improved energy performance predictions, particularly for campuses who rely on this information for power production planning purposes. Problem Statement ISU FP&M is concerned with new LEED buildings on the ISU campus consuming more energy than originally predicted by the energy models developed in the construction phases of the building. ISU FP&M would like to investigate the current assumptions used in the creation of these LEED energy models so that they can be re-evaluated to develop models that more accurately predict energy usage, which can be applied to the development of energy models for other future buildings on ISU campus. A case study building is used to conduct this analysis. Objectives The following are the objectives of this research: 1. Determine if the Biorenewables Research Laboratory (BRL) on campus is consuming more energy than the model predicted. 2. Analyze the historic energy consumption of the building against the modeled consumption. 3. Develop recommendations to improve the accuracy of future building energy models on campus. 10 Turner, Cathy, and Mark Frankel. "Energy performance of LEED for new construction buildings." Scofield, John H. "Do LEED-certified buildings save energy? Not really." Newsham, Guy R., Sandra Mancini, and Benjamin J. Birt. "Do LEED-certified buildings save energy?" Scofield, John H. "Efficacy of LEED-certification in reducing energy consumption." Schwartz, Yair, and Rokia Raslan. "Variations in results of building energy simulation tools." pg. 6

8 III. METHODOLOGY Energy Consumption Output results from the BRL LEED energy model 15 are analyzed and the yearly consumption of chilled water, electricity, and steam (each with units of MBTU) is determined. The model predicts a small amount of natural gas used for domestic water heating; the BRL building does not indicate a natural gas utility line, so this energy is combined with the steam energy of the model. The energy consumption of the actual building is obtained from the ISU FP&M website. 16 The meter number for total energy consumption of chilled water (in ton-hours), electricity (in kwh), and steam (in lbs.) is used to collect the historical monthly energy consumption for the years 2012 through This monthly data is summed by year and by system, and compared to the annual energy consumption predicted by the model. Degree Day Calculations The assumptions listed in the energy model results indicate that Typical Meteorological Year (TMY2) weather data from the Des Moines International Airport was used to represent weather patterns the building is exposed to for this particular model. TMY2 weather data includes weather data collected hourly across a 30-year period from 1961 to 1990 at this location. It is designed to best represent the average weather conditions in this location. 17 It is common practice to use TMY data for building energy modeling analysis as the purpose of the creation of this data is for use in understanding the normal weather in a specific location. It should be noted that TMY data does not represent future weather performance, nor does it include weather extremes that can occur over time. These are common criticisms of the use of this data for energy performance prediction; however, at this time there are no other commonly accepted methods for typical weather dataset development. 18,19,20,21 This TMY2 data was downloaded from EnergyPlus weather reference files. 22 Outdoor air temperature values from the TMY2 data are converted into degrees Fahrenheit and averaged by 24-hour day periods. Each 24-hour day with an average temperature below 65 F is subtracted from 65. Each 24-hour day with an average temperature above 65 F is taken less 65 F. Once the number of degrees above and below 65 F for each day are calculated, the days with averages below 65 F are summed by month to determine Heating Degree Days (HDD), and the days with average values below 65 F are summed by month to determine Cooling Degree Days (CDD). 23 The threshold value of 65 F used for these calculations is commonly used in the building energy performance field. 15 Building energy model created by John Beattie with Affiliated Engineers, Inc on March 16, "Utility Billing Charges." Iowa State University Division of University Services, Marion, William, and Ken Urban. User's Manual for TMY2s: Typical Meteorological Years Crawley, Drury B. "Which weather data should you use for energy simulations of commercial buildings?" Bhandari, Mahabir, Som Shrestha, and Joshua New. "Evaluation of weather datasets for energy simulation." Guan, Lisa. "Preparation of future weather data to study the impact of climate change on buildings Barnaby, Charles S., and Drury B. Crawley. "Weather data for building performance simulation." EnergyPlus. "Weather Data by Location." U.S. Department of Energy's Building Technologies Office. 23 ASHRAE Handbook - Fundamentals pg. 7

9 Historic hourly outdoor air temperature values from 2012 to 2015 are also collected from the National Oceanic and Atmospheric Administration. 24 This data is cleaned to remove days of missing data, and follows a process similar to that stated above for the energy model weather data in order to determine the HDD and CDD for each month of the historic years. Weather Normalization The annual energy use from each historic year is averaged by system. Chilled water is used to heat the building in colder months. Therefore, the historic annual energy use for chilled water is divided by the total CDD historic average and multiplied by the CDD of the TMY2 model data to determine the weather normalized chilled water energy use of the model. CDD is chosen since chilled water is used for cooling campus buildings. This process is repeated using a summation of HDD and CDD to determine the weather normalized electric energy use of the model. A summation of HDD and CDD is chosen since electricity loads are fairly constant at all outdoor air temperature values throughout the year. HDD to determine the weather normalized steam energy use of the model. HDD is chosen since steam is used for heating campus buildings. The equation for weather normalization of this model is: Energy Use Actual HDD Actual X HDD Model = Energy Use Model (Equation 1) Utility Cost Output results from the energy model are analyzed and the yearly utility costs of chilled water, electricity, and steam are determined. The utility costs of the actual building are obtained from the ISU FP&M website. 25 The meter number for total energy consumption of chilled water, electricity, and steam is used to collect the associated historical monthly utility costs for the years 2012 through This monthly data is summed by year and by system, and compared to the yearly utility cost collected from the model. Energy Usage Trends Monthly data for each historical year s energy consumption is paired with the value of degree days for that respective month. These data pairs are plotted and trended graphically by year. 24 National Centers for Environmental Information (NCEI). "Quality Controlled Datasets." 25 "Utility Billing Charges." Iowa State University Division of University Services, pg. 8

10 IV. RESULTS This section states observations based on the results of this research regarding energy consumption, utility cost, and energy usage trends. Tabulated results from the building study research are shown in Appendices B and C. Energy Consumption A graph of energy usage of the energy model compared to the historic energy usage by system is shown in Figure 3 below. A degree day weather normalized graph of the model compared to the average historic energy usage is shown in Figure 4 below. The total energy usage, including chilled water, electricity, and steam, are summed together for the normalized energy model data, as well as for the historic average usage data. According to the data collected for this research, these sums indicate that the BRL building is consuming 4% less combined total energy than the model originally predicted overall, when compared to its historic average consumption (8% less chilled water consumption, 3% less electricity consumption, and 2% less steam consumption). Figure 3: Yearly Energy Usage from the Energy Model and Actual Consumption pg. 9

11 Figure 4: Normalized Annual Energy Usage from the Energy Model and Actual Averaged Consumption from Utility Cost A graph of utility cost of the model compared to the historic utility costs by system is shown in Figure 5 below. A degree day weather normalized graph of the model compared to the average historic utility costs is shown in Figure 6 below. The total utility costs, including chilled water, electricity, and steam, are summed together for the normalized energy model data, as well as for the historic average utility cost data. According to this data, these sums indicate that the BRL building is spending 25% more on utility bills than the model originally predicted. pg. 10

12 Figure 5: Yearly Utility Costs from the Energy Model and Actual Consumption Figure 6: Normalized Utility Costs from the Energy Model and Actual Averaged Consumption from pg. 11

13 Energy Usage Trends Graphic analysis of historic energy usage by year indicates that the historic building energy consumption within each specific utility is changing over time. An increase in chilled water energy usage is observed when plotted against CDD is shown in Figure 7 below. Figure 7: Monthly Chilled Water Use from , Indicating Increased Use Over Time pg. 12

14 In general, the electricity use is fairly constant and consistent regardless of the time of year and weather conditions. However, a slight increase in electric energy usage is observed when plotted against CDD and against HDD as shown respectively in Figures 8 and 9 below. Figure 8: Monthly Electric Use from , Indicating Slight Increase Over Time Figure 9: Monthly Electric Use from , Indicating Slight Increase Over Time pg. 13

15 A decrease in steam energy usage is observed when plotted against HDD as shown in Figure 10 below. Figure 10: Monthly Steam Use from , Indicating Slight Decrease Over Time pg. 14

16 V. DISCUSSION Energy Consumption Energy consumption of a building can be very difficult to analyze because there are so many factors affecting it, and often has limited data and information to support this analysis. Outside the building, energy consumption is affected by outdoor air temperatures, humidity levels, solar radiation, wind speed and direction among others. Inside the building, energy consumption is affected by factors such as occupants, schedule of use, and plug loads. Energy consumption is also affected by the construction integrity of the building that can cause increased rates of infiltration if not carefully controlled. Maintenance operations of the building are also highly influential. Properly maintained equipment will have a longer life expectancy and perform more efficiently than those that are not. Despite these many factors influencing building energy consumption, outdoor air temperature is the factor that commonly provides the most accurate correlation with building energy use in mechanically heated and cooled buildings according to industry standards. 26 A graph depicting the large variation in monthly average outdoor air temperatures from year to year related to the analysis of this project can be seen in Figure 11 below. This is why it is very important that the energy model data is weather normalized by outdoor air temperature using degree days to help remove the influence of varying outdoor air temperatures and produce more appropriate data for energy comparison. Particularly since the variation in the outdoor temperature ranges from year to year, and may not necessarily be the same as what a TMY weather file projects. Figure 11: Average Monthly Temperatures of the TMY2 and Des Moines Airport Weather Station 26 ASHRAE Handbook - Fundamentals pg. 15

17 Plant Capacity Energy consumption of current and future buildings on campus is important to ISU FP&M because the ISU power plant must be able to continue keeping up and providing this energy as campus grows. If the ISU power plant is not able to provide the chilled water, electricity, and steam required to power and condition the buildings on campus, then ISU will need to purchase power from the Ames electric utility grid, which is typically more expensive, and limits the control that ISU has over the cost associated with the use of electricity on campus. As of December 2016, the ISU power plant produces 16,765 tons of chilled water (79.8% of full capacity), 28.7 MW of electricity (62.4% of full capacity), and 339,000 lbs/hr of steam (55.6% of full capacity). 27 While this is currently sufficient for the demands of the campus in its current capacity, as the campus continues to grow and expand its buildings footprint, future demands of the campus may merit more careful analysis of the demand as compared to the supply produced. Utility Cost If FP&M wishes to use the energy model of the building created for LEED during the construction period of BRL as an indicator of utility costs, they may need to update the utility assumption in the model with more current rates. At least for this particular building, the LEED energy model appears to closely predict annual energy consumption; however, the utility cost prediction was not as accurate because utility rates were found to be very volatile throughout the years of study included in this report. Based on this research, it would seem reasonable to continue to use the model s energy consumption predictions to determine future use, but apply updated utility rates if FP&M plans to use the model to indicate future costs. Utility rates used in the energy model assumptions in comparison with the rates taken from historic years are shown in Figure 12 below. The cause of increased utility rates in the time frame observed for this particular project may be due to new EPA regulations regarding coal-fired boilers that went into effect on January 31, The ISU power plant likely knew that these regulations would be coming into effect and increased the utility rates during the years leading up to 2016 so that they could fund the replacement of older boilers that would not meet the new regulations, with newer gas-fired boilers. 27 Kisch, Wendy. "Energy Usage at Iowa State University." Personal interview. 1 Dec Kisch, Wendy. "Energy Usage at Iowa State University." Personal interview. 1 Dec pg. 16

18 Figure 12: Utility Use Rates of the ISU Campus from the Energy Model, and from , Indicating an Increase Over Time University Budget Implications The utility cost of current and future buildings on campus is important to FP&M because it impacts the university budget. The university is allotted a certain budget from the State of Iowa to pay for utilities; however, if any of this budget is leftover after utilities have been paid, this leftover money can be used to fund other budgets around campus, thus providing ISU and FP&M with an incentive to reduce the utility cost. Energy Usage Trends The changes in energy use of the BRL building over time are not necessarily correlated with the building s energy efficiency. Increasing energy usage is not synonymous with non-energy efficient operation. In this particular case, the change in energy usage over time could very likely be due to increased occupants in building. 29 The energy model was developed to represent 100% occupant capacity; however, the building did not start out at full-capacity in the first year it opened. It has taken time for people to move into this building. 27 After speaking with FP&M, it is understood that the move-in period for BRL spanned from 2012 to This extended move-in period is non-typical of university buildings because they are usually budgeted for and built after the need for the new building is overwhelmingly present. BRL on the other hand, was built with the anticipation that the university would grow into the new building as students and faculty move-in and occupy it. It should be noted however, that in comparison of actual versus predicted performance, these types of factors may need to be taken into account. Otherwise incorrect analysis and conclusions may result. 29 Kisch, Wendy. "Energy Usage at Iowa State University." Personal interview. 1 Dec pg. 17

19 Energy Intensity Energy Use Intensity (EUI) is a published rating that can be used to benchmark buildings against other buildings to determine if a building is consuming more or less energy than average buildings of similar use based on square footage of building space. The BRL building has a site EUI of kbtu/ft 2, which is very high when compared to the national average site EUI of 67.3 kbtu/ft 2 for office buildings. 30 However, BRL is not a typical office building, rather a large portion of the building is laboratory space. For this reason, it is not reasonable to compare these two values. Similarly, the nature of each building on a university campus is very different, so comparing the EUI of kbtu/ft 2 for BRL to the national average EUI of kbtu/ft 2 for a university building 31 can be misleading. The simple conclusion that lab buildings consume more energy than other typical buildings on a university campus, is not enough to say that lab buildings are not energy efficient. When compared to other building types, laboratory buildings consume a much greater amount of energy due to the inherent design and purpose of the building. Depending on the purpose of a lab, a lab will typically have higher air exchange rates, and exhaust a large amount of air to the outside through the HVAC or fume hoods so chemicals and fumes produced are not recirculated into the air supply stream. For these reasons, there is no widely-accepted national average EUI for comparing energy efficiency of laboratory university buildings, however there are efforts nation-wide to improve the energy performance of labs. The results of this work provide evidence of the need for more analysis of campus building energy performance standards and assumptions, and awareness of the impact that different uses of campus buildings can have on the energy performance. 30 Energy Star. "U.S. Energy Use Intensity by Property Type." Mar Energy Star. "U.S. Energy Use Intensity by Property Type." Mar pg. 18

20 VI. CONCLUSIONS The results of this research conclude that the LEED energy model of the BRL building is fairly accurate for predicting annual energy consumption, but is not very accurate in predicting annual utility costs. This indicates that the assumptions used in the creation of this particular LEED energy model are mostly accurate, except for the utility rate assumption. The LEED energy model was built to prove LEED compliance through energy efficiency, thus it may be necessary to update the model with current utility rates to account for rate fluctuations if FP&M plans to use it as an indicator of future utility costs. For FP&M to reduce the energy consumption and utility cost of their buildings, it appears as though, from the analysis of this building, the focus does not need to be on improving the assumptions of their LEED models, rather they need to focus on improving the design of their building systems. This could be done by increasing the LEED standard for buildings on campus to LEED Platinum, or by finding other ways to incorporate energy efficient design into the systems of current and future buildings. For example, starting a Green Labs Program or considering LEED for Building Operations and Maintenance at the university to create awareness of potential implementations for sustainable practices and operational benefits in buildings (including labs) around campus. 32,33,34,35 This could be potentially accomplished through an increased implementation of energy recovery of exhaust heat from various equipment and air streams that could be applied to precondition incoming air through mechanical means such as an: energy recovery runaround loop, energy recovery ventilator, or energy recovery wheel. Application of these devices must be carefully considered and designed specifically for each building. However, since lab buildings are such high users of energy, they have the most potential for energy savings on campus and may be well worth the investment. Further investigation into the energy model itself and present operation of the building s systems is also needed to determine specific causes of consumption discrepancies within each system. Future Direction Improved building energy models for future buildings on campus will hopefully help ISU FP&M to foresee changes in energy consumption and utility bills for the campus so that they can adjust and plan accordingly. Further analysis of the other LEED buildings on campus will help to determine if the results of the findings in this analysis hold true across campus, or if there are more complex challenges associated with the LEED building assumptions and energy analysis that need to be addressed. 32 GreenHarvard. "Green Labs." Sustainability at Harvard. Harvard University, "CU Green Labs Program." Environmental Center University of Colorado Boulder. 34 "Sustainability UVA, From the Grounds Up." Green Labs. University of Virginia, 23 June LEED. Building Operations Maintenance. U.S. Green Building Council, pg. 19

21 Works Cited ASHRAE Handbook - Fundamentals. Atlanta: American Society of Heating, Refrigerating, and Air- Conditioning Engineers, Print. ASHRAE Standard: Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Atlanta: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Web. Barnaby, Charles S., and Drury B. Crawley. "Weather data for building performance simulation." Building Performance Simulation for Design and Operation (2011): Bhandari, Mahabir, Som Shrestha, and Joshua New. "Evaluation of weather datasets for building energy simulation." Energy and Buildings 49 (2012): "Commercial Reference Buildings." Commercial Reference Buildings Department of Energy. Office of Energy Efficiency & Renewable Energy, n.d. Web. Crawley, Drury B. "Which weather data should you use for energy simulations of commercial buildings?/discussion." ASHRAE Transactions104 (1998): 498. "CU Green Labs Program." Environmental Center University of Colorado Boulder. Greening CU, n.d. Web. "Design & Construction." Design & Construction Office of Sustainability. Iowa State University of Science and Technology, Web. EnergyPlus. "Testing and Validation." Testing and Validation EnergyPlus. U.S. Department of Energy's Building Technologies Office, n.d. Web. EnergyPlus. "Weather Data by Location." Weather Data by Location EnergyPlus. U.S. Department of Energy's Building Technologies Office, n.d. Web. Energy Star. "U.S. Energy Use Intensity by Property Type." Energy Star Portfolio Manager. Energy Star, Mar Web. GreenHarvard. "Green Labs." Sustainability at Harvard. Harvard University, Web. Guan, Lisa. "Preparation of future weather data to study the impact of climate change on buildings." Building and environment 44, no. 4 (2009): Hirsch, James J. "EQUEST." EQUEST. DOE-2, Web. Kisch, Wendy. "Energy Usage at Iowa State University." Personal interview. 1 Dec pg. 20

22 LEED. "Building Operations Maintenance Leadership in Energy & Environmental Design." Building Operations Maintenance Leadership in Energy & Environmental Design. U.S. Green Building Council, Web. Marion, William, and Ken Urban. User's Manual for TMY2s: Typical Meteorological Years. Golden: National Renewable Energy Laboratory, Web. National Centers for Environmental Information (NCEI). "Quality Controlled Datasets." Quality Controlled Datasets. Department of Commerce, n.d. Web. Newsham, Guy R., Sandra Mancini, and Benjamin J. Birt. "Do LEED-certified buildings save energy? Yes, but." Energy and Buildings 41, no. 8 (2009): Office for Sustainability. "Sustainability UVA, From the Grounds Up." Green Labs Program Sustainability at the University of Virginia. University of Virginia, 23 June Web. Reddy, T. Agami. "Literature Review on Calibration of Building Energy Simulation Programs: Uses, Problems, Procedures, Uncertainty, and Tools." ASHRAE transactions (2006). Schwartz, Yair, and Rokia Raslan. "Variations in results of building energy simulation tools, and their impact on BREEAM and LEED ratings: A case study." Energy and Buildings 62 (2013): Scofield, John H. "Do LEED-certified buildings save energy? Not really." Energy and Buildings 41, no. 12 (2009): Scofield, John H. "Efficacy of LEED-certification in reducing energy consumption and greenhouse gas emission for large New York City office buildings." Energy and Buildings 67 (2013): Turner, Cathy, and Mark Frankel. "Energy performance of LEED for new construction buildings." New Buildings Institute 4 (2008): "Utility Billing Charges." Utility Billing. Iowa State University Division of University Services, Web. Wang, Liping, Paul Mathew, and Xiufeng Pang. "Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building." Energy and Buildings 53 (2012): pg. 21

23 APPENDIX A Poster Presentation Figure 13: Poster Presentation pg. 22

24 APPENDIX B Yearly Energy Data Table CHILLED WATER ELECTRIC STEAM TOTAL SOURCE TOTAL HDD-65 TOTAL CDD-65 CHILLED WTR (KBTU) RATE ($/KBTU) COST ELECTRIC (KBTU) RATE ($/KBTU) COST STEAM (KBTU) RATE ($/KBTU) COST TOTAL (KBTU) COST MODEL MODEL ,780, $ 109,839 6,291, $ 142,426 7,704, $ 108,212 21,775,900 $ 360,477 MODEL NORMALIZED MODEL ,639, $ 79,610 6,144, $ 139,099 7,314, $ 102,745 19,098,388 $ 321,454 HISTORIC HISTORIC AVERAGE ,178, $ 85,583 5,980, $ 175,156 7,176, $ 140,765 18,335,509 $ 401,504 HISTORIC ,589, $ 73,034 5,393, $ 153,020 7,399, $ 143,453 17,382,128 $ 369,507 HISTORIC ,638, $ 76,419 5,846, $ 171,011 8,513, $ 166,544 18,998,273 $ 413,975 HISTORIC ,166, $ 87,092 6,351, $ 189,068 6,547, $ 129,342 18,065,571 $ 405,503 HISTORIC ,320, $ 106,551 6,329, $ 188,410 6,245, $ 123,383 18,896,066 $ 418,344 Figure 14: Yearly Energy Data Table pg. 23

25 APPENDIX C Monthly Energy Data Table 2012 HISTORICAL TON-HR KWH LBS KBTU KBTU KBTU MONTH HDD CDD CHILLED WTR ELECTRIC STEAM CHILLED WTR ELECTRIC STEAM HISTORICAL TON-HR KWH LBS KBTU KBTU KBTU MONTH HDD CDD CHILLED WTR ELECTRIC STEAM CHILLED WTR ELECTRIC STEAM HISTORICAL TON-HR KWH LBS KBTU KBTU KBTU MONTH HDD CDD CHILLED WTR ELECTRIC STEAM CHILLED WTR ELECTRIC STEAM HISTORICAL TON-HR KWH LBS KBTU KBTU KBTU MONTH HDD CDD CHILLED WTR ELECTRIC STEAM CHILLED WTR ELECTRIC STEAM Figure 15: Monthly Energy Data Table pg. 24