Energy Policy 67 (2014) Contents lists available at ScienceDirect. Energy Policy. journal homepage:

Transcription

1 Energy Policy 67 (2014) Contents lists available at ScienceDirect Energy Policy journal homepage: A comprehensive framework to quantify energy savings potential from improved operations of commercial building stocks Elie Azar a,1, Carol C. Menassa b,2,n a Department of Civil and Environmental Engineering, University of Wisconsin-Madison, 2256 Engineering Hall, 1415 Engineering Drive, Madison, WI 53706, USA b Department of Civil and Environmental Engineering, University of Michigan, 2350 Hayward Street, Ann Arbor, MI 48109, USA. H I G H L I G H T S Human actions highly influence energy performance of commercial building stocks. It is challenging to quantify operation-related energy savings potential. The proposed framework quantifies potential energy savings from improved operations. The framework can be applied on any stock of commercial buildings. Applications include support for operation-focused solutions in energy policies. article info Article history: Received 7 October 2013 Received in revised form 10 December 2013 Accepted 12 December 2013 Available online 17 January 2014 Keywords: Energy savings quantification Energy conservation Commercial building stocks Operation-focused interventions Energy management Occupancy interventions abstract While studies highlight the significant impact of actions performed by occupants and facility managers on building energy performance, current policies ignore the importance of human actions and the potential energy savings from a more efficient operation of building systems. This is mainly attributed to the lack of methods that evaluate non-technological drivers of energy use for large stocks of commercial buildings to support policy making efforts. Therefore, this study proposes a scientific approach to quantifying the energy savings potential due to improved operations of any stock of commercial buildings. The proposed framework combines energy modeling techniques, studies on human actions in buildings, and surveying and sampling methods. The contributions of this study to energy policy are significant as they reinforce the role of human actions in energy conservation, and support efforts to integrate operation-focused solutions in energy conservation policy frameworks. The framework 0 s capabilities are illustrated in a case study performed on the stock of office buildings in the United States (US). Results indicate a potential 21 percent reduction in the current energy use levels of these buildings through realistic changes in current building operation patterns. & 2013 Elsevier Ltd. All rights reserved. 1. Introduction Global demand for energy has been rising at accelerating rates over the past decade while fossil fuels are becoming scarce with soaring prices (US Energy Information Administration (EIA), 2013a). The resulting energy crisis, coupled with global warming repercussions, is motivating developed countries to reduce their energy consumption and carbon emissions. In the United States (US) for instance, the building sector presents an important opportunity for large-scale energy savings. Commercial buildings in particular account for 19 percent of national energy consumption and their n Corresponding author. addresses: eazar@wisc.edu (E. Azar), menassa@umich.edu (C.C. Menassa). 1 PhD Candidate. 2 Assistant Professor, John L. Tishman Faculty Scholar. energy demand is growing at a rate higher than any other sector of the economy (US Energy Information Administration (EIA), 2013a; Yudelson, 2010). Similarly, the European Union has identified buildings as the most promising target to improve energy efficiency with commercial buildings again providing the highest potential for energy use reduction (Commission of the European Communities (CEC), 2006). In an effort to achieve large scale energy savings, governments typically rely on energy policy tools that can help conserve energy in thousands of commercial buildings such as appliance standards, building energy codes and labeling, and demand-side management programs (Lopes et al., 2012; Jennings et al., 2011; US Environmental Protection Agency (EPA), 2010; Levine and Urge-Vorsatz, 2007). Traditionally, these tools have used a one-dimensional approach to energy conservation by mainly promoting technological 0 solutions including efficient building envelopes; office equipment; lighting /$ - see front matter & 2013 Elsevier Ltd. All rights reserved.

2 460 E. Azar, C.C. Menassa / Energy Policy 67 (2014) systems; heating, ventilation and air conditioning systems (HVAC); to name a few (Daouas, 2011; US Environmental Protection Agency (EPA), 2010; Escrivá-Escrivá et al., 2010). On the other hand, recent studies show that human actions (both by occupants and facility managers) are major determinants of energy use and could hinder optimal operations of buildings, leading to excessive energy use and defeating the purpose of technological investments (Azar and Menassa, 2012; Augenbroe et al., 2009; Levine and Urge- Vorsatz, 2007). In fact, the lack of understanding and account of human actions has significantly contributed to the observed differences between desired and actual energy levels even when technological strategies are implemented in the building (Augenbroe et al., 2009; Levine and Urge-Vorsatz, 2007; Henze, 2001). As a result, designers, facility managers, researchers, and policy makers are becoming increasingly aware of the need to improve building operations through energy conservation, and integrate the corresponding operation-focused solutionsinenergypolicyframeworks(lopes et al., 2012; Ucci et al., 2012; Cabinet Office, 2011). These solutions can include (1) energy management strategies by facility managers and engineers to optimize the performance of the different building systems (e.g., regular maintenance, energy audits, and energy monitoring) (Colmenar-Santos et al., 2013; Escrivá-Escrivá et al., 2010), or/and (2) occupancy interventions that encourage occupants to adopt energy conservation practices (e.g., energy education and training, feedback techniques, and incentives) (Azar and Menassa, 2012; Carrico and Riemer, 2011). However, such solutions have been rarely integrated in energy policies, limiting their adoption on large-scale levels and leaving their potential energy conservation benefits unexplored (Lopes et al., 2012;Allcott and Mullainathan, 2010;Urge-Vorsatz et al., 2009; Levine and Urge- Vorsatz, 2007). In an effort to identify and overcome the barriers behind the mentioned slow policy adoption, studies have highlighted key factors for the development of successful large-scale energy policy tools, which typically target a large stock of commercial buildings (e.g., city, state, or country) (Lopes et al., 2012; Jennings et al., 2011; Allcott and Mullainathan, 2010;Urge-Vorsatz et al., 2009;Intergovernmental Panel onclimatechange(ipcc),2007). These include the need to: (1) Identify and quantify specific energy savings potential for different building characteristics and energy systems (Lopes et al., 2012; Urge-Vorsatz et al., 2009; Intergovernmental Panel on Climate Change (IPCC), 2007). (2) Scale the projected benefits on the whole targeted stock of buildings in order to explore and support the need for policy adoption, and justify the corresponding investment costs (Allcott and Mullainathan, 2010). (3) Set specific and measurable energy reduction goals and pave pathways to reach them through operation-focused solutions. This will also form a benchmark against which the success of the adopted energy policy can be evaluated and improved (Jennings et al., 2011). Despite the identified importance of quantifying and scaling energy savings potential for a given building stock, this task remains challenging to perform for operation-focused solutions due to several reasons (Lopes et al., 2012; Urge-Vorsatz et al., 2009; Intergovernmental Panel on Climate Change (IPCC), 2007; Levine and Urge-Vorsatz, 2007). First, building energy modeling tools adopt a systems-focused approach to energy use analysis in buildings, typically overlooking the important role that human actions can have in determining building energy performance (Azar and Menassa, 2012; Hoes et al., 2009; Turner and Frankel, 2008). Second, studies that considered human drivers to energy conservation are mostly qualitative, and do not integrate a quantitative energy calculation aspect that generates measurable results for energy policy purposes (Lopes et al., 2012; Ucci et al., 2012; Zhang et al., 2011). For instance, Ucci et al. (2012) developed a theoretical framework of the mechanisms affecting pro-environmental behaviors but did not translate the findings into quantitative energy savings values for a large number of buildings that can motivate energy conservation efforts. Finally, research on quantifying energy savings potential in commercial buildings is limited to few observational case studies with results that are hard to generalize due to the small sample size used (Masoso and Grobler, 2010; Sanchez et al., 2007; Webber et al., 2006). As an example, Webber et al. (2006) found that more than half of office equipment are typically left running in commercial buildings, highlighting the potential energy savings that can be achieved from an improved operation of the buildings. However, the small sample size of 12 buildings limits the generalization of the results. In summary, there is a growing and significant need for a general framework that quantifies and illustrates the energy savings potential from improved operations of a commercial building stock. Such a framework is essential to support policy-making efforts with clear energy conservation targets, which are essential to the integration of operation-focused solutions in energy policy frameworks and the justification of any investment costs (Lopes et al., 2012; Allcott and Mullainathan, 2010; Urge-Vorsatz et al., 2009; Levine and Urge- Vorsatz, 2007). In addition, other decision-makers such as energy utility companies or building stock owners (e.g., universities) can also benefit from the framework to identify energy conservation opportunities in their buildings and develop appropriate and targeted energy conservation strategies (e.g., educational campaigns). 2. Objectives The main goal of this research is to fill the identified gap in literature and develop a framework capable of quantifying the energy savings potential from an improved operation of any given stock of commercial buildings. The proposed framework helps answer the following research questions, which are integral to promote operation-focused solutions in energy conservation policies and initiatives that target a large stock of commercial buildings: (1) How much energy can be saved if more efficient operation patterns are adopted in the studied building stock (e.g., commercial buildings in the US)? Such an evaluation is essential to prove that an improved operation of the buildings can be very beneficial in terms of energy savings and deserves to be further researched and promoted through operationfocused solutions. (2) Within the stock, what type of buildings exhibit the largest energy savings potential (e.g., buildings with specific size, location, age)? The answer to this question helps set priorities on the type of buildings that needs to be targeted first in order to achieve fast and important energy reductions. (3) How is this potential spread on different building systems (e.g., lighting, HVAC, equipment) and different energy sources (e.g., electricity, natural gas)? This additional level of granularity helps policy-makers set specific energy saving goals (e.g., 10 percent reduction in lighting energy use) and avoid setting general targets that are typically harder to achieve and verify. Also, this could help other decision-makers (e.g., utility companies) to better understand and control the demand for specific energy sources such as natural gas. In order to answer those questions, the proposed framework quantifies the impact of human control and actions on the energy performance of different building systems. The framework is scalable to cover all buildings in the studied

3 E. Azar, C.C. Menassa / Energy Policy 67 (2014) building stock, avoiding case-specific results that are hard to generalize. Finally, the framework is general and easily replicable on any stock of commercial buildings. 3. Energy conservation in buildings Prior to presenting the developed methodology, it is first important to discuss the energy conservation principles that motivated this work 0 s focus on the operation of commercial building stocks rather than the traditional technology-focused approach. First, reducing energy use requires an understanding of the different factors that affect building demand for energy and performance. Three main factors are highlighted in literature (Oldewurtel et al., 2012; Peng et al., 2011; Neto and Fiorelli, 2008): (1) building design characteristics (i.e., civil, mechanical, and electrical systems), (2) building operation (i.e., how building managers operate the building systems and how building occupants use the building), and (3) external factors (i.e., weather conditions). Since the latter cannot be controlled nor modified, energy reduction is therefore limited to improving and optimizing the first two factors as discussed below. The first approach to energy conservation, which relates to building design characteristics, uses technology-focused solutions where building systems are designed, renovated or replaced by more energy efficient systems resulting in less energy use. An extensive amount of studies in literature discuss the different possible alternatives such as the use of more efficient building envelopes, office equipment, lighting systems, and HVAC (Escrivá-Escrivá et al., 2010). These technological 0 solutions have been promoted on large scales in different countries through various energy-related policies such as appliance standards, building energy codes and labeling, financial incentives, and public sector energy leadership programs including procurement policies that encourage investments in those solutions (Lopes et al., 2012; Jennings et al., 2011; US Environmental Protection Agency (EPA), 2010; Levine and Urge-Vorsatz, 2007). However, a significant difference remains between the desired energy use levels obtained during design phase and the observed levels during building operation phase, which is referred to in literature as the Energy Efficiency Gap (Lopes et al., 2012). Several factors contribute to this gap such as weather variations, inaccurate modeling of building systems, and building system failures (Ng et al., 2013; Oldewurtel et al., 2012; Granderson et al., 2011). However recent studies indicate that the observed difference can in large parts be attributed to the lack of understanding and control of human actions and controls on the different building systems (Ucci et al., 2012; Carbon Trust, 2006; Ueno et al., 2006). Studies have in fact shown that human actions are major determinants of energy use and could hinder optimal operations of buildings leading to excessive energy use and defeating the purpose of technology-focused investments (Augenbroe et al., 2009; Levine and Urge-Vorsatz, 2007). As a result, there is a growing interest to research operation-focused solutions such as energy management and occupancy interventions and promote their integration in energy policy efforts (Lopes et al., 2012; Ucci et al., 2012; Cabinet Office, 2011). However, as previously discussed, integrating such solutions in energy policies that target a large number or stock of buildings requires a detailed quantification of the potential energy savings that can be achieved across the building stock. Such an evaluation remains limited in literature as shown in the subsequent section, which highlights previous energy savings quantification studies that motivated the need for the proposed framework. 4. Energy savings quantification A review of literature indicates that (1) few studies have quantified energy savings due to improved operation of commercial buildings, and (2) most studies were limited to a small sample size with results that are hard to generalize for a large stock of buildings. These studies are divided into two main categories: energy audits (Masoso and Grobler, 2010; Sanchez et al., 2007; Webber et al., 2006) and feedback strategies (Carrico and Riemer, 2011; Granderson et al., 2011; Staats et al., 2000). For instance, Webber et al. (2006) studied office equipment after-hour usage in 12 different commercial buildings in the US showing that less than 50 percent of office equipment is switched off during non-operating hours. While the authors highlighted a potential for energy savings, they did not quantify it for a large number or stock of buildings to support the need for a largescale energy conservation efforts that promote higher equipment turn-off rates. Similar case-specific results were found in studies by Masoso and Grobler (2010) and Sanchez et al. (2007). These studies identify potential energy savings through a more efficient operation of building systems by occupants and facility managers. The feedback strategies, on the other hand, evaluated the impact of providing building occupants with information related to their energy consumption levels in different contexts to encourage energy conservation (Carrico and Riemer, 2011; Staats et al., 2000). While results were promising with observed energy savings around 7 percent, the design of the studies was limited by small sample sizes and short evaluation periods. Finally, Urge-Vorsatz et al. (2009) summarize the findings of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, which confirms the difficulty in quantifying the impact of human actions on commercial buildings 0 performance. Thus, it is currently challenging to evaluate the potential energy savings from an improved operation of a stock of commercial buildings, a limitation that is believed to have significantly contributed to the low adoption of operation-focused solutions in energy policies and initiatives (Allcott and Mullainathan, 2010). In parallel, some energy quantification studies can be found on residential buildings. However, most studies are again case-specific with a large range of potential energy savings. For instance, a comprehensive review of European studies by Gynther et al. (2012) estimates the potential energy savings from an improved operation around 20 percent from current levels. On the other hand, a world review by Urge-Vorsatz et al. (2009) shows that results could vary by up to 100 percent between case studies. Consequently and similar to the commercial buildings case, these results are hard to generalize in the absence of a framework that can be used to quantify energy savings for a large number of buildings and support the integration of operation-focused solutions in large-scale energy conservation initiatives. Therefore, there is a growing need for a standardized and comprehensive framework that quantifies the potential energy savings benefits from a more efficient operation of any stock of commercial buildings. Policy makers can mostly benefit from such a framework to set specific energy savings goals for their stock of commercial buildings (e.g., 10 percent reduction in lighting use for commercial buildings in Chicago, IL), and finally pave pathways to reach those energy conservation goals through energy conservation policies and initiatives (Lopes et al., 2012; Jennings et al., 2011; Allcott and Mullainathan, 2010; Urge-Vorsatz et al., 2009; Levine and Urge- Vorsatz, 2007). 5. Material and methods In order to evaluate the energy savings potential from an improved operation of a stock of buildings, the proposed methodology combines three areas that have rarely been integrated in literature: energy modeling, existing studies on the impact of human actions and control on energy use, and surveying techniques. Building energy modeling is first used in this study to emulate existing building conditions, since it is impractical and

4 462 E. Azar, C.C. Menassa / Energy Policy 67 (2014) I-a Set scope of building stock to study (e.g., office building in Chicago, IL) Phase I Data gathering and stock aggregation OR I-b Obtain building information from existing databases (e.g., Commercial Building Energy Consumption Survey (CBECS)) I-c Collect building information data (e.g., survey methods, data from utility companies) II-a Develop base case energy models I-d Define typical buildings in the population of interest I-e Calculate weighting factors (actual number of buildings each typical building represents) II-b Calibrate energy models (to reflect current building operation conditions) Phase II Energy modeling and back casting III-b Vary models parameters (to reflect improved building operation conditions) III-c Compare base case and alternative runs (calculate energy savings) III-a Determine potential improved building operation conditions (from Building Codes) III-d Calculate total energy savings potential for the population of interest III-e Sensitivity analysis (vary individual parameters) Phase III Parametric variation Fig. 1. Methodology. extremely expensive to monitor, study, and test the energy performance of a large number of actual buildings (Saporito et al., 2001). This technique has been commonly used in policy decision support contexts such as macro-scale regional and national energy supply assessments or micro-scale engineering approaches (e.g., simulation of technologies use) (Lopes et al., 2012). However, since traditional energy modeling tools often fail to adequately account for human actions and control (Azar and Menassa, 2012), related studies in literature are used in the building energy modeling process to overcome this barrier and effectively quantify the energy savings potential from improved operations of commercial buildings. Finally, sampling weights are used to generalize the obtained results to the entire stock of buildings under study, and provide quantitatively reliable information that can be used to motivate energy conservation policy efforts. The different phases of the methodology are presented in Fig. 1 and detailed in the following sub-sections Phase I Data gathering and stock aggregation The goal of Phase I is to gather the required information on the stock of buildings of interest to develop building energy models that emulate the actual performance of these buildings (Phase II). First, Step I-a starts by setting the scope of the building stock to study (e.g., office buildings in Chicago, IL). Next, information needs to be gathered on the stock of buildings of interest such as building characteristics (e.g., size, location, age, type and size of mechanical and electrical systems), operation-related characteristics (e.g., information from building automation systems or facility managers such as building schedule, number of occupants, settings of HVAC systems, lighting, and equipment), and weather data. As shown in Fig. 1, the needed information can be obtained in two ways. First, when available, existing databases can be used such as the Commercial Buildings Energy Consumption Survey (CBECS), which contains various information on the US stock of commercial buildings (US Energy Information Administration (EIA), 2003a) (Phase I-b). A detailed example on the use of such databases can be found in Azar and Menassa (2012). Also, information can be obtained from other sources such as utility companies, which typically gather information on the stock of buildings they serve, or by developing and conducting new surveys to obtain the needed information (Phase I-c). CBECS can also be used in this case to guide the survey development and distribution tool for other cities or countries around the world (US Energy Information Administration (EIA), 2003a). As with the development of any survey, great care is needed to ensure that the different phases are designed with care including the sampling process (i.e., number of phases and sampling method such as cluster sampling), the survey design, data collection, and post-survey adjustments and analyses (Lohr, 2010; Marsden and Wright, 2010). Next, since it is impractical to individually model every building in the population, Step I-d consists of defining a number of theoretical typical buildings that cover or represent a large number of buildings in the population of interest. These buildings vary according to main buildings characteristics that have an important influence on energy use. The choice of characteristics can be determined through a pilot study where preliminary energy models are developed and building characteristics are varied to track their influence on building energy performance, or from previous studies (Azar and Menassa, 2012; National Renewable Energy Laboratory

5 E. Azar, C.C. Menassa / Energy Policy 67 (2014) (NREL), 2011). Typical building characteristics considered are building size, location, and age, but other characteristics can also be used such as building orientation, construction materials, type of HVAC systems, and proximity of other buildings. As an example, in a building stock of 30 office buildings where only building size influences energy consumption, typical buildings can be defined based on this criterion resulting for instance in one small, one medium, and one large typical building, each representing a portion of the 30 buildings. In Step I-e, the actual number of buildings represented by each typical commercial building is calculated to obtain weighting factors (National Renewable Energy Laboratory (NREL), 2011). These factors are essential to scale the results observed from typical buildings to the entire stock of buildings they represent, as discussed in Phase II. This process starts with the sampling process where a sample is selected to gather information on the population of interest. A theoretical example is shown in Fig. 2, where a simple random sample (SRS) of 6 buildings is chosen from a stock of 30 office buildings. Due to the choice of SRS as a sampling method, each building in the sample has the same probability of being selected and hence can be estimated to represent the same number of actual buildings in the stock (Lohr, 2010). This weight is calculated by dividing the total number of buildings by the sample size (in this case the weight is 5). The weight of each typical building is then obtained by summing the weights of buildings in the sample with similar characteristics. In this example, the obtained weights are 10, 15, and 5 for the small, medium and large typical buildings, respectively. Note that due to sampling errors, these weights do not necessarily reflect the exact actual number of buildings in the stock Phase II Energy modeling The next step after defining typical buildings consists of developing and calibrating building energy models to emulate the energy performance of these typical buildings. This phase results in one building energy model for each typical commercial building extracted from the population, to be used in the parametric variation of Phase III. Step II-a therefore consists of developing building energy models where commercial software tools such as EnergyPlus, equest, and TRNSYS (Crawley et al., 2008) can be used. The inputs for these models correspond to the building and operation characteristics information collected in Phase I, while weather information are typically integrated in most commercial software programs. A detailed model development process can be found in Azar and Menassa (2012) and National Renewable Energy Laboratory (NREL) (2011). The next step consists of calibrating the models to ensure they truly emulate the energy performance of the typical buildings they represent (Step II-b). Different calibration methods are available in literature, most of which use the mean bias error (MBE) as a calibration reference, calculated by averaging the errors between the models predicted energy use and the actual energy use data of the buildings measured on a monthly basis (Azar and Menassa, 2012; Yoon et al., 2003; ASHRAE, 2002). The energy models are then calibrated to reduce the MBE below a certain percentage specified by professional standards or guidelines (e.g., 5 percent) (Yoon et al., 2003; ASHRAE, 2002). At that point, the models become valid for use and for analysis Phase III Parametric variation After developing the base case models that reflect the actual buildings operation conditions, the goal of this phase is to emulate the building performance under alternative operation conditions that can result in lower energy use levels. For example, this could be achieved by setting thermostat temperature set points to levels that avoid excessive cooling or heating loads, reducing equipment and lighting use for unoccupied periods, using natural ventilation, blinds and shades when possible, among other measures (Azar and Menassa, 2012; Moezzi, 2009). Therefore, Step III-a consists of determining alternative building operation conditions while (1) not affecting the work tasks of occupants (e.g., an alternative operation condition that results in reduced working hours cannot be used), (2) meeting building energy standards, and (3) maintaining good indoor conditions and high occupancy comfort levels. Next, the collected information is used in Step III-b as alternative input parameters for the energy models, hence customizing the models to emulate alternative and more efficient operation conditions. Step III-c then consists of running the energy models under the two sets of parameters, base case and alternative, and observing any differences in building energy performance. If the alternative run for one of the models predicts energy levels 10 percent lower than the base case model, then the proposed improved operation conditions for this type of buildings results in 10 percent energy savings. Eq. (1) illustrates this calculation for any of the models parametric variation. Fig. 2. Weighting factors calculation example when typical buildings are chosen based on size.

6 464 E. Azar, C.C. Menassa / Energy Policy 67 (2014) Similar to the calculation for total building energy use and in accordance with the objectives of the study, granular information In Step I-e, CBECS was also used as a starting point to obtain weighting factors for the typical buildings identified in the Total Enegy Savings Model i ¼ Total Enegy Use BASE CASE i Total Enegy Use ALTERNATIVE i Total Enegy Use BASE CASE i 100% ð1þ can also be calculated by energy system (e.g., HVAC, equipment, lighting) or energy type (e.g., electric, natural gas, oil). Eq. (2) illustrates the calculation of lighting energy savings. In Step III-d, and following the calculation of the energy savings potential in each typical building, the weights obtained from Step previous phase. However, while 16 weather zones are considered in the choice of typical buildings (US Department of Energy (DOE), 2010), CBECS simplifies US climate zones to only 5 zones (US Energy Information Administration (EIA), 2003b), complicating the weight determination process. Both the CBECS and the DOE weather zones are shown in Fig. 3. A three-step process is therefore Lighting Enegy Savings Model i ¼ Lighting Enegy Use BASE CASE i Lighting Enegy Use ALTERNATIVE i Lighting Enegy Use BASE CASE i 100% ð2þ I-e are used to scale the results observed in the individual models (Phase III-e) to the entire stock of buildings as shown in Eq. (3). Finally, the sensitivity analysis in Step III-e consists of individually varying each alternative input parameter and tracking the impact on the previously observed building energy savings. This step helps account for potential uncertainty or inaccuracy in the developed to overcome this limitation and spread the weights obtained from CBECS, which are based on 5 weather zones, to the 16 US Department of Energy (DOE) (2010) zones. So first, a visual comparison is made between the two weather maps to determine how the zones overlap. For instance, CBECS Zone 3 corresponds to DOE Zones 4A, 4B, and 4C. Next, data from Jarnagin and Total Enegy Savings Potential ¼ n i ¼ 1 ðenergy Saving Potential Model i Weight Model i Þ n i ¼ 1 Weight Model i ð3þ chosen values for alternative parameters. The results can help decision-makers become aware of the parameters with high influence on building energy performance (e.g., after-hours lighting use), and avoid variations in these parameters, which could hinder energy conservation efforts. 6. Case study This section illustrates the capabilities of the framework by quantifying the energy savings potential in the population of office buildings in the US. The structure of this section is similar to the previous one where the three phases of the methodology are individually presented Phase I application Data gathering and stock aggregation Starting with Step I-a, the scope of this case study was set to US office buildings. CBECS is then used in Step I-b to gather the data required on the stock of buildings of interest to be used in the subsequent phases. A summary of the characteristics is later presented in the results section. In Step I-d, a combination of pilot studies performed by the authors and from literature (Azar and Menassa, 2012; National Renewable Energy Laboratory (NREL), 2011) determined that building size, location, and age, are the main building characteristics with the most influence on building energy performance. As a result, 96 typical office buildings were identified combining 3 building sizes (small, medium, and large), 16 climate zones based on the US Department of Energy (DOE) classification (US Department of Energy (DOE), 2010), and 2 building age categories (pre-1980 and post-1980). Bandyopadhyay (2010) is used to determine how Zone 3, using the same example, is divided between the 3 identified DOE zones (e.g., 4A 30 percent, 4B 20 percent, and 4C 50 percent). The final step consists of using the obtained results to spread the CBECS weights obtained from CBECS data files (US Energy Information Administration (EIA), 2003a) on the 96 typical buildings Phase II application Energy modeling In this phase, the authors make use of benchmark energy models developed by the DOE Commercial Building Initiative in conjunction with three national laboratories (US Department of Energy (DOE), 2013). The initiative used CBECS data to develop benchmark models using EnergyPlus for a large number of commercial buildings with different characteristics and in different US weather zones. It is important to mention that a similar base case development process has been covered in details in a previous work by the authors (Azar and Menassa, 2012). An initial calibration process was performed by the DOE initiative on the technical building design parameters (US Department of Energy (DOE), 2013; National Renewable Energy Laboratory (NREL), 2011). However, given the emphasis of this paper on building operation, the authors used existing studies and building codes (ASHRAE, 2007a, 2007b) to initialize the operation-related parameters of the models to values that emulate the actual operation of the buildings represented. For instance, in order to determine the actual after-hours lighting use in the buildings, the CBECS variable LTNHRP8-Percent lit when closed was analyzed (US Energy Information Administration (EIA), 2003a), showing that on average, office buildings in the US have 10 percent of their lights on

7 E. Azar, C.C. Menassa / Energy Policy 67 (2014) Fig. 3. Weather zones comparison, obtained from US Department of Energy (DOE) (2010) and US Energy Information Administration (EIA) (2003b). Table 1 Summary of operation related input parameters. Input parameters Values for base case energy models, as observed in actual buildings Values for alternative energy models Sources used to determine the alternative parameters Range of values for sensitivity analysis Thermostat temperature set points Occupied Thermostat temperature set points Unoccupied Cooling: 24 1C (75.2 1F) Heating: 21 1C (69.8 1F) Cooling: C (80.1 1F) Cooling: 27 1C (80.6 1F) Heating: 19 1C (66.2 1F) Cooling: C (90.0 1F) ASHRAE and ASHRAE (ASHRAE, 2007a, 2007b) ASHRAE and ASHRAE (ASHRAE, 2007a, 2007b) Heating: C (60.1 1F) Heating: C (55.0 1F) After-hours equipment Weekdays: 40% running Weekdays: 20% CBECS (US Energy Information Administration use running (EIA), 2003a) and Azar and Menassa (2012) Weekends: 30% running Weekends: 15% running After-hours lighting use 10% Running 5% Running CBECS 2003 (US Energy Information Administration (EIA), 2003a) Cooling: C Heating: C Cooling: C Heating: C Weekdays: 10 30% Weekends: 5 25% 0 10% after-hours. A summary of the main operation-related parameters considered are shown in Table 1 and discussed below Phase III application Parametric variation In this phase, an extensive review of literature is performed to determine alternative operation settings that can be promoted through energy management strategies or occupancy interventions in commercial buildings. ASHRAE building energy standards (ASHRAE, 2007a, 2007b) are reviewed to ensure that the proposed settings do not violate building standards and maintain occupants comfort. Three operation-related parameters, which typically have high impacts on building energy performance (Azar and Menassa, 2012), are varied to determine the alternative energy use profile as shown in Table 1: (1)thermostat temperature set points for occupied and unoccupied periods (i.e., after-hours), (2) equipment use for unoccupied periods, and (3) lighting use for unoccupied periods. The first column in Table 1 lists the input parameter categories that are considered. The second column summarizes their average values (i.e., base case scenario) as observedbythedoeinitiative todevelopthedoeenergyplusbasecasemodels.thefollowing two columns contain the recommended values to be used in the alternative scenario and the sources used to justify the selected values. The last column shows the range over which the alternative values are individually varied in the sensitivity analysis. Starting with the thermostat temperature set points for occupied hours, a value of 27 1C (80.6 1F) is proposed for the cooling season and 19 1C(66.21F) for the heating season. These numbers are chosen for two main reasons. First, they are considered acceptable by standard ASHRAE (ASHRAE, 2007a), which sets the requirements for the design and settings of different building systems. Second, they provide a good comfort level for occupants according to standard ASHRAE (ASHRAE, 2007b). In fact, the proposed temperatures would result in a percentage of dissatisfied (PPD) index that is less than 15 percent; where PPD is a quantitative prediction of the percentage of thermally dissatisfied people (ASHRAE, 2007b). SincenostandardsintheUSclearlystatethe recommended thermostat temperatures during occupancy hours, a review of other countries standards was performed to ensure that the suggested temperatures are acceptable. The suggested temperatures fall within the recommended range for several countries such as the United Kingdom (UK), Spain, and Holland (Health and Safety Executive Board (HSE), 2009). As a result, the chosen temperatures are judged adequate to be used in the alternative models.

8 466 E. Azar, C.C. Menassa / Energy Policy 67 (2014) Next, thermostat temperature set points for the unoccupied hours are obtained based on standard ASHRAE (ASHRAE, 2007a), which recommends temperatures between C (551F) and C (90 1F). In this case, comfort level is not an issue as the setting is designed for when the buildings are unoccupied. This assumption is acceptable since set point temperatures in actual US office buildings are already out of the desired comfort range of PPD less than 15 percent (Refer to column 2 in Table 1). The third category covers the after-hours equipment use. The base case energy models used by the DOE have values of 40 percent for week-days and 30 percent for week-ends. In order to ensure the validity of these numbers, data is collected from CBECS (US Energy Information Administration (EIA), 2003a) using a database developed by the authors in a previous study (Azar and Menassa, 2012). An average value of 40 percent after-hours equipment use was obtained for US office buildings, hence confirming the values used in the base case models. As per the alternative values, a 50 percent reduction target from base case levels was used; thus, setting a target reduction at 20 and 15 percent, respectively. The alternative values are in line with what some US office buildings are already using to reduce their energy consumption (US Energy Information Administration (EIA), 2003a). This confirms that these values are realistic and can be set as targets for potential policies. In addition, a sensitivity analysis will be conducted on this target level to take into account any uncertainty and its impact on the projected energy saving potential. Finally, after-hours lighting use is considered where base case models have a value of 10 percent. Alternative values are then set to 5 percent after-hours lighting use, using the same 50 percent reduction target. Here again, CBECS data shows that the reduction targets are realistic since some buildings are already showing after-hours lighting use level as low as 5 percent (US Energy Information Administration (EIA), 2003a). As a result, the chosen Table 2 Summary of energy saving potential by typical building. Energy model Typical building characteristics Weight Total energy savings (%) Energy model Typical building characteristics Weight Total energy savings (%) Location Size Age Location Size Age 1 1A Large Post A Medium Pre1980 1, A Large Post A Medium Pre , B Large Post B Medium Pre1980 4, A Large Post A Medium Pre , B Large Post B Medium Pre1980 5, B Large Post B Medium Pre1980 5, C Large Post C Medium Pre1980 1, A Large Post A Medium Pre , B Large Post B Medium Pre1980 1, C Large Post C Medium Pre1980 4, A Large Post A Medium Pre , B Large Post B Medium Pre , A Large Post A Medium Pre , B Large Post B Medium Pre1980 3, A Large Post A Medium Pre1980 3, A Large Post A Medium Pre A Large Pre A Small Post1980 2, A Large Pre A Small Post , B Large Pre B Small Post1980 8, A Large Pre A Small Post , B Large Pre B Small Post , B Large Pre B Small Post , C Large Pre C Small Post1980 3, A Large Pre A Small Post , B Large Pre B Small Post C Large Pre C Small Post1980 2, A Large Pre A Small Post , B Large Pre B Small Post , A Large Pre A Small Post , B Large Pre B Small Post1980 1, A Large Pre A Small Post1980 1, A Large Pre A Small Post A Medium Post A Small Pre1980 1, A Medium Post A Small Pre , B Medium Post B Small Pre1980 6, A Medium Post A Small Pre , B Medium Post B Small Pre , B Medium Post B Small Pre , C Medium Post C Small Pre1980 3, A Medium Post A Small Pre , B Medium Post B Small Pre1980 2, C Medium Post C Small Pre1980 5, A Medium Post A Small Pre , B Medium Post B Small Pre , A Medium Post A Small Pre , B Medium Post B Small Pre1980 7, A Medium Post A Small Pre1980 7, A Medium Post A Small Pre1980 1,

9 E. Azar, C.C. Menassa / Energy Policy 67 (2014) alterative value is considered valid for this study and can be set as the target of policies that help reduce the after-hours lighting use of a stock of buildings to a value of 5 percent. Once all the operation-related parameters are determined (Step III-a in Fig. 1), a parametric variation is performed on the 96 energy models (Step III-b), varying the operation-related parameters from the base case profiles to the alternative ones using the values from Table 1, and then comparing their energy performance results (Step III-c). The process of running the models and comparing the results was repeated for the 96 pairs of models. Such a large number of operations can be very time consuming and subject to potential human errors. Therefore, the group simulation function in EnergyPlus is used to group the 96 models in one batch that is left to run overnight. Next, a programming code is developed using Visual Basic for Applications (VBA) and is integrated in a Microsoft Excel Macro to collect the results of the simulation. More specifically, the Macro imports the 96 output files one at time and copies the needed results into one large spreadsheet for data analysis. Then, formulas such as the ones shown in Eqs. (1) and (2) are used to calculate potential energy savings for each model (Step III-c). Next, by multiplying the amount of energy saved by each model by the number of actual buildings it represents, then summing the results, the potential total energy that can be saved in US office buildings is obtained (Step III-d). Finally, Step III-e consists of performing a sensitivity analysis on each of the alternative parameters shown in Table 1. This step helps capture the impact of potential uncertainties in the values chosen for alternative parameters. In the absence of standards that specify the range over which the parameters should be varied, it is common to pre-set a fixed level of error or uncertainty. As a discussed in Eisenhower et al. (2012), it is common to use an uncertainty value of 710 percent on the input parameters and track the influence of such change on the outputs. This value was used in this case study and ranges of values were obtained for the different alternative input parameters. For thermostat temperature set points, ranges were computed by directly applying a 710 percent variation to the alternative parameter values that were chosen. As for after-hours equipment and lighting use, since these values already represent a percentage of the time that equipment or lighting systems are used after-hours, values of 10 percent were subtracted and added to the alternative values to generate the ranges for the sensitivity analysis. A summary of the ranges is presented in the last column of Table 1. It is also important to note that in order to avoid a negative value for after-hours lighting use, the range was reduced to 75 percent for this particular parameter. In addition, since the influence of parameters is expected to vary for different building characteristics, sensitivity analysis is repeated on different energy models and the results are discussed in the upcoming section Results and discussion This section summarizes the results obtained from applying the proposed methodology on the case study of US office buildings. Table 2 summarizes (1) the characteristics of the 96 typical office buildings modeled using EnergyPlus, namely the location, size, and age of the buildings, (2) the calculated weights for each model, and (3) the total energy savings potential of each model from applying the parametric variation of Table 1. As shown in Table 2, Model 17, which represents large buildings constructed before 1980 and located in weather Zone 1A, shows the lowest potential in energy savings with 10 percent. Details on how parameters such as building size, age, and location, affect the observed levels of energy savings are presented in subsequent paragraphs. Also in Table 2, Model 65 which represents small buildings constructed after 1980 and also located in Zone 1A, shows the highest energy saving potential with 27 percent. Such information can be very beneficial to policy makers as it highlights the type of buildings that would benefit the most from improved operations and could be the first target of operation-focused energy conservation initiatives. For instance, owners of buildings with high energy savings potential can be required by the DOE to have annual energy audits performed to ensure that their Fig. 4. Energy savings potential by end-use for typical buildings.

10 468 E. Azar, C.C. Menassa / Energy Policy 67 (2014) buildings are within acceptable operation efficiency levels. Also, once additional strategies (e.g., energy trainings for building managers, feedback techniques) are promoted to achieve energy savings, the values in Table 2 can serve as benchmark against which progress is evaluated and adjustments are made. As for the overall energy saving potential in the considered stock of buildings, a value of 21 percent is obtained by applying the individual energy savings from Table 2 in Eq. (3). This means that in US office buildings, an adoption of the alternative operation characteristics of Table 1 is expected to reduce energy consumption by 21 percent. Here again, this quantification can assist the policy making process as it confirms the importance of improving operations and supports the need to integrate operation-focused solutions in large-scale energy conservation policies. Next, in accordance with the objectives of this study, the potential of energy savings in each type of buildings is broken down by end-use, which helps develop targeted and specific energy conservation initiatives. The results are summarized in Fig. 4, where the energy conservation potential is detailed for (1) equipment, (2) HVAC, and (3) lighting energy use. In almost all cases, HVAC shows the highest share of energy savings, especially in small buildings where HVAC typically accounts for the major portion of energy use (Azar and Menassa, 2012). Equipment shows the second highest potential in energy savings, with relatively consistent values across the different building types. Finally, lighting shows the least potential for energy savings with relatively low savings across all the models. This can be attributed to the already low value of 10 percent after-hours lighting use in base case buildings. Such results can assist decision makers in targeting their interventions on the building systems with the highest potential for energy savings. For instance, utility companies serving commercial buildings in warm weather zones would highly benefit from sending out educational information with energy bills (e.g., brochures or s) on how to optimize HVAC controls to avoid high peak-hour energy demands. This would help avoid overloading the grid during peak-hours while reducing energy bills for building owners. Following the break-down by end-use, energy savings are detailed by energy source (i.e., electricity and natural gas). Here again, decision-makers can use the results to determine the type of energy sources they want to target in their energy conservation efforts. This choice can be driven by a multitude of factors related to the type of energy source such as market demand, price, and the environmental impacts associated with the energy production and delivery phases. Furthermore, energy sources such as electricity supply a multitude of end-use systems (e.g., lighting, equipment, and HVAC), making the estimation of its energy savings potentials very valuable (US Energy Information Administration (EIA), 2013b). The results for all of the 96 typical buildings are summarized in Fig. 5. Two main observations can be made from the results. First, in small and large buildings, energy savings in natural gas increase when moving from hot to cold weather climates (e.g., from 1A to 8A). This pattern is expected as large amounts of natural gas are typically needed to heat the buildings in cold climates. Hence, better thermostat settings lead to large reductions in natural gas consumption. The second observation on the other hand is less expected, expressed by an increase in natural gas consumption when the same conservative behavior was adopted in some medium size buildings. More specifically, most medium size buildings constructed after 1980 show an increase in natural gas consumption, highlighted by a negative value for the percentage energy savings (Fig. 5). A more detailed analysis is therefore needed to explain the observed results. Starting with the type of heating systems used in these particular buildings, a gas furnace with electric reheat turns out to be common as opposed to the other types of buildings that only have gas furnaces (Deru et al., 2011; US Energy Information Administration (EIA), 2003a). With such a system, it is very common to have an economizer cycle within the air handling units, which increases air supply whenever outdoor air temperature gets closer to the indoor temperature set point (Wang and Song, 2012). Therefore, by decreasing the thermostat heating set point in the alternative models (Table 1), the economizer increases air flow to reduce overall energy consumption. So, while total energy is reduced, the natural gas portion increases as the air Fig. 5. Energy savings potential by source for typical building.

11 E. Azar, C.C. Menassa / Energy Policy 67 (2014) Fig. 6. Total absolute energy savings per year for all commercial buildings in the US. handling units use more natural gas to heat the incoming air to C (551F) before reheating it to the desired room temperature. More details on the observed phenomenon can be found in Wang and Song (2012). The obtained results can here again help decisionmakers better understand how energy is consumed in their building stock and guide their energy conservation efforts. For instance, for buildings with the above-described characteristics (e.g., new medium-sized buildings in Chicago, IL), guidelines can be sent to facility managers on how to control and optimize the use of the economizer cycle in the air handling units. This can help avoid increases in natural gas consumption during high demand periods such as the winter season. Next, the yearly energy savings in the US are quantified by building type in order to highlight the building type with the highest absolute potential for large-scale energy savings. This is shown in Fig. 6 where the energy saving potential for each typical building is multiplied by its weight, resulting in the total amount of energy that can be saved from actual buildings of similar type. Two particular types of buildings show a significantly high energy savings potential (i.e., 425,000,000 GJ/yr), which are large buildings and medium buildings both located in zone 4A and constructed prior to Five other types of buildings also show relatively high potential (i.e., 415,000,000 GJ/yr), as shown in Fig. 6. This type of results can be useful for decision makers looking to reach specific energy conservation targets in their building stock, guiding them towards the category of buildings with the highest absolute energy savings potential when accounting for the actual number of similar buildings in the population. Finally, the results of the sensitivity analysis are presented in Fig. 7. Three particular buildings were tested. The first building (Small building constructed after 1980 and located in weather zone 1A) was chosen as it has shown the highest overall potential for energy savings (Table 2). Then, since weather conditions can have a significant impact on the sensitivity of models, the sensitivity analysis was repeated on two additional buildings that are similar in size and age but are located in Zones 4A and 8A, respectively. The three buildings have shown overall potential energy savings of 27, 22, and 16 percent respectively (Table 2). As shown in Fig. 7, the influence of each operation parameter is overlaid on the observed total energy savings for each building. For instance, the occupied cooling temperatures parameter shows the highest influence on the first building, resulting in a variation in total building energy savings from 10 to 37 percent. The influence of this parameter was less significant for the two other models (4A and 8A), since moving to colder weather zones decreases the influence of cooling needs and consequently of cooling-related parameters. An inverse trend was observed for heating-related parameters (occupied and unoccupied heating thermostat temperatures), which increase influence when moving to colder zones due to increased heating loads. As per the afterhours lighting and equipment parameters, their influence is largest in the hot weather of Zone 1A. This can be attributed to the heat gain from lighting and equipment use, which highly affects air conditioning loads in hot weather zones. On the other hand, this impact is less significant in cold weather regions, where heat gains can contribute to reducing the required heating loads. 7. Limitations Prior to concluding, it is important to mention some of the limitations of this work that can set the ground for future research. First, evaluating the efficiency of individual operation-focused strategies and interventions is not considered in this paper as it is beyond the scope of this research. The main focus of this paper is on the methodology of quantifying the overall energy savings potential from an improved operation in commercial buildings, which in turn would motivate and justify the need for additional focus and research on different possible solutions and techniques. Second, the building energy models in the case study were obtained from the DOE Commercial Building Initiative since this particular process has already been detailed in a previous work by

12 470 E. Azar, C.C. Menassa / Energy Policy 67 (2014) % 35.0% Influence of Operation Parameters on Energy Savings Potential Thermostat Temperature - Occupied Cooling (OC) Energy Savings Potential [%] 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% OC OH UC UH AE AL OH OC UC UH AE AL OC OH UC AL AE UH Thermostat Temperature - Occupied Heating (OH) Thermostat Temperature - Unoccupied Cooling (UC) Thermostat Temperature - Unoccupied Heating (UH) After-hours Equipment Use (AE) After-hours Lighting Use (AL) 0.0% Small Building - Post Zone 1A Small Building - Post Zone 4A Buildings Small Building - Post Zone 8A Estimated Energy Savings Potential when applying alternative parameter values (shown in 3 rd column of Table 1) Fig. 7. Sensitivity analysis results. the authors (Azar and Menassa, 2012). Therefore, the existing models were initially used to avoid redundancy and dedicate more importance to how energy modeling was combined with previous studies and sampling methods to overcome current barriers in literature on the topic. Third, in the case study, average values were used to determine the occupancy parameters for each type of typical buildings (e.g., afterhours lighting use). In other words, due to the lack of granularity in CBECS data, no variation was considered within the group of buildings represented by each typical building. Accounting for such variations can be considered in the future to better represent the range of values that can be observed in the entire population. Fourth, sensitivity analysis was conducted on a limited number of buildings that showed significant potential for energy savings, and did not include all the 96 types of buildings considered in this study. Such an evaluation is beyond the scope of this paper, and more details on the influence of individual parameters can be found in Azar and Menassa (2012). Nonetheless, the performed sensitivity analysis highlights the impact of potential uncertainties in the values of alternative parameters, and presents a methodology that can be replicated on any building energy model. Finally, the authors did not consider all building operation parameters that can be improved to conserve energy. Future research can therefore integrate additional parameters (e.g., elevator use, opening of windows, use of blinds and day lighting), which are expected to increase energy savings estimates and make an even stronger case to the potential energy savings from improved operations. 8. Conclusions and policy implications This paper presents a framework capable of quantifying the energy savings potential from a more efficient operation of commercial buildings energy systems. Prior to this study, it was challenging to measure operation-related energy conservation opportunities, contributing to the low adoption of techniques such as energy management and occupancy interventions in commercial buildings. The proposed framework is general and can be applied on any group of commercial buildings as illustrated in a case study on US office buildings. The contributions of this paper to energy policy are significant as they fill several gaps identified in literature. First, the proposed methods can be used by policy makers to set clear energy conservation goals and develop a pathway to reach them. Second, the framework provides detailed information about the potential energy savings by end-use and energy source, hence allowing detailed and targeted objectives to be set. Third, the scalability potential of the framework provides policy makers with the ability to aim and plan for large-scale energy savings initiatives (e.g., city, state, country) rather than focusing on individual buildings. In addition, other stakeholders can also benefit from the framework such as utility companies (e.g., for energy load leveling) or educational institutions (e.g., campus-wide energy conservation efforts). Moreover, quantifying the energy savings potential of human actions is expected to increase interest and boost research on the different techniques that can be used to achieve those energy savings. Future research therefore includes investigating, testing, and optimizing different energy management strategies and occupancy interventions. This type of research has so far been very limited in commercial buildings. However, with the proposed framework s ability to evaluate a building stock s energy conservation opportunities, researchers can now motivate and focus their efforts on improving and optimizing specific energy conservation methods to achieve the desired energy savings. In parallel to optimizing the discussed operation-focused solutions, additional research is required to develop and adapt policy instruments to promote those solutions on large commercial building stocks. Several policy instruments have been used in the past for residential buildings and include public leadership programs, awareness raising and information campaigns, mandatory audit and energy management programs, in addition to demand side management programs (Levine and Urge-Vorsatz, 2007). The benefit-cost ratioofsuchprogramsvarywidelyasitdependsonseveralfactors

13 E. Azar, C.C. Menassa / Energy Policy 67 (2014) such as their design, their interactions with other policy instruments, local regulations, level of enforcement, among others (Urge-Vorsatz et al., 2009). Nonetheless, a review of the effectiveness of these programs on residential buildings indicates important benefits with costsaslowas8us$/tco 2 (UK study conducted in 2005) (Levine and Urge-Vorsatz, 2007). The observed values are very promising, especially when compared to other traditional CO 2 abatement methods such as building codes (more than 46 US$/tCO 2 ), increased wind power generation (20 US$/tCO 2 ), or carbon storage in new coal power plants (44 US$/tCO 2 ) (US study conducted in 2009) (Allcott and Mullainathan, 2010; Granade et al., 2009). The observed results in the residential building sector cannot be directly translated to commercial buildings, but can be used to emphasize the price difference and cost effectiveness of such programs over other possible solutions to reduce building energy and carbon footprint. In addition, these results motivate the need for additional efforts to develop energy policy instruments for commercial buildings, estimate their benefits, and promote their application on a large-scale. Finally, as discussed by Allcott and Mullainathan (2010) and Levine and Urge-Vorsatz (2007), governments can start by funding potentially high-impact operation-focused energy conservation measures in commercial buildings, as part of their broader support for energy innovation. In the US, a bill was introduced in 2009 (US House of Representatives, HR 3247) to establish the first program at the Department of Energy (DOE) to understand behavioral factors that influence energy conservation and speed the adoption of promising initiatives. While this promising bill failed to be enacted, it can still serve as an example of the next step that needs to be taken to promote and eventually integrate operation-focused solutions in energy policies. Acknowledgments The authors would like to acknowledge the financial support for this research received from the US National Science Foundation (NSF) CBET and CMMI-BRIGE awards, and the Wisconsin Alumni Research Foundation (WARF). Any opinions and findings in this paper are those of the authors and do not necessarily represent those of NSF or WARF. References Allcott, H., Mullainathan, S., Behavior and energy policy. Science 327, ASHRAE, 2007a , Energy Standard for Buildings Except Low-Rise Residential Buildings. American Society of Heating Refrigerating and Air- Conditioning Engineers Inc., Atlanta, GA. ASHRAE, 2007b , Thermal Environment Conditions for Human Occupancy. American Society of Heating Refrigerating and Air-Conditioning Engineers Inc., Atlanta, GA. ASHRAE, Guideline , Measurement of Energy and Demand Savings. American Society of Heating Refrigerating and Air-Conditioning Engineers Inc., Atlanta, GA. Augenbroe, G., Castro, D., Ramkrishnan, K., Decision model for energy performance improvements in existing buildings. J. Eng. Des. Technol. 7, Azar, E., Menassa, C.C., A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings. Energy Build. 55, Cabinet Office, Behaviour Change and Energy Use. UK Government, London, UK. Carbon Trust, The UK Climate Change Programme: Potential Evolution for Business and the Public Sector. Carbon Trust, Cambridge, UK. Carrico, A.R., Riemer, M., Motivating energy conservation in the workplace: an evaluation of the use of group-level feedback and peer education. J. Environ. Psychol. 31, Colmenar-Santos, A., Terán de Lober, L.N., Borge-Diez, D., Castro-Gil, M., Solutions to reduce energy consumption in the management of large buildings. Energy Build. 56, Commission of the European Communities (CEC), Communication from the Commission Action Plan for Energy Efficiency: Realising the Potential. CEC, Brussels, Belgium. Crawley, D.B., Hand, J.W., Kummert, M., Griffith, B.T., Contrasting the capabilities of building energy performance simulation programs. Build. Environ. 43, Daouas, N., A study on optimum insulation thickness in walls and energy savings in Tunisian buildings based on analytical calculation of cooling and heating transmission loads. Appl. Energy 88, Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., Torcellini, P., Liu, B., Halverson, M., Winiarski, D., Rosenberg, M., U.S. Department of Energy Commercial Reference Building Models of the National Building Stock. National Renewable Energy Laboratory, Golden, CO. Eisenhower, B., Neill, Z.O., Fonoberov, V.A., Mezi, I., Uncertainty and sensitivity decomposition of building energy models. Building Perform. Simul. 5 (3), Escrivá-Escrivá, G., Segura-Heras, I., Alcázar-Ortega, M., Application of an energy management and control system to assess the potential of different control strategies in HVAC systems. Energy Build. 42, Granade, H.C., et al., Unlocking Energy Efficiency in the U.S. Economy. McKinsey & Co., New York, NY. Granderson, J., Piette, M.A., Ghatikar, G., Building energy information systems: user case studies. Energ. Effic. 4 (1), Gynther, L., Mikkonen, I., Smits, A., Evaluation of European energy behavioural change programmes. Energ. Effic. 5, Health and Safety Executive Board (HSE), HSE Review of the Current Approach to Workplace Temperatures. UK HSE, London, UK. Henze, G.P., Building energy management as continuous quality. Archit. Eng. 7, Hoes, P., Hensen, J.L.M., Loomans, M., De Vries, B., Bourgeois, D., User behavior in whole building simulation. Energy Build. 41 (3), Intergovernmental Panel on Climate Change (IPCC), IPCC Fourth Assessment Report (AR4). IPCC, Geneva, Switzerland. Jarnagin, R.E., Bandyopadhyay, G.K., Weighting Factors for the Commercial Building Prototypes Used in the Development of ANSI/ASHRAE/IENSA Standard Pacific Northwest National Laboratory, Richland, WA. Jennings, A.M., Hirst, N., Gambhir, A., Reduction of Carbon Dioxide Emissions in the Global Building Sector to Grantham Institute for Climate Change, London, UK. Levine, M., Urge-Vorsatz, D., Residential and commercial buildings. In: Climate Change 2007: Mitigation. Cambridge University Press, Cambridge, UK. Lohr, S.L., Sampling: Design and Analysis. Cengage Learning, Stamford, CT. Lopes, M.a.R., Antunes, C.H., Martins, N., Energy behaviours as promoters of energy efficiency: a 21st century review. Renewable Sustainable Energy Rev. 16 (6), Marsden, P.V, Wright, J.D., Handbook of Survey Research. Emerald Group Publishing, Bradford, UK. Masoso, O.T., Grobler, L.J., The dark side of occupants behaviour on building energy use. Energy Build. 42 (2), Moezzi, M., Behavioral Assumptions in Energy Efficiency Potential Studies. California Institute for Energy and Environment, Oakland, CA. Ng, L.C., Musser, A., Persily, A.K., Emmerich, S.J., Multizone airflow models for calculating infiltration rates in commercial reference buildings. Energy Build. 58, National Renewable Energy Laboratory (NREL), U.S. Department of Energy Commercial Reference Building Models of the National Building Stock. NREL, Golden, CO. Neto, A.H., Fiorelli, F.A.S., Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy Build. 40, Oldewurtel, F., Parisio, A., Jones, C.N., Gyalistras, D., Gwerder, M., Stauch, V., Lehmann, B., Morari, M., Use of model predictive control and weather forecasts for energy efficient building climate control. Energy Build. 45, Peng, C., Yan, D., Wu, R., Wang, C., Zhou, X., Jiang, Y., Quantitative description and simulation of human behavior in residential buildings. Build. Simul. 5, Sanchez, M., Webber, C., Brown, R., Busch, J., Pinckard, M., Roberson, J., Space heaters, computers, cell phone chargers: how plugged in are commercial buildings? In: 2006 ACEEE Summer Study on Energy Efficiency in Buildings, Less is More, En Route to Zero Energy Buildings, Berkeley, CA. Saporito, A., Day, A.R., Karayiannis, T.G., Parand, F., Multi-parameter building thermal analysis using the lattice method for global optimisation. Energy Build. 33, Staats, H., Van Leeuwen, E., Wit, A., A longitudinal study of informational interventions to save energy in an office building. J. Appl. Behav. Anal. 33, Turner, C., Frankel, M., Energy Performance of LEED for New Construction Buildings. New Buildings Institute, Vancouver, WA. Ucci, M., Domenech, T., Ball, A., Whitley, T., Wright, C., Mason, D., Corrin, K., Milligan, A., Rogers, A., Fitzsimons, D., Gaggero, C., Westaway, A., Behaviour change potential for energy saving in non-domestic buildings: development and pilot-testing of a benchmarking tool. Build. Serv. Eng. Res. Technol., Ueno, T., Sano, F., Saeki, O., Tsuji, K., Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data. Appl. Energy 83, Urge-Vorsatz, D., Novikova, A., Koeppel, S., Boza-Kiss, B., Bottom-up assessment of potentials and costs of CO 2 emission mitigation in the buildings sector: insights into the missing elements. Energ. Effic. 2,

14 472 E. Azar, C.C. Menassa / Energy Policy 67 (2014) US Department of Energy (DOE), Guide to Determining Climate Regions by County. DOE, Washington, DC. US Department of Energy (DOE), Commercial Building Initiative. DOE, Washington, DC. US Energy Information Administration (EIA), 2003a. Commercial Buildings Energy Consumption Survey (CBECS). EIA, Washington, DC. US Energy Information Administration (EIA), 2003b. U. S. Climate Zones for 2003 CBECS. EIA, Washington, DC. US Energy Information Administration (EIA), 2013a Annual Energy Outlook. EIA, Washington, DC. US Energy Information Administration (EIA), 2013b. Energy Explained, Your Guide to Understanding Energy. EIA, Washington, DC plained/index.cfm. US Environmental Protection Agency (EPA), ENERGY STAR s and Other Climate Protection Partnerships 2010 Annual Report. EPA, Washington, DC. Wang, G., Song, L., Air handling unit supply air temperature optimal control during economizer cycles. Energy Build. 49, Webber, C.A., Roberson, J.A., McWhinney, M.C., Brown, R.E., Pinckard, M.J., Busch, J. F., After-hours power status of office equipment in the USA. Energy 31, Yudelson, J., Greening Existing Buildings. Green Source/McGraw-Hill, New York, NY. Yoon, J., Lee, E.J., Claridge, D.E., Calibration procedure for energy performance simulation of a commercial building. J. Sol. Energy Eng. 125, 251. Zhang, T., Siebers, P.-O., Aickelin, U., Modelling electricity consumption in office buildings: an agent based approach. Energy Build. 43 (10),