International Construction Specialty Conference of the Canadian Society for Civil Engineering (ICSC) (5th : 2015)

A relative energy prediction methodology to support decision making in deep retrofits Gultekin, Pelin; Anumba, Chimay J.; Asce, F.; Leicht, Robert M. Jun 30, 2015

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5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction    Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015   A RELATIVE ENERGY PREDICTION METHODOLOGY TO SUPPORT DECISION MAKING IN DEEP RETROFITS Pelin Gultekin1,4, Chimay J. Anumba2, F.ASCE and Robert M. Leicht3 1 Graduate Research Assistant, Department of Architectural Engineering, The Pennsylvania State University, 104 Engineering Unit A, University Park, PA 16802, USA 2 Professor and Head, Department of Architectural Engineering, The Pennsylvania State University, 104 Engineering Unit A, University Park, PA 16802, USA 3 Assistant Professor, Department of Architectural Engineering, The Pennsylvania State University, 104 Engineering Unit A, University Park, PA 16802, USA 4 pug118@psu.edu  Abstract: Various energy prediction tools and methods are widely used in the design process to reduce the energy consumption of buildings. It is also well known that not only the system selection but also the synergies of these critical system selection decisions have impact on building energy performance. However, these decisions are incorporated into simulations without synergies are considered early in design process. These late evaluations cannot go beyond the projection of energy performance that is already selected for design. Thus, this is a need for a decision support system that stimulates the integration of decisions to achieve higher levels of energy efficiency compared to considering individual measures. In the light of these indicators, this paper presents a critical review of energy conservation measures (ECM) used in retrofit project case studies. The individual impact of changes to these critical ECMs is modeled in EnergyPlus and eQuest. A simplified energy prediction methodology fed by a process model, and the possible synergies are presented. The prediction range is tested in three case studies with different energy performance levels. The calculation algorithm relies on determining individual system performance relative to the ASHRAE standard. This relative system performance evaluation is also useful in defining the scope of system retrofit by comparing options with the standard baseline. It assists collaborative design teams to evaluate the individual impact of system decisions and overall energy saving prediction rate earlier in the design of a variety of deep retrofit projects. 1 INTRODUCTION Approaches to support decision making prediction in energy performance include checklists, databases, and forward and inverse modeling. It is well known that integrated system upgrades result in significantly more energy reduction rates than standard approaches. However, these synergies between decisions are not communicated to all team members in order to be able make energy-efficiency decisions early in the design process. In a typical energy efficient retrofit design, decisions are entered into simulations without collaboration to document and predict the energy performance of design. These late evaluations cannot go beyond the projection of energy performance of what has already been selected for design. Thus, there is a need for a simplified energy analysis methodology to provide instant comparative performance evaluations and to improve decision making by interdisciplinary design teams. 289-1 This paper reviews energy efficiency initiatives and measures in retrofit projects and then presents three case study projects that informed the development of a new approach to comparative energy outcome analysis in deep retrofit projects. The key features of the new approach are presented and the benefits over conventional approaches highlighted. 2 ENERGY EFFICIENCY INITIATIVES 2.1 Energy Efficiency Metrics The energy performance target of a retrofit design affects the criteria used for system selection. These selections are usually based on energy conservation measures (ECMs). An ECM is intended to improve the energy efficiency of building infrastructure, including heating, cooling, and ventilation systems, utility systems, roof, and windows. This is achieved by an engineering investigation to identify potential replacements of, or upgrades to, existing systems that enhance energy efficiency (Kunselman 2013).  In order to evaluate the performance of ECMs, it is important to quantify their impact on building performance. One of the most common quantitative energy comparison measures is energy (Btu) per square foot, energy (Btu) per occupant, and cost per square foot (PNNL 2012). Designers usually make comparisons by using the Btu/sqf per year. These comparisons generally rely on various energy simulation tools, databases and checklists. According to a study done in 2007, there are 330 energy performance evaluation initiatives available using different measures of performance outputs (WBDG 2014). These initiatives include websites, technical publications, software, databases, checklists and matrices. From the literature review on performance evaluation metrics of energy efficient buildings, the most common units of analysis for energy efficiency are as follows: • LEED Scorecard: Certified, Silver, Gold, Platinum (USGBC 2005),  • Normalized Annual Energy Consumption and Energy Use for heating in kWh/m2 (Rey 2004; Zhu 2006), Annual Electricity Use in kWh/m2 (Rey 2004), • Energy and Time consumption Index (ETI) (Chen et al. 2006), • Energy Saving by Retrofitting expressed by (1- Energy/ Energy Baseline) % (Gholap and Khan, 2007). For the purposes of this paper, the energy percentage reduction output measure defined by Gholap and Khan (2007) for energy performance comparisons was used. 2.2  Energy Efficiency Initiatives As mentioned before in the scope of building performance evaluations; checklists, technical publications/standards, and software are the most popular initiatives. These initiatives carry different levels of information and are more beneficial to a certain design phase than others. With the use of different units of analysis, the performance evaluation methodology also varies.  2.2.1 Checklists Checklists and rating systems provide a holistic performance evaluation in addition to energy performance. One of the most common green building rating systems is the Leadership in Energy and Environmental Design (LEEDTM) program that was developed by the United States Green Building Council (USGBC). This program aims to provide third-party verification that a building or community was designed and built using strategies aimed at improving performance across all the metrics that matter most: energy savings, water efficiency, CO2 emissions reduction, improved indoor environmental quality, and stewardship of resources and sensitivity to their impacts (USGBC: What LEEDTM is 2010).  10 bonus credits are available, four of which address regionally specific environmental issues. This individual type of approach provides more flexible and reliable results for green building performance. There are also 289-2 different types of certifications for different types of buildings, such as Core and Shell, and Commercial Interiors.  2.2.2 Technical publications and Standards Technical publications and standards prepared by professional societies, national laboratories, and academicians are also used in the design process as a benchmark and recommendations. The outcomes of these studies are improved on year by year to improve the energy efficiency of buildings. American Society of Heating and Air-Conditioning Engineers (ASHRAE) has developed the ASHRAE 90.1 standard to provide minimum requirements for the energy-efficient design of buildings except low-rise residential buildings (ASHRAE 2007). Various versions of this standard are widely used in the building industry. As seen in an analysis done by a national laboratory (PNNL 2011), ASHRAE 90.1-2010 standard targets a minimum requirement of 19% lower energy use than ASHRAE 90.1-2007, and 24% lower than ASHRAE 90.1-2004. In this research, ASHRAE 90.1-2007 is used as a common baseline to compare case studies. It is on continuous upgrades every three years. The current version is ASHRAE 90.1-2013. In addition to providing minimum requirement standards, ASHRAE is delivering performance and process related recommendations. ASHRAE 189.1 is targeting higher energy efficiency levels for the design of High-Performance, Green Buildings except Low-Rise Residential Buildings.  In addition to these efforts, ASHRAE delivered Advanced Energy Design Guide 30% to 50% to explain and show a way and recommendations to achieve 30% and 50% savings relative to ASHRAE 90.1. Briefly, this guide provides critical ECMs under four main categories: optimize orientation and glazing, use efficient building systems, use efficient energy producing systems, and engage occupants (ASHRAE 2011). These ECMs include energy measures for envelope, lighting, HVAC water and air side, renewable sources, and plug and process loads. This specific technical publication is designed to provide recommendations to achieve 50% energy savings when compared with the minimum code requirements of ASHRAE Standard 90.1-2004. It also gives hints on an efficient process with key design activities as organized below (Table 1).  Table 1: ASHRAE AEDG key design activities ASHRAE AEDG Key Design Activities Project Kick-off and Conceptual Design Set Design Goal (OPR) Perform Site Brainstorm Session Analyze Floor Plate Zoning Schematic Design Exercise Test Fit Study Glazing Study Climate/ Natural Resource Define Behavioral Analysis Unit (BAU) Case  Compare ECM energy use to BAU Find ECMs Design Development Write Basis of Design (BOD) Document  Perform Life Cycle Cost (LCC) Analysis of ECMs Confirm Design Cost Compare BOD to OPR by Commissioning Authority (CxA)   Construction Documents Perform Constructability Review/ Waste Reduction Review Material Sourcing Control Strategy Review by Project Programmer  Update Design Changes Check back Design against OPR by CxA 289-3 2.2.3 Modeling Techniques The use of modeling techniques has increased year by year in order to predict a relative energy efficiency rate to building performance standards. There are four times more building performance simulation (BPS) tools than nearly 15 years ago. However, the increase in tools used by architects in early design is not as high as the increase in the engineering tools used for system design later in the design process (Attia et al. 2012). In particular, the ECMs and user interfaces are specialized according to certain systems and disciplines. In this paper, the aim is to provide a list of ECMs that are meaingful for architects, engineers, and project managers in a collaborative decision making environment.  With regard to energy consumption analysis techniques, there are two types of prediction methods. First, inverse modeling is commonly used more in academia, which is used to predict building parameters such as ECMs by energy use and drivers with statistical models. Secondly, forward modelling is commonly used more in industry to predict energy use by building parameters and environmental drivers such as weather using energy models. There is a need for more case studies to provide valid statistical models rather than energy models. Thus, the forward modeling technique was used due to a limited number of case studies. Also, one of the objectives of this research was to understand the practical context and approaches for the decision making environment of the deep retrofit design process. In-depth process evaluation was prioritized over energy evaluation, which is defined as comparative performance of alternatives rather than precise energy consumption prediction. Since there are around 400 energy modeling tools (DOE Website 2013), designers are willing to use the most realistic prediction tools. However, these tools are not only specialized based on expertise and experience, but also on specific design phases. A study shows that only 1% of BPS tools carry pre-design information (Attia et al. 2012). This shows that the aim of using the tools is to predict the performance of already selected design alternatives. Another study shows that starting energy simulation at the conceptual design phase has greater impact ability on the level of energy efficiency performance than schematics and starting even later at design development phase provided significantly lower level of green performance outcomes (Gultekin et al 2013).   Figure 1: Pearson’s correlation analysis results showing the effect of starting to use energy simulations at various phases of design on high-performance green index (Source: Gultekin et al. 2013) Since the goal of this paper is on improving the collaborative design decision making environment, the focus was placed on the early phases of design. Performance analysis was undertaken with a simplified comparative energy comparison rather than energy prediction. There is still room for improvement in terms of the energy predictions of forward modeling tools. A study compared three widely used energy simulation tools; DOE-2, DeST, and EnergyPlus (Zhu et al. 2013) by using three case studies (simulated cases). Figure 2 shows the discrepancy of annual heating and cooling loads (kWh) with the use of the same building parameters, environmental drivers, and occupancy behavior.  289-4  Figure 2: Prediction comparison of DOE-2, DeST, and EnergyPlus in annual heating and cooling load of same building (Source: Zhu et al. 2013) Even though these there still discrepancies in the performance prediction of these three tools, they are pretty detailed in terms of ECM parameters and calculation algoritms. Since the focus of this study is on a comparative analysis of ECMs, the impact of particular ECMs was tested by simulation using EnergyPlus. For this analysis, DOE Commercial Reference Building’s EnergyPlus data was used to simulate certain technical criteria (NREL 2010). For retrofit comparison basecase, a medium sized office building in Zone 5A (RefBldgMediumOfficePre1980_v1.4) climate region by using State College typical meteorological year TMY weather data. ASHRAE 90.1 Prototype Building Specifications was used as as base case (PNNL 2014). For the selection of critical technical criteria, simplified energy simulation tools that use the most critical ECMs to predict energy were reviewed. Energy-10, developed by National Renewable Energy Laboratory (NREL), was one of the rare early design tools that provides annual energy use information, cost breakdown, and ranking of energy efficiency strategies (Deru et al. 2010). Another simplified energy model is Design Advisor, developed by MIT (2007), which also provides ECM inputs such as: zone configuration, building orientation, room, window, wall, thernal mass, occupancy, and ventilation rates. Its aim is also to improve the understanding of ECMs for various disciplines with a simplified user-interface. These tools still require the input of form, zone, orientation, occupancy, and internal loads. This study is limited to a specific region, form and building type, and excludes orientation and zoning impacts in energy performance analysis.  3 RETROFIT CASE STUDIES In this research, case studies have been found appropriate to gain first-hand experience of the current approaches being adopted and to understand the practical context for decision making in retrofit projects. Three case study projects in Pennsylvania were investigated. Two of the projects were superior examples in terms of the collaborative design process and new technologies used, while one was a typical project that represents the traditional retrofit process. Two of the case studies, Energy Innovation Center (EIC) Retrofit Project and Energy Efficient Buildings Hub (EEB Hub)  Retrofit Project in Pittsburgh and Philadelphia respectively are deep retrofit projects. Both projects are best practice examples regarding the collaborative project delivery process and energy efficiency technologies used. The decision making is transparent due to the aim of delivering showcase projects in terms of energy efficiency technologies and techniques used as well as the process followed. The third case study was of an academic building (HHD) on a university campus, which provided an example of a traditional retrofit process; this enabled the identification of differences from the more modern approaches in the other two case studies through the cross-case analysis. Case studies are selected purposefully to reflect the various levels of energy efficiency, process followed, and tools used.  For the energy performance data, the case studies represented different levels of ASHRAE 90.1 and LEED targets. The two showcase projects represented an integrated project delivery approach based on contractual and informal project processes. The EIC followed a typical design-build approach with the early involvement of the contactor in the design process, while the EEB Hub project was an example of an integrated design process as part of an informal integrated project delivery (IPD) process. On the other hand, the HHD building represented a fragmented design process with a lower 289-5 energy efficiency target as a result of a design-bid-build delivery process. Additionally, the use of various simulation tools in different projects with different efficiency levels is beneficial for our simplified energy performance prediction test comparisons (Table 2). The multiple source calculation algorithms improve the validity of our energy performance range prediction. Table 2: The use of various simulation tools in case studies  Energy Modelling Tool Lighting Modelling Tool Modeled by LEED Documentation and Target ASHRAE Efficiency EIC TraneTrace AGI32 In-house 3rd Party Platinum 90.1 2007 53% EEB eQuest Radiance 3rd Party 3rd Party Gold 90.1 2007 38% HHD HAP AGI32 In-house In-house Silver 90.1.2004 28% 3.1 Case Study Data Source Case studies are the main data source of this research. Since the number of case studies is limited, construction projects were selected purposefully and compared with literature. The decision making environment and decision scope in the case studies were tracked by attendance in the project team meetings. The variety of energy simulation tools provided us to range our predictions with different algorithms. The simulation tools that are used for case study predictions are TraneTrace, Hourly Analysis Program (HAP), and eQuest. 4 CALCULATION ALGORITHM After the key decision points were sequenced into a process model, certain steps that required decision support were refined. The refinement criteria were defined as energy efficiency performance and available technical criteria tracked in project participant interviews and documents.  4.1 System Algorithm  Related critical technical criteria from the key decisions point of view were collected from project documents and reports for all the case studies as follows.   ECM1. Natural Lighting Hours ECM2. Interior Lighting Power Density ECM3. Window-Wall Fraction ECM4. Wall R-Value (assembly) ECM5. Roof R-Value ECM6. Window U-Value ECM7. Shading ECM8. Daylighting controls ECM9. Ventilation Rate ECM10. Chiller Efficiency ECM11. Boiler/Furnace Efficiency ECM12. Heat Recovery ECM13. AHU Fan Type ECM14. Pump Type 289-6 These ECMs were individually tested on Commercial Reference Building model (NREL 2010) as a baseline by using EnergyPlus v8.1 and the energy reports of EEB Hub Project that were created by eQuest. ECMs are taken from energy reports and created after the discussions in expert workshops. Input parameters such as u-value, COP, cfm/sqf are tested in EnergyPlus, and the output parameters such as natural lighting hours %, daylighting controls are taken from eQuest reports.  Finally, the individual system parameter efficiency represents the individual system efficiency (1- Energy/ Energy Baseline)% (Gholap and Khan 2007) is used as a proportion of change in the case study and test options at certain key decision point as in Table 3. Table 3: Individual system efficiency based on test option ECM ECM Unit Baseline Test Option Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100 10 0.9 ECM2 Interior Lighting Power Density W/sqf 1.1 1 0.8 ECM3 Window-Wall Fraction % (<50) 33 20 0.9 ECM4 Wall R-Value (assembly) ft2fh/BTU 5 20 3.4 ECM5 Roof R-Value ft2fh/BTU 20 30 1.3 ECM6 Window U-Value BTU/ft2fh 0.62 0.4 3.2 ECM7 Shading Y/N 0 1 0.4 ECM8 Daylighting controls Y/N 0 1 4.3 ECM9 Ventilation Rate cfm/sqf 0.7 0.5 4.7 ECM10 Chiller Efficiency COP 2.638 3.517 3.8 ECM11 Boiler/Furnace Efficiency % 78 88 6.1 ECM12 Heat Recovery Y/N 0 1 2.7 ECM13 AHU Fan Type kw/cfm 0.000702 0.0005 3.7 ECM14 Pump Type % 85 95 3.9 4.2 Algorithm Testing for Defining Range Since the focus is on comparative energy performance rather than predicting energy consumption amounts, precision was not of prime concern. Based on the energy simulation, the results of EEB HUB, EIC and HHD (38%, 53%, 23%) are tracked in project reports submitted by project team members and compared with energy predictions by adding up individual system parameter efficiency ratings (e.g. 33%, 44%, 19%) The proposed prediction deviates 5%, 9%, 4% from the energy efficiency rates documented in reports, respectively. The precise average of deviation of three projects is 5.82% lower than the reported numbers. The prediction range was rounded to 10% more or less than the DSS prediction, which covers both the average and largest deviations that can result from the impact of interdependent measures.    In EEB HUB prediction calculation, the deviation is 5% which is lower than the average. The calculated prediction and reported performance gap is lower than the average performance deviation. There are two main reasons for such close prediction. First, all baselines and proposed performance criteria of ECMs were captured and documented well during the design of the project. Secondly, there are commonalties between the list of critical ECMs and test project ECMs while setting up the calculation algorithm.  The list of tested case study parameters is documented in Table 4. 289-7 Table 4: EEB HUB ECM comparison of baseline vs proposed design ECM Unit Baseline Proposed Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100 10 0.9 ECM2 Interior Lighting Power Density W/sqf 1.1 2.48 -11.04 ECM3 Window-Wall Fraction % (<50) 13 17.5 -0.31153846 ECM4 Wall R-Value (assembly) ft2fh/BTU 4 24 4.533333333 ECM5 Roof R-Value ft2fh/BTU 0 30 3.9 ECM6 Window U-Value BTU/ft2fh 0.67 0.32 5.090909091 ECM7 Shading Y/N 0 0.4 0.16 ECM8 Daylighting controls Y/N 0 1 4.3 ECM9 Ventilation Rate cfm/sqf 0.6 0.3 7.05 ECM10 Chiller Efficiency COP 2.726 3.517 3.419567691 ECM11 Boiler/Furnace Efficiency % 80 96 9.76 ECM12 Heat Recovery Y/N 0 1 2.7 ECM13 AHU Fan Type kw/cfm 0.000702 0.000661 0.750990099 ECM14 Pump Type % 85 90 1.95  In the EIC prediction calculation, the deviation is 9% which is higher than the average. The prediction and reported performance gap is higher than the average performance deviation, but still in the range of 10% projected energy performance range. The prediction gap can be resulted from the uncertainty of baseline assumptions, which is mark as (*). These baseline criteria were not precisely documented due to inaccessibility of energy model, and flexibility of the project goal and system selections. Since the project is a green demonstration showcase, the interdependencies of the flexible system schedule enabled much higher efficiency than individual system performance. The list of tested case study parameters is documented in Table 5.  Table 5: EIC ECM comparison of baseline vs proposed design ECM Unit Baseline Proposed Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100* 10 0.9 ECM2 Interior Lighting Power Density W/sqf 1.1 2.1 -8 ECM3 Window-Wall Fraction % (<50) 33 23 0.692308 ECM4 Wall R-Value (assembly) ft2fh/BTU 0 20 4.533333 ECM5 Roof R-Value ft2fh/BTU 20* 30 1.3 ECM6 Window U-Value BTU/ft2fh 1.2 0.43 11.2 ECM7 Shading Y/N 0* 0.8 0.32 ECM8 Daylighting controls Y/N 0* 1 4.3 ECM9 Ventilation Rate cfm/sqf 0.6 0.2 9.4 ECM10 Chiller Efficiency COP 2.93 4.4 6.354949 ECM11 Boiler/Furnace Efficiency % 80* 98 10.98 ECM12 Heat Recovery Y/N 0* 1 2.7 289-8 ECM13 AHU Fan Type kw/cfm 0.000702* 0.001 -5.45842 ECM14 Pump Type % 85* 97 4.68  In the HHD prediction calculation, the deviation is 4%, which is lower than the average. The prediction and reported performance gap is lower than the average performance deviation. The prediction gap can be caused by the assumptions (*) made for baseline comparison. The preciseness of the baseline criteria is interrupted with inaccessibility of energy model, and unavailable proposed model parameter. Additionally, 5% ASHRAE 90.1 conversion difference is subtracted from 2004 version 2007 of ASHRAE according PNNL’s recommendation. The list of tested case study parameters is documented in Table 6. Table 6: HHD ECM comparison of baseline vs proposed design ECM Unit Baseline Proposed Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100* 60 0.4 ECM2 Interior Lighting Power Density W/sqf 1.03 0.93 0.8 ECM3 Window-Wall Fraction % (<50) 22.9 23.1 -0.01385 ECM4 Wall R-Value (assembly) ft2fh/BTU 15.38 15.62 0.0544 ECM5 Roof R-Value ft2fh/BTU 20.82 21.28 0.0598 ECM6 Window U-Value BTU/ft2fh 0.45 0.4 0.727273 ECM7 Shading Y/N 0* 0.4 0.16 ECM8 Daylighting controls Y/N 0* 0 0 ECM9 Ventilation Rate cfm/sqf N/A N/A N/A ECM10 Chiller Efficiency COP 2.93 4.11 5.101251 ECM11 Boiler/Furnace Efficiency % 80* 100 12.2 ECM12 Heat Recovery Y/N 0* 0 0 ECM13 AHU Fan Type kw/cfm N/A N/A N/A ECM14 Pump Type % N/A N/A N/A 5 CONCLUSIONS A simplified comparative energy performance analysis methodology for deep retrofit designs has been presented. This study contributes to an integrated design process by providing critical decision stages with regard to energy performance, and describing the resolution of system decision making conflicts by adding criteria impact. This relative system performance evaluation is also useful in defining the scope of system retrofit by presenting a comparison of options relative to a standard baseline. It assists collaborative design teams to evaluate the individual impact of sub-system decisions earlier by identifying potential synergies between sub-systems in the design of deep retrofit projects. The limitations of this research are as follows: • Synergies are tracked for the future step, but not calculated. • ECM impacts are calculated with a single energy model single tool and an energy analysis report with single set of decisions.  • Modeling level of detail is accepted as it is in Commercial Reference Building model. Zoning, occupancy, building type measures are accepted as constant. The prediction will improve with the reflection of these variables impact.  289-9 • The number of deep retrofit case studies is limited to create a complete list of all technical criteria. This study focused on tracking the critical technical criteria which are defined by project team members of three projects and literature review. • Load calculation is analyzed based on final building energy load, not based on sub-systems such as heating, cooling, and lighting. Even though the impact of system on load type varies, the source of energy is accepted as electricity. The future step of this study is developing a decision support system that provides guidance to interdisciplinary teams to analyze their decisions during retrofit design process. This tool will enhance system-based decision making process by comparing their proposed decision alternative individually and energy consumption reduction rate relative to a certain baseline. References American Society of Heating, Refrigerating and Air Conditioning Engineers (2011). “ASHRAE - 2003 HVAC Applications Handbook - Ch. 14 - Laboratories” 〈http://ateam.lbl.gov/Design-Guide/DGHtm/ashrae.2003hvacapplicationshandbook.ch.14.laboratories.htm.〉 (Oct. 10, 2011) Attia, S. Hensen, J.L.M., Beltrán, L., De Herde, A. (2012) ‘Selection criteria for building performance simulation tools: contrasting architects' and engineers' needs’, Journal of Building Performance Simulation, vol 5, no3, pp 155-169. Chen, Z., Clements-Croome, D., Hong, J., Li, H. and Xu, Q. (2006) ‘A multi-criteria lifespan energy efficiency approach to intelligent building assessment’, Energy and Buildings, vol 38, no 5, pp393–409. Department of Energy (DOE), (2008), Energy Efficiency Trends in Residential and Commercial Buildings, available at: 〈http://apps1.eere.energy.gov/buildings/publications/pdfs/corporate/bt_stateindustry.pdf〉 Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., Torcellini, P., Liu, B., Halverson, M., Winiarski, D., Yazdanian, M., Huang, J.., Crawley, D. (2010) U.S. Department of Energy Commercial Reference Building Models of the National Building Stock. NREL; 2011. Accessible link: http://www.nrel.gov/docs/fy11osti/46861.pdf (Access: May 2013). Gholap, A. K. and Khan, J. A. (2007) ‘Design and multi-objective optimization of heat exchangers for refrigerators’, Applied Energy, vol 84, no 12, pp1226–1239. Gultekin, P., Mollaoglu-Korkmaz, S., Riley, D.R., Leicht, R.M. (2013). "Process Indicators to Track High Performance Green Building Project Performance." ASCE Journal of Construction Engineering and Management, 139(12).    Kunselman, S. (2013) ‘Energy Conservation Measures Presentation’, University of Michigan Lawrence Berkeley National Laboratory (LBNL) (2012), “Ready to Retrofit: The Process of Project Team Selection, Building Benchmarking, and Financing Commercial Building Energy Retrofit Projects” 〈http://eetd.lbl.gov/node/50446〉 Pacific Northwest National Laboratory (PNNL) (2012), Advanced Energy Retrofit Guide: Practical Ways to Improve Energy Performance. 〈http://www.pnnl.gov/main/publications/external/technical_reports/pnnl-20761.pdf〉 Rey, E. (2004) ‘Office building retrofitting strategies: Multi-criteria approach of an architectural and technical issue’, Energy and Buildings, vol 36, no 4, pp367–372. U.S. Green Building Council (USGBC). (2009). “USGBC: USGBC research program.” 〈http://www.usgbc.org/DisplayPage.aspx?CMSPageID=1718〉 (Oct. 15, 2010). Zhu, Y. (2006) ‘Applying computer-based simulation energy auditing: A case study’, Energy and Buildings, vol 38, no 5, pp421–428.  289-10  5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction    Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015   A RELATIVE ENERGY PREDICTION METHODOLOGY TO SUPPORT DECISION MAKING IN DEEP RETROFITS Pelin Gultekin1,4, Chimay J. Anumba2, F.ASCE and Robert M. Leicht3 1 Graduate Research Assistant, Department of Architectural Engineering, The Pennsylvania State University, 104 Engineering Unit A, University Park, PA 16802, USA 2 Professor and Head, Department of Architectural Engineering, The Pennsylvania State University, 104 Engineering Unit A, University Park, PA 16802, USA 3 Assistant Professor, Department of Architectural Engineering, The Pennsylvania State University, 104 Engineering Unit A, University Park, PA 16802, USA 4 pug118@psu.edu  Abstract: Various energy prediction tools and methods are widely used in the design process to reduce the energy consumption of buildings. It is also well known that not only the system selection but also the synergies of these critical system selection decisions have impact on building energy performance. However, these decisions are incorporated into simulations without synergies are considered early in design process. These late evaluations cannot go beyond the projection of energy performance that is already selected for design. Thus, this is a need for a decision support system that stimulates the integration of decisions to achieve higher levels of energy efficiency compared to considering individual measures. In the light of these indicators, this paper presents a critical review of energy conservation measures (ECM) used in retrofit project case studies. The individual impact of changes to these critical ECMs is modeled in EnergyPlus and eQuest. A simplified energy prediction methodology fed by a process model, and the possible synergies are presented. The prediction range is tested in three case studies with different energy performance levels. The calculation algorithm relies on determining individual system performance relative to the ASHRAE standard. This relative system performance evaluation is also useful in defining the scope of system retrofit by comparing options with the standard baseline. It assists collaborative design teams to evaluate the individual impact of system decisions and overall energy saving prediction rate earlier in the design of a variety of deep retrofit projects. 1 INTRODUCTION Approaches to support decision making prediction in energy performance include checklists, databases, and forward and inverse modeling. It is well known that integrated system upgrades result in significantly more energy reduction rates than standard approaches. However, these synergies between decisions are not communicated to all team members in order to be able make energy-efficiency decisions early in the design process. In a typical energy efficient retrofit design, decisions are entered into simulations without collaboration to document and predict the energy performance of design. These late evaluations cannot go beyond the projection of energy performance of what has already been selected for design. Thus, there is a need for a simplified energy analysis methodology to provide instant comparative performance evaluations and to improve decision making by interdisciplinary design teams. 289-1 This paper reviews energy efficiency initiatives and measures in retrofit projects and then presents three case study projects that informed the development of a new approach to comparative energy outcome analysis in deep retrofit projects. The key features of the new approach are presented and the benefits over conventional approaches highlighted. 2 ENERGY EFFICIENCY INITIATIVES 2.1 Energy Efficiency Metrics The energy performance target of a retrofit design affects the criteria used for system selection. These selections are usually based on energy conservation measures (ECMs). An ECM is intended to improve the energy efficiency of building infrastructure, including heating, cooling, and ventilation systems, utility systems, roof, and windows. This is achieved by an engineering investigation to identify potential replacements of, or upgrades to, existing systems that enhance energy efficiency (Kunselman 2013).  In order to evaluate the performance of ECMs, it is important to quantify their impact on building performance. One of the most common quantitative energy comparison measures is energy (Btu) per square foot, energy (Btu) per occupant, and cost per square foot (PNNL 2012). Designers usually make comparisons by using the Btu/sqf per year. These comparisons generally rely on various energy simulation tools, databases and checklists. According to a study done in 2007, there are 330 energy performance evaluation initiatives available using different measures of performance outputs (WBDG 2014). These initiatives include websites, technical publications, software, databases, checklists and matrices. From the literature review on performance evaluation metrics of energy efficient buildings, the most common units of analysis for energy efficiency are as follows: • LEED Scorecard: Certified, Silver, Gold, Platinum (USGBC 2005),  • Normalized Annual Energy Consumption and Energy Use for heating in kWh/m2 (Rey 2004; Zhu 2006), Annual Electricity Use in kWh/m2 (Rey 2004), • Energy and Time consumption Index (ETI) (Chen et al. 2006), • Energy Saving by Retrofitting expressed by (1- Energy/ Energy Baseline) % (Gholap and Khan, 2007). For the purposes of this paper, the energy percentage reduction output measure defined by Gholap and Khan (2007) for energy performance comparisons was used. 2.2  Energy Efficiency Initiatives As mentioned before in the scope of building performance evaluations; checklists, technical publications/standards, and software are the most popular initiatives. These initiatives carry different levels of information and are more beneficial to a certain design phase than others. With the use of different units of analysis, the performance evaluation methodology also varies.  2.2.1 Checklists Checklists and rating systems provide a holistic performance evaluation in addition to energy performance. One of the most common green building rating systems is the Leadership in Energy and Environmental Design (LEEDTM) program that was developed by the United States Green Building Council (USGBC). This program aims to provide third-party verification that a building or community was designed and built using strategies aimed at improving performance across all the metrics that matter most: energy savings, water efficiency, CO2 emissions reduction, improved indoor environmental quality, and stewardship of resources and sensitivity to their impacts (USGBC: What LEEDTM is 2010).  10 bonus credits are available, four of which address regionally specific environmental issues. This individual type of approach provides more flexible and reliable results for green building performance. There are also 289-2 different types of certifications for different types of buildings, such as Core and Shell, and Commercial Interiors.  2.2.2 Technical publications and Standards Technical publications and standards prepared by professional societies, national laboratories, and academicians are also used in the design process as a benchmark and recommendations. The outcomes of these studies are improved on year by year to improve the energy efficiency of buildings. American Society of Heating and Air-Conditioning Engineers (ASHRAE) has developed the ASHRAE 90.1 standard to provide minimum requirements for the energy-efficient design of buildings except low-rise residential buildings (ASHRAE 2007). Various versions of this standard are widely used in the building industry. As seen in an analysis done by a national laboratory (PNNL 2011), ASHRAE 90.1-2010 standard targets a minimum requirement of 19% lower energy use than ASHRAE 90.1-2007, and 24% lower than ASHRAE 90.1-2004. In this research, ASHRAE 90.1-2007 is used as a common baseline to compare case studies. It is on continuous upgrades every three years. The current version is ASHRAE 90.1-2013. In addition to providing minimum requirement standards, ASHRAE is delivering performance and process related recommendations. ASHRAE 189.1 is targeting higher energy efficiency levels for the design of High-Performance, Green Buildings except Low-Rise Residential Buildings.  In addition to these efforts, ASHRAE delivered Advanced Energy Design Guide 30% to 50% to explain and show a way and recommendations to achieve 30% and 50% savings relative to ASHRAE 90.1. Briefly, this guide provides critical ECMs under four main categories: optimize orientation and glazing, use efficient building systems, use efficient energy producing systems, and engage occupants (ASHRAE 2011). These ECMs include energy measures for envelope, lighting, HVAC water and air side, renewable sources, and plug and process loads. This specific technical publication is designed to provide recommendations to achieve 50% energy savings when compared with the minimum code requirements of ASHRAE Standard 90.1-2004. It also gives hints on an efficient process with key design activities as organized below (Table 1).  Table 1: ASHRAE AEDG key design activities ASHRAE AEDG Key Design Activities Project Kick-off and Conceptual Design Set Design Goal (OPR) Perform Site Brainstorm Session Analyze Floor Plate Zoning Schematic Design Exercise Test Fit Study Glazing Study Climate/ Natural Resource Define Behavioral Analysis Unit (BAU) Case  Compare ECM energy use to BAU Find ECMs Design Development Write Basis of Design (BOD) Document  Perform Life Cycle Cost (LCC) Analysis of ECMs Confirm Design Cost Compare BOD to OPR by Commissioning Authority (CxA)   Construction Documents Perform Constructability Review/ Waste Reduction Review Material Sourcing Control Strategy Review by Project Programmer  Update Design Changes Check back Design against OPR by CxA 289-3 2.2.3 Modeling Techniques The use of modeling techniques has increased year by year in order to predict a relative energy efficiency rate to building performance standards. There are four times more building performance simulation (BPS) tools than nearly 15 years ago. However, the increase in tools used by architects in early design is not as high as the increase in the engineering tools used for system design later in the design process (Attia et al. 2012). In particular, the ECMs and user interfaces are specialized according to certain systems and disciplines. In this paper, the aim is to provide a list of ECMs that are meaingful for architects, engineers, and project managers in a collaborative decision making environment.  With regard to energy consumption analysis techniques, there are two types of prediction methods. First, inverse modeling is commonly used more in academia, which is used to predict building parameters such as ECMs by energy use and drivers with statistical models. Secondly, forward modelling is commonly used more in industry to predict energy use by building parameters and environmental drivers such as weather using energy models. There is a need for more case studies to provide valid statistical models rather than energy models. Thus, the forward modeling technique was used due to a limited number of case studies. Also, one of the objectives of this research was to understand the practical context and approaches for the decision making environment of the deep retrofit design process. In-depth process evaluation was prioritized over energy evaluation, which is defined as comparative performance of alternatives rather than precise energy consumption prediction. Since there are around 400 energy modeling tools (DOE Website 2013), designers are willing to use the most realistic prediction tools. However, these tools are not only specialized based on expertise and experience, but also on specific design phases. A study shows that only 1% of BPS tools carry pre-design information (Attia et al. 2012). This shows that the aim of using the tools is to predict the performance of already selected design alternatives. Another study shows that starting energy simulation at the conceptual design phase has greater impact ability on the level of energy efficiency performance than schematics and starting even later at design development phase provided significantly lower level of green performance outcomes (Gultekin et al 2013).   Figure 1: Pearson’s correlation analysis results showing the effect of starting to use energy simulations at various phases of design on high-performance green index (Source: Gultekin et al. 2013) Since the goal of this paper is on improving the collaborative design decision making environment, the focus was placed on the early phases of design. Performance analysis was undertaken with a simplified comparative energy comparison rather than energy prediction. There is still room for improvement in terms of the energy predictions of forward modeling tools. A study compared three widely used energy simulation tools; DOE-2, DeST, and EnergyPlus (Zhu et al. 2013) by using three case studies (simulated cases). Figure 2 shows the discrepancy of annual heating and cooling loads (kWh) with the use of the same building parameters, environmental drivers, and occupancy behavior.  289-4  Figure 2: Prediction comparison of DOE-2, DeST, and EnergyPlus in annual heating and cooling load of same building (Source: Zhu et al. 2013) Even though these there still discrepancies in the performance prediction of these three tools, they are pretty detailed in terms of ECM parameters and calculation algoritms. Since the focus of this study is on a comparative analysis of ECMs, the impact of particular ECMs was tested by simulation using EnergyPlus. For this analysis, DOE Commercial Reference Building’s EnergyPlus data was used to simulate certain technical criteria (NREL 2010). For retrofit comparison basecase, a medium sized office building in Zone 5A (RefBldgMediumOfficePre1980_v1.4) climate region by using State College typical meteorological year TMY weather data. ASHRAE 90.1 Prototype Building Specifications was used as as base case (PNNL 2014). For the selection of critical technical criteria, simplified energy simulation tools that use the most critical ECMs to predict energy were reviewed. Energy-10, developed by National Renewable Energy Laboratory (NREL), was one of the rare early design tools that provides annual energy use information, cost breakdown, and ranking of energy efficiency strategies (Deru et al. 2010). Another simplified energy model is Design Advisor, developed by MIT (2007), which also provides ECM inputs such as: zone configuration, building orientation, room, window, wall, thernal mass, occupancy, and ventilation rates. Its aim is also to improve the understanding of ECMs for various disciplines with a simplified user-interface. These tools still require the input of form, zone, orientation, occupancy, and internal loads. This study is limited to a specific region, form and building type, and excludes orientation and zoning impacts in energy performance analysis.  3 RETROFIT CASE STUDIES In this research, case studies have been found appropriate to gain first-hand experience of the current approaches being adopted and to understand the practical context for decision making in retrofit projects. Three case study projects in Pennsylvania were investigated. Two of the projects were superior examples in terms of the collaborative design process and new technologies used, while one was a typical project that represents the traditional retrofit process. Two of the case studies, Energy Innovation Center (EIC) Retrofit Project and Energy Efficient Buildings Hub (EEB Hub)  Retrofit Project in Pittsburgh and Philadelphia respectively are deep retrofit projects. Both projects are best practice examples regarding the collaborative project delivery process and energy efficiency technologies used. The decision making is transparent due to the aim of delivering showcase projects in terms of energy efficiency technologies and techniques used as well as the process followed. The third case study was of an academic building (HHD) on a university campus, which provided an example of a traditional retrofit process; this enabled the identification of differences from the more modern approaches in the other two case studies through the cross-case analysis. Case studies are selected purposefully to reflect the various levels of energy efficiency, process followed, and tools used.  For the energy performance data, the case studies represented different levels of ASHRAE 90.1 and LEED targets. The two showcase projects represented an integrated project delivery approach based on contractual and informal project processes. The EIC followed a typical design-build approach with the early involvement of the contactor in the design process, while the EEB Hub project was an example of an integrated design process as part of an informal integrated project delivery (IPD) process. On the other hand, the HHD building represented a fragmented design process with a lower 289-5 energy efficiency target as a result of a design-bid-build delivery process. Additionally, the use of various simulation tools in different projects with different efficiency levels is beneficial for our simplified energy performance prediction test comparisons (Table 2). The multiple source calculation algorithms improve the validity of our energy performance range prediction. Table 2: The use of various simulation tools in case studies  Energy Modelling Tool Lighting Modelling Tool Modeled by LEED Documentation and Target ASHRAE Efficiency EIC TraneTrace AGI32 In-house 3rd Party Platinum 90.1 2007 53% EEB eQuest Radiance 3rd Party 3rd Party Gold 90.1 2007 38% HHD HAP AGI32 In-house In-house Silver 90.1.2004 28% 3.1 Case Study Data Source Case studies are the main data source of this research. Since the number of case studies is limited, construction projects were selected purposefully and compared with literature. The decision making environment and decision scope in the case studies were tracked by attendance in the project team meetings. The variety of energy simulation tools provided us to range our predictions with different algorithms. The simulation tools that are used for case study predictions are TraneTrace, Hourly Analysis Program (HAP), and eQuest. 4 CALCULATION ALGORITHM After the key decision points were sequenced into a process model, certain steps that required decision support were refined. The refinement criteria were defined as energy efficiency performance and available technical criteria tracked in project participant interviews and documents.  4.1 System Algorithm  Related critical technical criteria from the key decisions point of view were collected from project documents and reports for all the case studies as follows.   ECM1. Natural Lighting Hours ECM2. Interior Lighting Power Density ECM3. Window-Wall Fraction ECM4. Wall R-Value (assembly) ECM5. Roof R-Value ECM6. Window U-Value ECM7. Shading ECM8. Daylighting controls ECM9. Ventilation Rate ECM10. Chiller Efficiency ECM11. Boiler/Furnace Efficiency ECM12. Heat Recovery ECM13. AHU Fan Type ECM14. Pump Type 289-6 These ECMs were individually tested on Commercial Reference Building model (NREL 2010) as a baseline by using EnergyPlus v8.1 and the energy reports of EEB Hub Project that were created by eQuest. ECMs are taken from energy reports and created after the discussions in expert workshops. Input parameters such as u-value, COP, cfm/sqf are tested in EnergyPlus, and the output parameters such as natural lighting hours %, daylighting controls are taken from eQuest reports.  Finally, the individual system parameter efficiency represents the individual system efficiency (1- Energy/ Energy Baseline)% (Gholap and Khan 2007) is used as a proportion of change in the case study and test options at certain key decision point as in Table 3. Table 3: Individual system efficiency based on test option ECM ECM Unit Baseline Test Option Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100 10 0.9 ECM2 Interior Lighting Power Density W/sqf 1.1 1 0.8 ECM3 Window-Wall Fraction % (<50) 33 20 0.9 ECM4 Wall R-Value (assembly) ft2fh/BTU 5 20 3.4 ECM5 Roof R-Value ft2fh/BTU 20 30 1.3 ECM6 Window U-Value BTU/ft2fh 0.62 0.4 3.2 ECM7 Shading Y/N 0 1 0.4 ECM8 Daylighting controls Y/N 0 1 4.3 ECM9 Ventilation Rate cfm/sqf 0.7 0.5 4.7 ECM10 Chiller Efficiency COP 2.638 3.517 3.8 ECM11 Boiler/Furnace Efficiency % 78 88 6.1 ECM12 Heat Recovery Y/N 0 1 2.7 ECM13 AHU Fan Type kw/cfm 0.000702 0.0005 3.7 ECM14 Pump Type % 85 95 3.9 4.2 Algorithm Testing for Defining Range Since the focus is on comparative energy performance rather than predicting energy consumption amounts, precision was not of prime concern. Based on the energy simulation, the results of EEB HUB, EIC and HHD (38%, 53%, 23%) are tracked in project reports submitted by project team members and compared with energy predictions by adding up individual system parameter efficiency ratings (e.g. 33%, 44%, 19%) The proposed prediction deviates 5%, 9%, 4% from the energy efficiency rates documented in reports, respectively. The precise average of deviation of three projects is 5.82% lower than the reported numbers. The prediction range was rounded to 10% more or less than the DSS prediction, which covers both the average and largest deviations that can result from the impact of interdependent measures.    In EEB HUB prediction calculation, the deviation is 5% which is lower than the average. The calculated prediction and reported performance gap is lower than the average performance deviation. There are two main reasons for such close prediction. First, all baselines and proposed performance criteria of ECMs were captured and documented well during the design of the project. Secondly, there are commonalties between the list of critical ECMs and test project ECMs while setting up the calculation algorithm.  The list of tested case study parameters is documented in Table 4. 289-7 Table 4: EEB HUB ECM comparison of baseline vs proposed design ECM Unit Baseline Proposed Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100 10 0.9 ECM2 Interior Lighting Power Density W/sqf 1.1 2.48 -11.04 ECM3 Window-Wall Fraction % (<50) 13 17.5 -0.31153846 ECM4 Wall R-Value (assembly) ft2fh/BTU 4 24 4.533333333 ECM5 Roof R-Value ft2fh/BTU 0 30 3.9 ECM6 Window U-Value BTU/ft2fh 0.67 0.32 5.090909091 ECM7 Shading Y/N 0 0.4 0.16 ECM8 Daylighting controls Y/N 0 1 4.3 ECM9 Ventilation Rate cfm/sqf 0.6 0.3 7.05 ECM10 Chiller Efficiency COP 2.726 3.517 3.419567691 ECM11 Boiler/Furnace Efficiency % 80 96 9.76 ECM12 Heat Recovery Y/N 0 1 2.7 ECM13 AHU Fan Type kw/cfm 0.000702 0.000661 0.750990099 ECM14 Pump Type % 85 90 1.95  In the EIC prediction calculation, the deviation is 9% which is higher than the average. The prediction and reported performance gap is higher than the average performance deviation, but still in the range of 10% projected energy performance range. The prediction gap can be resulted from the uncertainty of baseline assumptions, which is mark as (*). These baseline criteria were not precisely documented due to inaccessibility of energy model, and flexibility of the project goal and system selections. Since the project is a green demonstration showcase, the interdependencies of the flexible system schedule enabled much higher efficiency than individual system performance. The list of tested case study parameters is documented in Table 5.  Table 5: EIC ECM comparison of baseline vs proposed design ECM Unit Baseline Proposed Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100* 10 0.9 ECM2 Interior Lighting Power Density W/sqf 1.1 2.1 -8 ECM3 Window-Wall Fraction % (<50) 33 23 0.692308 ECM4 Wall R-Value (assembly) ft2fh/BTU 0 20 4.533333 ECM5 Roof R-Value ft2fh/BTU 20* 30 1.3 ECM6 Window U-Value BTU/ft2fh 1.2 0.43 11.2 ECM7 Shading Y/N 0* 0.8 0.32 ECM8 Daylighting controls Y/N 0* 1 4.3 ECM9 Ventilation Rate cfm/sqf 0.6 0.2 9.4 ECM10 Chiller Efficiency COP 2.93 4.4 6.354949 ECM11 Boiler/Furnace Efficiency % 80* 98 10.98 ECM12 Heat Recovery Y/N 0* 1 2.7 289-8 ECM13 AHU Fan Type kw/cfm 0.000702* 0.001 -5.45842 ECM14 Pump Type % 85* 97 4.68  In the HHD prediction calculation, the deviation is 4%, which is lower than the average. The prediction and reported performance gap is lower than the average performance deviation. The prediction gap can be caused by the assumptions (*) made for baseline comparison. The preciseness of the baseline criteria is interrupted with inaccessibility of energy model, and unavailable proposed model parameter. Additionally, 5% ASHRAE 90.1 conversion difference is subtracted from 2004 version 2007 of ASHRAE according PNNL’s recommendation. The list of tested case study parameters is documented in Table 6. Table 6: HHD ECM comparison of baseline vs proposed design ECM Unit Baseline Proposed Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100* 60 0.4 ECM2 Interior Lighting Power Density W/sqf 1.03 0.93 0.8 ECM3 Window-Wall Fraction % (<50) 22.9 23.1 -0.01385 ECM4 Wall R-Value (assembly) ft2fh/BTU 15.38 15.62 0.0544 ECM5 Roof R-Value ft2fh/BTU 20.82 21.28 0.0598 ECM6 Window U-Value BTU/ft2fh 0.45 0.4 0.727273 ECM7 Shading Y/N 0* 0.4 0.16 ECM8 Daylighting controls Y/N 0* 0 0 ECM9 Ventilation Rate cfm/sqf N/A N/A N/A ECM10 Chiller Efficiency COP 2.93 4.11 5.101251 ECM11 Boiler/Furnace Efficiency % 80* 100 12.2 ECM12 Heat Recovery Y/N 0* 0 0 ECM13 AHU Fan Type kw/cfm N/A N/A N/A ECM14 Pump Type % N/A N/A N/A 5 CONCLUSIONS A simplified comparative energy performance analysis methodology for deep retrofit designs has been presented. This study contributes to an integrated design process by providing critical decision stages with regard to energy performance, and describing the resolution of system decision making conflicts by adding criteria impact. This relative system performance evaluation is also useful in defining the scope of system retrofit by presenting a comparison of options relative to a standard baseline. It assists collaborative design teams to evaluate the individual impact of sub-system decisions earlier by identifying potential synergies between sub-systems in the design of deep retrofit projects. The limitations of this research are as follows: • Synergies are tracked for the future step, but not calculated. • ECM impacts are calculated with a single energy model single tool and an energy analysis report with single set of decisions.  • Modeling level of detail is accepted as it is in Commercial Reference Building model. Zoning, occupancy, building type measures are accepted as constant. The prediction will improve with the reflection of these variables impact.  289-9 • The number of deep retrofit case studies is limited to create a complete list of all technical criteria. This study focused on tracking the critical technical criteria which are defined by project team members of three projects and literature review. • Load calculation is analyzed based on final building energy load, not based on sub-systems such as heating, cooling, and lighting. Even though the impact of system on load type varies, the source of energy is accepted as electricity. The future step of this study is developing a decision support system that provides guidance to interdisciplinary teams to analyze their decisions during retrofit design process. This tool will enhance system-based decision making process by comparing their proposed decision alternative individually and energy consumption reduction rate relative to a certain baseline. References American Society of Heating, Refrigerating and Air Conditioning Engineers (2011). “ASHRAE - 2003 HVAC Applications Handbook - Ch. 14 - Laboratories” 〈http://ateam.lbl.gov/Design-Guide/DGHtm/ashrae.2003hvacapplicationshandbook.ch.14.laboratories.htm.〉 (Oct. 10, 2011) Attia, S. Hensen, J.L.M., Beltrán, L., De Herde, A. (2012) ‘Selection criteria for building performance simulation tools: contrasting architects' and engineers' needs’, Journal of Building Performance Simulation, vol 5, no3, pp 155-169. Chen, Z., Clements-Croome, D., Hong, J., Li, H. and Xu, Q. (2006) ‘A multi-criteria lifespan energy efficiency approach to intelligent building assessment’, Energy and Buildings, vol 38, no 5, pp393–409. 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(2013) ‘Energy Conservation Measures Presentation’, University of Michigan Lawrence Berkeley National Laboratory (LBNL) (2012), “Ready to Retrofit: The Process of Project Team Selection, Building Benchmarking, and Financing Commercial Building Energy Retrofit Projects” 〈http://eetd.lbl.gov/node/50446〉 Pacific Northwest National Laboratory (PNNL) (2012), Advanced Energy Retrofit Guide: Practical Ways to Improve Energy Performance. 〈http://www.pnnl.gov/main/publications/external/technical_reports/pnnl-20761.pdf〉 Rey, E. (2004) ‘Office building retrofitting strategies: Multi-criteria approach of an architectural and technical issue’, Energy and Buildings, vol 36, no 4, pp367–372. U.S. Green Building Council (USGBC). (2009). “USGBC: USGBC research program.” 〈http://www.usgbc.org/DisplayPage.aspx?CMSPageID=1718〉 (Oct. 15, 2010). Zhu, Y. (2006) ‘Applying computer-based simulation energy auditing: A case study’, Energy and Buildings, vol 38, no 5, pp421–428.  289-10  A Relative Energy Prediction Methodology To Support Decision Making In Deep Retrofits Pelin Gultekin, Chimay J. Anumba & Robert M. Leicht Department of Architectural Engineering The Pennsylvania State University pelin@psu.edu  5th International/11th Construction Specialty Conference     Outline MOTIVATION PROJECT AIM  RESEARCH TIERS CASE STUDIES DSS DEVELOPMENT: Decision Point Window Generation  ENERGY EFFICIENCY PREDICTIONS: Case Studies & Calculations CONTRIBUTIONS  2 Motivation •  Building energy services require significant energy use, about 40 quadrillion Btu (quads) per year.   •  Existing building constituting more than 98% of the building stock, the greatest impact on reducing building energy consumption in the US will result from retrofitting of existing buildings. •  Retrofit projects require more integration due to the need for resolution of the inherent characteristics of existing building and designing “best fit” for these specific characteristics.  •  Hence, the complexity of the process can be improved with transparency of the critical decisions early in the process. Energy Consumption by Sectors HVAC system makes up 51% of total energy use, and lighting represents 25% of total use. 4  (US Energy Information Administration 2006) Buildings 41% Industrial 30% Transportation 29% Consumed Energy by Sectors  (US Energy Information Administration 2009) 35% Heating 10% Cooling 6% Ventilation 25% Lighting Retrofit Measures Energy consumption of buildings built before 1980 can be as high as 300 kW/m2 which is greater than the triple consumption of the modern structure built with the least energy-efficient guidelines (Bournay 2008). 5 0 50 100 150 200 250 300 350 kW/m2 BTU/sqf Energy Consumption Rate for Heating and Hot Water Built < 1980 Built > 1980 For the impacts of system measures, a research compared six buildings with different renovation solutions and their energy usage performances.  (Yin & Menzel 2011) Energy Efficiency Initiatives 6 Website Publication Software Database Checklist/Matrix Total Occurances 103 83 64 62 18 0 20 40 60 80 100 120 Number of initiatives (Adapted: Kaysare WBDG 2007) (Source: Attia et al. 2012) (Source: Gultekin et al. 2011) Project Aim To develop an integrated process model and decision support system that can provide proactive guidance to project managers and integrated teams in undertaking energy-efficient retrofits of existing buildings.     8 To-Be Process Model Decision Support System (DSS) Energy Performance Prediction Research Tiers TIER 1: Early Designà Evaluate Decision Alternatives TIER 2 Simplifiedà Measures for Collaborative Teams  TIER 3 Retrofità Flexible Measures 9 Process Model Energy Prediction 1. Individual ECM calculated  2. Synergies informed Critical Decisions Case Study Technical Criteria Literature Review DSS Case Study Test RMI Retrofit Guide 3 Retrofit Processes To-Be Deep Retrofit  Process ECM   Simplified Tools: E10 & DA Calculation Test+Range Comprehensive Forward Simulation: EnergyPlus TraneTrace eQuest HAP Technical Reports: NL, AIA-DOE ASHRAE (baseline) Commercial Reference Building (PNNL & NREL) 10 Process Model Interviews with Project Team OMNI Class OPP Design Division Case Studies  Decision-Making Environment Energy Plus Calculation eQuest Reports Decision Point Window Generation Sequence Diagram 11 Critical Energy Efficiency Decision Points •  Identify Daylight Opportunities •  Decide Required Levels of Illumination •  Analyze Shape and Proportions •  Identify Envelope Schema  •  Identify Roof Schema  •  Identify Window Schema •  Evaluate Daylighting Strategies •  Evaluate Electrical Load Reduction Strategy •  Identify Ventilation Strategies •  Select Cooling System •  Select Heating System •  Finalize Heating Efficiency •  Finalize Fan Efficiency •  Finalize Pump Efficiency 12 ECM Unit Baseline Option Individual System Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100 10 0.9 ECM2 Interior Lighting Power Density W/sqf 1.1 1 0.8 ECM3 Window-Wall Fraction % (<50) 33 20 0.9 ECM4 Wall R-Value (assembly) ft2fh/BTU 5 20 3.4 ECM5 Roof R-Value ft2fh/BTU 20 30 1.3 ECM6 Window U-Value BTU/ft2fh 0.62 0.4 3.2 ECM7 Shading Y/N 0 1 0.4 ECM8 Daylighting controls Y/N 0 1 4.3 ECM9 Ventilation Rate cfm/sqf 0.7 0.5 4.7 ECM10 Chiller Efficiency COP 2.638 3.517 3.8 ECM11 Boiler/Furnace Efficiency % 78 88 6.1 ECM12 Heat Recovery Y/N 0 1 2.7 ECM13 AHU Fan Type kw/cfm 0.000702 0.0005 3.7 ECM14 Pump Type % 85 95 3.9 Energy Efficient Buildings Hub •  Research Laboratory and Education Facility •  30,000 sqf- Design-Bid-Build •  Target: LEED Gold & 38% reduction in energy consumption of the building  –  Energy Efficient System Selection, Sensory-rich, stimulating and scale appropriate environment –  Daylighting, Natural Ventilation and Lighting, with User-interaction  –  Clear commissioning requirements –  Healing the site; restorative/regenerative; co-evolutionary managing of site  13 Energy Efficient Buildings Hub Calculations 14 ECM Unit Baseline Proposed Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100 10 0.9 ECM2 Interior Lighting Power Density W/sqf 1.1 2.48 -11.04 ECM3 Window-Wall Fraction % (<50) 13 17.5 -0.31153846 ECM4 Wall R-Value (assembly) ft2fh/BTU 4 24 4.533333333 ECM5 Roof R-Value ft2fh/BTU 0 30 3.9 ECM6 Window U-Value BTU/ft2fh 0.67 0.32 5.090909091 ECM7 Shading Y/N 0 0.4 0.16 ECM8 Daylighting controls Y/N 0 1 4.3 ECM9 Ventilation Rate cfm/sqf 0.6 0.3 7.05 ECM10 Chiller Efficiency COP 2.726 3.517 3.419567691 ECM11 Boiler/Furnace Efficiency % 80 96 9.76 ECM12 Heat Recovery Y/N 0 1 2.7 ECM13 AHU Fan Type kw/cfm 0.000702 0.000661 0.750990099 ECM14 Pump Type % 85 90 1.95 Overall: 33% Energy Innovation Center •  Business Incubator: Green Technology Demonstration Showcase under the ownership of Pittsburgh Green Innovators Inc. •  300,000sqf (27,870sqm)- Design Build Delivery •  Target: LEED Platinum & 53% reduction in energy consumption of the building  15 Courtesy of DHA & CJL Energy Innovation Center Calculations ECM Unit Baseline Proposed Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100* 10 0.9 ECM2 Interior Lighting Power Density W/sqf 1.1 2.1 -8 ECM3 Window-Wall Fraction % (<50) 33 23 0.692308 ECM4 Wall R-Value (assembly) ft2fh/BTU 0 20 4.533333 ECM5 Roof R-Value ft2fh/BTU 20* 30 1.3 ECM6 Window U-Value BTU/ft2fh 1.2 0.43 11.2 ECM7 Shading Y/N 0* 0.8 0.32 ECM8 Daylighting controls Y/N 0* 1 4.3 ECM9 Ventilation Rate cfm/sqf 0.6 0.2 9.4 ECM10 Chiller Efficiency COP 2.93 4.4 6.354949 ECM11 Boiler/Furnace Efficiency % 80* 98 10.98 ECM12 Heat Recovery Y/N 0* 1 2.7 ECM13 AHU Fan Type kw/cfm 0.000702* 0.001 -5.45842 ECM14 Pump Type % 85* 97 4.68 16 Overall: 44% Health and Human Development Building •  Research Laboratory and Education Facility •  39,147 sf- Design-Bid-Build (Standard Retrofit Process) •  Target: LEED Silver & 23% reduction in energy consumption of the building •  Include the use of recycled materials, regional materials, and low-emission materials •  Match the architecture of the Henderson North & Old Main 17 Health and Human Development Building Calculations ECM Unit Baseline Proposed Individual System Parameter Efficiency (%) ECM1 Natural Lighting Hours % daylight hours 100* 60 0.4 ECM2 Interior Lighting Power Density W/sqf 1.03 0.93 0.8 ECM3 Window-Wall Fraction % (<50) 22.9 23.1 -0.01385 ECM4 Wall R-Value (assembly) ft2fh/BTU 15.38 15.62 0.0544 ECM5 Roof R-Value ft2fh/BTU 20.82 21.28 0.0598 ECM6 Window U-Value BTU/ft2fh 0.45 0.4 0.727273 ECM7 Shading Y/N 0* 0.4 0.16 ECM8 Daylighting controls Y/N 0* 0 0 ECM9 Ventilation Rate cfm/sqf N/A N/A N/A ECM10 Chiller Efficiency COP 2.93 4.11 5.101251 ECM11 Boiler/Furnace Efficiency % 80* 100 12.2 ECM12 Heat Recovery Y/N 0* 0 0 ECM13 AHU Fan Type kw/cfm N/A N/A N/A ECM14 Pump Type % N/A N/A N/A 18 Overall: 19% Contributions •  This study contributes to an integrated design process by providing critical decision stages with regard to energy performance, and describing the resolution of system decision making conflicts by adding criteria impact.  •  This relative system performance evaluation is also useful in defining the scope of system retrofit by presenting a comparison of options relative to a standard baseline.  •  It assists project managers and collaborative design teams to evaluate the individual impact of sub-system decisions earlier by identifying potential synergies between sub-systems in the design of deep retrofit projects.  19 Thank you 20 References •  American Society of Heating, Refrigerating and Air Conditioning Engineers (2011). “ASHRAE - 2003 HVAC Applications Handbook - Ch. 14 - Laboratories” ?http://ateam.lbl.gov/Design-Guide/DGHtm/ashrae.2003hvacapplicationshandbook.ch.14.laboratories.htm.? (Oct. 10, 2011) •  Attia, S. Hensen, J.L.M., Beltrán, L., De Herde, A. (2012) ‘Selection criteria for building performance simulation tools: contrasting architects' and engineers' needs’, Journal of Building Performance Simulation, vol 5, no3, pp 155-169. •  Bournay, E. (2008), “Energy consumption and CO2 emissions from building”, available at: www. grida.no/graphicslib/detail/energy-consumption-and-co2-emissions-from-building_91a8#. •  Chen, Z., Clements-Croome, D., Hong, J., Li, H. and Xu, Q. (2006) ‘A multi-criteria lifespan energy efficiency approach to intelligent building assessment’, Energy and Buildings, vol 38, no 5, pp393–409. •  Department of Energy (DOE), (2008), Energy Efficiency Trends in Residential and Commercial Buildings, available at: •  ?http://apps1.eere.energy.gov/buildings/publications/pdfs/corporate/bt_stateindustry.pdf? •  Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., Torcellini, P., Liu, B., Halverson, M., Winiarski, D., Yazdanian, M., Huang, J.., Crawley, D. (2010) U.S. Department of Energy Commercial Reference Building Models of the National Building Stock. NREL; 2011. Accessible link: http://www.nrel.gov/docs/fy11osti/46861.pdf (Access: May 2013). •  Gholap, A. K. and Khan, J. A. (2007) ‘Design and multi-objective optimization of heat exchangers for refrigerators’, Applied Energy, vol 84, no 12, pp1226–1239. •  Gultekin, P., Mollaoglu-Korkmaz, S., Riley, D.R., Leicht, R.M. (2013). "Process Indicators to Track High Performance Green Building Project Performance." ASCE Journal of Construction Engineering and Management, 139(12).    •  Kunselman, S. (2013) ‘Energy Conservation Measures Presentation’, University of Michigan •  Lawrence Berkeley National Laboratory (LBNL) (2012), “Ready to Retrofit: The Process of Project Team Selection, Building Benchmarking, and Financing Commercial Building Energy Retrofit Projects” ?http://eetd.lbl.gov/node/50446? •  Pacific Northwest National Laboratory (PNNL) (2012), Advanced Energy Retrofit Guide: Practical Ways to Improve Energy Performance. ?http://www.pnnl.gov/main/publications/external/technical_reports/pnnl-20761.pdf? •  Rey, E. (2004) ‘Office building retrofitting strategies: Multi-criteria approach of an architectural and technical issue’, Energy and Buildings, vol 36, no 4, pp367–372. •  U.S. Green Building Council (USGBC). (2009). “USGBC: USGBC research program.” ?http://www.usgbc.org/DisplayPage.aspx?CMSPageID=1718? (Oct. 15, 2010). •  Yin, H. and Menzel, K., (2011) “Decision Support Model for Building Renovation Strategies”  World Academy of Science, 269-276. •  Zhu, Y. (2006) ‘Applying computer-based simulation energy auditing: A case study’, Energy and Buildings, vol 38, no 5, pp421–428. 21 

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