Tobias Maile DRAFT – DO NOT DISTRIBUTE Simulation paper 1The contributions of the simulation paper are 1. A definition and list of simulation approximations and a mechanism to identify performance problems from differences between measured and simulated data. 2. Specific evidence of limitations of whole-building simulation tools (in particular EnergyPlus) for use to simulate actual operating conditions. Gaps: Previous research only mentions project specific approximations and does not provide a mechanism to identify performance problems (1). Previous research mentions limitations of simulation tools but does not provide specific evidence for those limitations. (2). Validation: Qualitative evidence for the mechanism to determine performance problems based on assumptions is given through examples that show the ability to characterize differences as performance problems or assumption based differences (assumptions eliminate false positives) (1). We show specific instances of identified software limitations that circumvent the accurate modeling of the real building (2). Domain: These contributions apply to the HVAC domain in non-residential buildings for the task of performance evaluation based the comparison of measurement and predicted data for engineers that evaluate building performance.Tobias Maile DRAFT – DO NOT DISTRIBUTE Simulation paper 2Simulating building energy performance for comparison with measured performance 1 Abstract Building energy performance is often unknown or inadequate given design goals. One concept to evaluate building energy performance is to compare measured with simulated performance data. This paper describes key tasks on how to simulate building energy performance for a comparison with measured data. We describe use of BIM (Building Information Models) to generate whole building energy simulation models, detail specific simulation models of four case studies, provide model simplifications and point out limitations of simulation tools. Previous research provides only project-specific simulation approximations; thus, we developed a generic list of approximations and a mechanism to use approximations to identify performance problems from differences between simulated and measured data. We base our research on a case study research method that provides real-life context of energy performance problems, existing design simulation models and limitations of simulation models. Based on these case studies we provide specific evidence of limitations of current whole building energy performance simulation tools (in particular EnergyPlus). Existing research does only mention some software limitations and does not provide specific evidence for them. 2 Introduction Building energy performance problems are a well-known issue today (Mills et al. 2005). Several studies show that HVAC (Heating, Ventilation and Air Conditioning) systems in buildings do not operate as predicted during design because of performance problems and inappropriate approximations during design (Scofield 2002; Piette et al. 2001; Persson 2005; Kunz et al. 2009). These studies indicate an untapped potential to reduce energy consumption of buildings and highlight the gap between design and actual energy performance of buildings. We compare simulated with measured building energy performance data in order to identify performance problems. The first criterion of simulating building energy performance is the selection of an appropriate simulation tool. Simulation models that are used for the comparison with measured data are typically either on a component (e.g., Xu et al. 2005) or on a building (e.g., Holst 2003) level. Detailed whole building energy simulation tools enable the simulation across different levels of detail from component to building level. A comprehensive list of available simulation tools references 382 exiting building energy software tools (US DOE 2010). These tools cover different simulation areas, have different focus, and cover one or more levels of detail. The selection of a simulation tool that is suitable for a comparison with measured data is a difficult task based on this large number of available tools. Crawley et al. (2008) compare the 20 major whole building simulation tools in detail, but mention the difficulty to compare tools due to inconsistent documentation and different formats that describe feature specifications. Independent of the tool selection, each simulation tool typically has limitations and shortcomings that are only partially known and documented. These limitations are often formulated via assumptions, simplifications, or approximations on project-specific instances. Existing literature mentions specificTobias Maile DRAFT – DO NOT DISTRIBUTE Simulation paper 3simulation approximations (e.g., Mergi 2007), but does not provide a comprehensive list of simulation approximations. We chose a case study based research method to provide real-life context (Yin 2003) for building energy performance problems. With four case studies, we observed current practice of identifying performance problems, of using existing design energy simulation models and of simulating energy performance. Based on the first two case studies we developed hypotheses for a formal definition of approximations, a formal representation, and a comparison methodology. We prospectively validate these hypotheses with two later case studies and compare them to the methods used in practice to illustrate the power of our approach. Multiple case studies of different building types and different HVAC systems provide more generality for our results compared to single case studies. Three of the case studies have been completed recently while one is about 30 years old. One case study focuses on natural ventilation only, two have a mixed natural and mechanical ventilation system and one has a mechanical ventilation system. The 30 year old building has a traditional HVAC system whereas the other three have more innovative systems. Three case studies had existing design simulation models versus one did not have an existing model due to its age. We developed an Energy Performance Comparison Methodology (EPCM) that compares simulated design with measured building performance data to determine differences and identify performance problems. The methodology focuses on building energy performance that depends on both the activities of occupants