Deviations between predicted and actual building energy consumption can be attributed to uncertainties introduced by four components of such projections: (1) the accuracy of the underlying models in simulation tools, (2) the accuracy of input parameters describing the design conditions of building envelope and HVAC systems, (3) actual weather, (4) variations in building operation practices. This study investigates uncertainties in energy consumption due to actual weather and building operational practices, using a simulation-based analysis of a medium-size office building. The combined effect of poor practice in building operations across multiple parameters results in an increase in energy use of 49-79% across four selected cities, while good practice reduces energy use by 15-29% across the cities. The impact of year-to-year weather fluctuation on energy use ranges from -4% to 6%. To determine the uncertainty distribution profile for annual energy use, a Monte Carlo method is applied to sample the possible combinations. This study finds that the uncertainty distribution in annual energy consumption approximately follows a log-normal distribution, and shows that the uncertainty range due to operational factors even at an 80% confidence level can dwarf the impact of design features.
15Building energy data has been used for decades to understand energy flows in 16 buildings and plan for future energy demand. Recent market, technology and policy 17 drivers have resulted in widespread data collection by stakeholders across the 18 buildings industry. Consolidation of independently collected and maintained 19 datasets presents a cost-effective opportunity to build a database of unprecedented 20 size. Applications of the data include peer group analysis to evaluate building 21 performance, and data-driven algorithms that use empirical data to estimate energy 22 savings associated with building retrofits. This paper discusses technical 23considerations in compiling such a database using the DOE Buildings Performance 24 Database (BPD) as a case study. We gathered data on over 700,000 residential and 25 commercial buildings. We describe the process and challenges of mapping and 26 cleansing data from disparate sources. We analyze the distributions of buildings in 27 the BPD relative to the Commercial Building Energy Consumption Survey (CBECS) 28 and Residential Energy Consumption Survey (RECS), evaluating peer groups of 29 buildings that are well or poorly represented, and discussing how differences in the 30 distributions of the three datasets impact use-cases of the data. Finally, we discuss 31 the usefulness and limitations of the current dataset and the outlook for increasing 32 its size and applications. 33
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