A statistical approach for post-processing residential building energy simulation output

Abstract Residential building energy simulation (RBES) software plays an important role in evaluating the energy consumption and efficiency potential of homes. These physics-based models are commonly used to assess the energy performance of homes and to predict benefits of making energy-saving improvements to homes a priori. However, software may produce biased estimates of energy consumption for a variety of reasons, including: errors in the measurement and observation of building characteristics; differences in the assumed versus actual occupant behavior; and errors in the physical models and algorithms used in the software. In order to evaluate and improve the accuracy of RBES software, the National Renewable Energy Laboratory (NREL) has assembled a set of approximately 1,250 U.S. homes for which measured energy consumption and audit-collected household energy characteristics are available. Algorithms have also been developed that automatically translate the data from each home into RBES input files so that model predictions of annual electricity and natural gas consumption can be compared to measured values. To assess and improve upon the accuracy of these predictions, we first cluster the homes using weighted, independent linear combinations of these variables and then build multiple linear regressions within clusters of similar homes to model the difference between measured and predicted energy consumption based on the recorded features of the homes. The statistical post-processing techniques that we develop for RBES models have the following benefits: (1) they can identify variables and algorithms that may be causing inaccuracies in the RBES process and (2) they can be used to adjust and improve the RBES predictions.

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