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Simulation-based evaluation and analysis of building system control performance and associated annual energy estimations can become both resource and timeintensive for advanced optimization-based control algorithms. Here we devise a data-driven analysis approach to select a representative subset of test days in a test year, such that those representative days capture seasonal weather variability patterns. We then only compute the energy performance for these representative days and use this reduced data set to compute approximate energy performance estimates for the remaining days through standard prediction approaches. This approach enables us to compute approximate annual energy estimations for building control systems with reduced computation requirements and good accuracy. We evaluate the proposed approach for a few building types and under differing climatic conditions.

Assessing Building Energy Performance via Selection of Representative Simulation Days