Abstract
Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, USA, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings’ monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.
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Acknowledgements
This research was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies of the United States Department of Energy, under Contract No. DE-AC02-05CH11231.
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Sun, K., Hong, T., Kim, J. et al. Application and evaluation of a pattern-based building energy model calibration method using public building datasets. Build. Simul. 15, 1385–1400 (2022). https://doi.org/10.1007/s12273-022-0891-2
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DOI: https://doi.org/10.1007/s12273-022-0891-2