Calibration of building energy computer models via bias-corrected iteratively reweighted least squares method

As the building sector contributes approximately three-quarters of the U.S. electricity load, analyzing buildings’ energy consumption patterns and establishing their effective operational strategy become of great importance. To achieve those goals, a physics-based building energy model (BEM), which can simulate a building’s energy demand under various weather conditions and operational scenarios, has been developed. To obtain accurate simulation outputs, it is necessary to calibrate some parameters required for the BEM’s pre-configuration. The BEM calibration is usually accomplished by matching the simulated energy use with the measured one. However, even with the efforts to calibrate the BEM at best, a systematic discrepancy between the two quantities is often observed, preventing the precise estimation of the energy demand. Such discrepancy is referred to as bias in this study. We present a new calibration approach that models the discrepancy to correct the relationship between the simulated and measured energy use. We show that our bias correction can improve predictive performance. Additionally, we observe the heterogeneous variance in the electricity loads, especially in the afternoon hours, which often reduces prediction accuracy and increases uncertainty. To address this issue, we incorporate heterogeneous weights into the least squares loss function. To implement the bias-correction procedure with the weighted least squares formulation, we propose a newly devised iteratively reweighted least squares algorithm. The effectiveness of the proposed calibration methodology is evaluated with a real-world dataset collected from a residential building in Texas.