Commercial building cooling energy forecasting using proactive system identification: A whole building experiment study

Model-based predictive control has been proven to be a promising solution for improving building energy efficiency and building-grid resilience. High fidelity energy forecasting models are essential to the performance of model predictive controls. The existing energy forecasting modeling principles: physics based (white box), data-driven (black box), and hybrid (gray box) modeling principles all have their own limitations in applying into the real field, such as extensive engineering effort, computation power, and long training periods. Previous studies by the authors presented a novel methodology for energy forecasting model development using system identification approaches based on system characteristics. In this study, whole building experiments are systematically designed and conducted to verify and validate this novel method in a real commercial building. The experimental results demonstrate that the proposed methodology is able to achieve 90% forecasting accuracy within a 1-minute calculation time for chiller energy and total cooling energy forecasting in a 1-day forecasting period under the experimental conditions. A Monte Carlo study also shows that the model is more sensitive to outdoor air temperature and direct solar radiation, but less sensitive to ventilation rate.