• Energy Research

Building energy modeling is essential for designing energy-efficient and flexible buildings that seamlessly integrate with the electrical grid. This study introduces a data-driven, control-oriented methodology using Resistance-Capacitance thermal network models to accurately forecast building thermal loads. It differentiates the impacts of fast and slow dynamics associated with different heating types—radiant and/or convective. A Model Predictive Control (MPC) framework optimizes coordination between the different building thermal dynamics, considering weather forecasts and price signals. The Varennes Library, a Net Zero Energy Institutional Building located in Québec (Canada), serves as a case study for performance assessment. Validation of the developed model demonstrates its efficacy in enabling MPC to formulate effective control strategies. Findings reveal that high-mass radiant heating is strategically used before indoor setpoint variation or demand response events. Up to 70% of the building thermal load is delivered to the active envelope for off-peak heat storage and on-peak release. Conversely the ventilation heating is prioritized in proximity of the change in setpoint or grid tariff with percentages over 80%. Results show the adoption of weather clusters for generalizing the optimal control setting, highlighting their influence on thermal loads while maintaining robust ventilation and active envelope heating coordination. The comparison between the predictive control strategy and the existing rule-based control shows improvements in indoor temperature and energy flexibility. During the MPC routine, a constant price signal reduces grid stress, achieving Load Factor (LF) values up to 0.72 compared to 0.60 with rule-based control, while demand response, though critical peak pricing, optimally shifts up to 100% of the thermal load during peak price hours.