Adaptive Weight Multi-Objective Building Energy Optimization: The Impacts of Thermal Comfort, Costs, and Emissions

This study analyzes the effect of utilizing thermal comfort as a direct objective in the optimized control of a building energy system integrated with energy flexible devices, including a solar-PV system and a battery storage for electricity supply, a heat pump and a natural gas furnace for heat supply. The proposed controller is a model-predictive controller with adaptive weights. The optimization problem includes several sub-objectives, including economic cost, emissions, and thermal discomfort. A Pareto-Front approach was used to find the tradeoff between the sub-objectives. In contrast to similar studies, the proposed controller can sacrifice thermal comfort for lower economic costs and emissions and utilizes the building mass thermal capacity as a thermal storage, optimizing heating signals. Simulation results show the controller’s effectiveness in exploiting the energy flexibility of thermal comfort, where seasonal changes are utilized to alter the performance of the building energy systems to manage all objectives at the same time.