Evaluate the Effectiveness of Intelligent Control Approaches in Building Energy Performance - A Hybrid Energy Resource Case Study

This paper aims to develop multi-objective controllers to improve the environmental footprints of hybrid energy resources buildings. Accordingly, a multi-source heating system is implemented to evaluate the potential of three kinds of controllers. We use a case study analysis of an apartment to study building system components: boiler, solar thermal panel, storage tank, and electrical heater. Three kinds of controllers are analyzed: a) a combination of Model Predictive Controller (MPC) and Genetic Algorithm (GA) (MP-GA), b) an optimized fuzzy sliding mode controller, and c) a traditional on/off controller. The controllers optimize the heating system by regulating the supplied energy from each energy resource. Our findings indicate that the fuzzy sliding mode controller and MPC-GA provided comfortable temperature levels 99% and 97% of the time, respectively. Furthermore, they improved heating energy savings by up to 9% compared to the on/off baseline, primarily due to their ability to estimate the supplied energy from the solar thermal panel and storage system.