A Comparison of Model Predictive Control and Heuristics in Building Energy Management

The increasing penetration of renewable energy sources in the electricity grid underscores the critical need for advanced energy management systems in residential settings. Traditional energy management systems are limited in handling the complexity and dynamism of energy systems, thus hindering efficient energy utilization in buildings. This study conducts a novel evaluation of Model Predictive Control versus traditional rule-based systems within a lab-based energy management system prototype for households. The experimental setup consisted of a bidirectional power supply, lithium-ion batteries, and a programmable logic controller. All systems were rigorously tested over consecutive seven-day periods, taking into account photovoltaic supply, electricity demand, and pricing to optimize energy management and reduce overall electricity costs. The results demonstrate the superior efficiency of the advanced control strategy in minimizing electricity costs compared to traditional controllers, which struggle with the complexity of sector coupling and dynamic energy environments. This research offers new insights into the practical viability and performance of Model Predictive Control in real-world settings, showcasing its potential for advanced energy management and emphasizing the importance of open standards in interface development for future advancements. Recognizing the necessity for comprehensive solutions, model- and data-driven approaches can integrate households as active participants in the dynamic energy system, enabling real-time adaptation to fluctuating grid conditions. By effectively communicating the advantages of advanced control technologies to policymakers, energy providers, and homeowners, we can encourage broader adoption and unlock their potential for creating a more sustainable, efficient, and cost-effective energy future.