Energy Optimization in Smart Building Controls: A Predictive Study of Reinforcement Learning Policies

Abstract

The rapid urbanization and modernization of infrastructure have escalated the energy consumption of commercial and residential buildings globally, necessitating advanced control methodologies to balance occupant comfort with energy efficiency. Traditional control mechanisms often fall short in dynamically adapting to complex thermodynamic interactions within multi-zone structures. Reinforcement learning has emerged as a promising alternative, capable of discovering optimal control strategies through continuous interaction with the building environment. However, evaluating and predicting the efficacy of these policies prior to exhaustive simulation or physical deployment remains a significant computational challenge. This paper introduces a novel framework that leverages graph analysis to predict the energy optimization potential of reinforcement learning policies in smart building controls. By representing the physical architecture and thermal dynamics of a building as a complex graph, where nodes denote distinct spatial zones and edges represent thermodynamic relationships, we extract sophisticated structural features from the control policies. These graph-based representations are subsequently utilized to predict energy savings and operational efficiency without the need for prolonged environmental interaction. The analysis demonstrates that incorporating spatial and thermodynamic dependencies significantly enhances the predictive accuracy of policy evaluation models. The findings provide a critical stepping stone for the rapid prototyping and safe deployment of advanced machine learning algorithms in critical building infrastructure, ultimately contributing to broader sustainability goals.