Theo Zangato, Aomar Osmani and Pegah Alizadeh, Universite Paris Sorbonne Nord, France

Amidst increasing energy demands and growing environmental concerns, the promotion of sustainable and energy-efficient practices has become imperative. This paper introduces a reinforcement learning-based technique for optimizing energy consumption and its associated costs, with a focus on energy management systems. A three-step approach for the efficient management of charging cycles in energy storage units within buildings is presented combining RL with prior knowledge. A unique strategy is adopted: clustering building load curves to discern typical energy consumption patterns, embedding domain knowledge into the learning algorithm to refine the agent's action space and predicting of future observations to make real-time decisions. We showcase the effectiveness of our method using real-world data. It enables controlled exploration and efficient training of Energy Management System (EMS) agents. When compared to the benchmark, our model reduces energy costs by up to 15%, cutting down consumption during peak periods, and demonstrating adaptability across various building consumption profiles.

Reinforcement Learning, Energy Management Systems, Time-Series, Clustering