Optimizing cost and battery health in home energy management systems using actor-critic fuzzy-rule networks under renewable energy uncertainty

Abstract This paper investigates household energy management systems that integrate renewable energy sources and battery storage, modeled as discrete-time optimization problems. Motivated by global trends toward decarbonization and recent policy initiatives promoting distributed energy resources, a data-driven method is proposed that combines fuzzy-rule networks with reinforcement learning in an actor-critic architecture. The controller adaptively regulates power demand while treating renewable energy as an uncertain disturbance. Relying only on real-time demand and battery status data, it aims to minimize electricity costs and preserve battery health. Validation addresses uncertainties in energy prices, user behavior, and environmental conditions. A virtual desired state of charge enhances operational stability, and comparative results confirm the controller’s effectiveness in reducing costs and optimizing battery performance.