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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月23日

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  • Chidentree Treesatayapun

摘要

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.