Deep Reinforcement Learning for Real-Time Energy Management in Smart Home

Abstract

Energy management in the smart home can help reduce residential energy costs by scheduling various energy consumption activities. However, accurately modeling factors, such as user behavior, renewable power generation, weather conditions, and real-time electricity prices can be challenging, making the design of an efficient energy management strategy difficult. This article proposes a real-time energy management algorithm based on deep reinforcement learning (DRL) for smart homes equipped with rooftop photovoltaics, energy storage systems, and smart appliances. The algorithm aims to minimize the energy cost while ensuring user comfort. A policy network that can output both discrete and continuous actions is designed to generate actions for different types of devices in a smart home. The proposed DRL-agent is trained using a proximal policy optimization approach with historical data and is used for real-time scheduling. Finally, simulations based on real-world data demonstrate the effectiveness and robustness of the proposed algorithm.