EdgeHEM: Sparse Federated Reinforcement Learning for Home Energy Management at the Edge

Abstract

With the growth of energy demand and the popularization of distributed energy resources, home energy management (HEM) has emerged as a crucial technology to improve energy efficiency and reduce electricity costs. HEM systems at the edge can provide more efficient and personalized energy services for households through real-time intelligent analysis. However, distributed edge devices may not have adequate storage space to support computation-intensive decision-making algorithms. To address this problem, this paper proposes EdgeHEM, an edge reinforcement learning framework for HEM that considers the memory constraints of edge devices. Specifically, a dynamic sparse learning strategy with topology evolution is explored to overcome memory limitations on the network scale. Furthermore, a compressed federated learning approach with gradient approximation is developed to leverage the transitions cached in the memory of multiple edge devices. Extensive experiments are conducted on the established hardware testbed to demonstrate the efficiency and practicality of EdgeHEM using real-world datasets.