Optimization of Home Energy Management Systems in Smart Cities Using Bacterial Foraging Algorithm and Deep Reinforcement Learning for Enhanced Renewable Energy Integration

Abstract

This paper presents a pioneering exploration into the optimization of Home Energy Management Systems (HEMS) through the novel application of the Bacterial Foraging Metaheuristic Optimization (BFMO) algorithm and Deep Reinforcement Learning (DRL). The study systematically addresses the pressing challenge of enhancing residential energy efficiency, focusing on dynamic appliance scheduling within HEMS. A robust methodology is established, encompassing data collection from smart homes, implementation details of the BFMO algorithm, DRL techniques, and a comprehensive evaluation framework. The unique contribution of this research lies in the effective integration of the BFMO algorithm and DRL to orchestrate energy‐conscious scheduling of home appliances within HEMS. The BFMO algorithm demonstrates its adaptability to fluctuating energy costs and consumption patterns by simulating the foraging behaviour of bacteria. At the same time, DRL enhances the system’s ability to learn and optimize scheduling decisions over time, showcasing their combined efficacy in real‐world scenarios. The algorithms’ iterative application of chemotaxis, reproduction, elimination‐dispersal, swarming, and learning consistently yields optimized appliance schedules. The main focus of this study resides in the evaluation metrics illustrating the tangible benefits of BFMO and DRL compared to traditional HEMS. Significant reductions in total energy consumption and cost, accompanied by improved peak demand management, exemplify the algorithms’ impact. Furthermore, the study delves into enhancing user comfort, integrating renewable energy sources, and the overall robustness of HEMS, all demonstrating the multifaceted advantages of the BFMO and DRL approaches. This research contributes methodologically by introducing and detailing these algorithms and provides a valuable dataset and evaluation metrics for future research in the domain. The findings underscore the immediate and long‐term relevance of optimizing HEMS with BFMO and DRL, catering to researchers, practitioners, and policymakers involved in advancing smart grid technologies and sustainable residential energy management. In summary, this work establishes the BFMO algorithm and DRL as pioneering and versatile tools for energy‐conscious appliance scheduling in HEMS, offering a substantial leap forward in the quest for efficient and sustainable residential energy management.