HYBRID ADAPIVE DEEP REINFORCEMENT LEARNING AND METAHEURISTICS OPTIMIZATION FRAMEWORK FOR SMART HOME ENERGY MANAGEMENT

Abstract

Traditional, static optimization techniques often fail to handle the high uncertainty, stochastic nature of renewable generation, and dynamic user preferences. This study proposes a novel, comprehensive hybrid adaptive framework for smart home energy management that integrates Deep Reinforcement Learning (DRL) with metaheuristic optimization. The proposed framework aims to minimize electricity costs, reduce peak-to-average ratios (PAR), and maximize user comfort. Within this framework, Deep Reinforcement Learning (especially algorithms like DQN or Multi-Objective DRL.) is utilize to learn optimal control policies in real time, adapting to unpredictable fluctuations in energy demand and price signals. Simultaneously, Metaheuristic Optimization algorithms (e.g Genetic Algorithm, Particle Swarm Optimization, or Bacterial Foraging) are employed to handle complex, constrains-driven scheduling task that require global optimization capabilities, enabling the effective management of household appliances, energy storage system (ESS), and electric vehicles (EVs). The synergy between DRL and metaheuristic techniques bridges the gap between fast, adaptive, real-time control (DRL) and precise, long-term, optimal planning (Metaheuristics). Performance evaluation, conducted through simulations, indicates that the hybrid approach significantly outperforms traditional methods by reducing energy bills (often by 20-50%) and lowering peak demand, while successfully ensuring that user comfort preferences are maintained. The study highlights the effectiveness of this adaptive framework in promoting energy sustainability, reducing grid dependence, and facilitating intelligent energy management in future residential, and smart city scenarios.