Development and Evaluation of New Methods of Electrical Energy Management in Smart Residential Environments Using Artificial Intelligence Algorithms

The ongoing evolution of smart residential environments is redefining paradigms in electrical energy management, driven by an urgent need for efficiency, cost reduction, grid stability, and enhanced user comfort. This paper presents the development, implementation, and evaluation of a novel artificial intelligence (AI)-powered framework for residential energy management systems, integrating advanced forecasting, real-time optimization, multi-agent coordination, and explainable AI techniques. The proposed approach leverages a hybrid deep learning (DL) and reinforcement learning (RL) architecture, equipped with dynamic prediction modules and adaptive, preference-aware control logic designed for diverse home environments. The methodology combines comprehensive high-fidelity simulations of ۱۵۰ virtual households—spanning various climate, behavioral, economic, and technological scenarios—with real-world validation in a fully-instrumented smart apartment test bed. Key components include long short-term memory (LSTM) neural networks for granular load and generation forecasting, a deep RL engine for optimal appliance operation and storage management, and a multi-agent system enabling peer-to-peer (P۲P) coordination for energy trading and collective demand response. The explainability module ensures transparent decision-making, fostering user trust and acceptance. Experimental results demonstrate that the proposed hybrid DL+RL solution achieves an average ۱۸% reduction in annual household energy use and a ۲۹% decrease in peak demand, outperforming rule-based and conventional machine learning baselines. The full innovation stack—including multi-agent and XAI capabilities—further lowers grid reliance, increases PV self-consumption by up to ۲۶%, and raises user satisfaction scores. Ablation and sensitivity analyses confirm that explainable and user-adaptive control logics are crucial for both sustained comfort and automation adoption, with peer-to-peer schemes delivering up to ۱۰% additional peak shaving at the community level. Case studies during extreme weather events and grid contingencies reveal the robustness and scalability of the framework, highlighting significant reductions in grid import and system resilience enabled by collaborative agent coordination and battery sharing. Ultimately, this research underscores the necessity of marrying advanced AI technologies with human-centric and community-aware approaches to achieve optimal, scalable, and trusted residential energy management in the context of future smart grids.

کلیدواژه ها:

Smart Home Energy Management ، Artificial Intelligence ، Reinforcement Learning ، Explainable AI ، Multi-Agent Systems ، Demand Response