AI-driven smart home optimization for sustainable energy and water management: a systematic review
Résumé
The increasing intricacy of smart home systems, propelled by the incorporation of renewable energy sources, IoT technologies, and water-dependent subsystems, demands sophisticated frameworks for effective and sustainable resource management. This paper offers a thorough evaluation of AI-based strategies for optimizing energy and water usage in smart homes, with the objective of assessing current methodologies and pinpointing research deficiencies. The review adheres to PRISMA rules and examines papers published from 2021 to 2025, obtained from Google Scholar and IEEE Xplore, with supplementary inclusion of works indexed in Scopus and Web of Science according to specified eligibility criteria. A theme synthesis methodology is utilized to analyze five principal domains: smart home energy management, non-intrusive load monitoring, reinforcement learning-based control, smart water management, and IoT security. The results demonstrate that although AI methodologies like machine learning, deep learning, and reinforcement learning markedly enhance energy efficiency, cost optimization, and real-time decision-making, the majority of research concentrates on single-resource systems and lacks comprehensive energy-water optimization. Further deficiencies encompass restricted practical implementation, inadequate economic assessment, and ongoing security issues. This review provides a thorough synthesis of recent achievements and emphasizes the necessity for integrated, scalable, and context-aware frameworks to facilitate sustainable smart home growth.
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