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<p>This paper presents a systematic literature review of energy management models for smart homes, conducted between 2018 and 2024, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Smart homes leverage advanced technologies to optimize energy consumption and enhance sustainability through interconnected devices and sophisticated algorithms. The review covers energy optimization techniques, predictive management, renewable energy integration, demand-side management, user behavior, and data protection. It examines the effectiveness of various models, identifies key tends, and addresses challenges such as integrating diverse energy sources, managing consumption variability, and ensuring data privacy. The findings reveal significant advancements in energy optimization, home automation, and grid stability. However, areas like demand-side management and artificial intelligence (AI) and machine learning (ML) driven algorithms for energy management remain underexplored and require further research. Recommendations are provided to improve energy management systems and guide future research for increased efficiency and sustainability in smart homes. This review offers valuable insights into the current state of energy management models and lays the groundwork for future developments in smart home energy systems.</p>

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Future of Energy Management Models in Smart Homes: A Systematic Literature Review of Research Trends, Gaps, and Future Directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月1日

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  • Ubaid ur Rehman,
  • Pedro Faria,
  • Luis Gomes,
  • Zita Vale

摘要

This paper presents a systematic literature review of energy management models for smart homes, conducted between 2018 and 2024, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Smart homes leverage advanced technologies to optimize energy consumption and enhance sustainability through interconnected devices and sophisticated algorithms. The review covers energy optimization techniques, predictive management, renewable energy integration, demand-side management, user behavior, and data protection. It examines the effectiveness of various models, identifies key tends, and addresses challenges such as integrating diverse energy sources, managing consumption variability, and ensuring data privacy. The findings reveal significant advancements in energy optimization, home automation, and grid stability. However, areas like demand-side management and artificial intelligence (AI) and machine learning (ML) driven algorithms for energy management remain underexplored and require further research. Recommendations are provided to improve energy management systems and guide future research for increased efficiency and sustainability in smart homes. This review offers valuable insights into the current state of energy management models and lays the groundwork for future developments in smart home energy systems.