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<p>The research article presents an Artificial Intelligence (AI) and Internet of Things (IoT)-based framework designed to evaluate contemporary research prototypes in energy preservation. Researchers used a systematic search technique to evaluate 20 IoT and AI-based energy preservation prototypes published from 2018 through 2024. The selection process is based on the predefined criteria focused on the relevance of AI-IoT integration and energy preservation, and the availability of detailed information about the implementation and evaluation of each prototype. The selected prototypes (i.e., the energy systems in which these were implemented, including smart grids, building energy management systems, and renewable energy systems) were analyzed based on the three-layer architectural framework of Data Collection, Energy Preservation, and Action and Control layers that we propose. A frequency-based analysis of all evaluation dimensions revealed that optimization, automation, and data interoperability are the predominant challenges in the field. The research findings deliver scientifically supported knowledge about common system limitations while pointing out specific research paths that should be followed next. This study provides actionable advice for researchers and engineers as well as policymakers who want to create scalable and adaptable AI-IoT systems that run efficiently.</p>

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Artificial intelligence and the Internet of Things in energy preservation: research prototypes, trends, and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月15日

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  • Atheer Aleran,
  • Hanan Almukhalfi,
  • Nahla J. Abid,
  • Ayman Noor,
  • Abdulqader M. Almars,
  • Talal H. Noor

摘要

The research article presents an Artificial Intelligence (AI) and Internet of Things (IoT)-based framework designed to evaluate contemporary research prototypes in energy preservation. Researchers used a systematic search technique to evaluate 20 IoT and AI-based energy preservation prototypes published from 2018 through 2024. The selection process is based on the predefined criteria focused on the relevance of AI-IoT integration and energy preservation, and the availability of detailed information about the implementation and evaluation of each prototype. The selected prototypes (i.e., the energy systems in which these were implemented, including smart grids, building energy management systems, and renewable energy systems) were analyzed based on the three-layer architectural framework of Data Collection, Energy Preservation, and Action and Control layers that we propose. A frequency-based analysis of all evaluation dimensions revealed that optimization, automation, and data interoperability are the predominant challenges in the field. The research findings deliver scientifically supported knowledge about common system limitations while pointing out specific research paths that should be followed next. This study provides actionable advice for researchers and engineers as well as policymakers who want to create scalable and adaptable AI-IoT systems that run efficiently.