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Ongoing global conflicts highlight energy security risks, also in the industrial sector, which is responsible for a significant energy usage. In response, applying advanced technologies like Big Data Analysis (BDA), smart sensors, cloud computing, and artificial intelligence (AI) it is possible to develop solutions for improving energy efficiency in an industry. This paper uses Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for analysis to systematically identify, evaluate, and synthesize recent research on the application of advanced technologies in predicting industrial energy consumption and enhancing energy efficiency. Special attention is given to the role of AI and real-time data processing in improving energy management strategies in industry. The results of the analysis indicate the potential implications of AI towards the prediction of energy consumption in industry, gaps in current research, and the direction of future research towards sustainable machining processes.

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Analysis of the AI-Based Solution for Monitoring Energy Usage Applied into Machining Processes - A Systematic Literature Review

  • 会议论文

  • 发表日期：

    2026年

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  • Małgorzata Szmołda,
  • Hanna Łosyk,
  • Snehal Vasant Kadbhane,
  • Justyna Patalas-Maliszewska

摘要

Ongoing global conflicts highlight energy security risks, also in the industrial sector, which is responsible for a significant energy usage. In response, applying advanced technologies like Big Data Analysis (BDA), smart sensors, cloud computing, and artificial intelligence (AI) it is possible to develop solutions for improving energy efficiency in an industry. This paper uses Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for analysis to systematically identify, evaluate, and synthesize recent research on the application of advanced technologies in predicting industrial energy consumption and enhancing energy efficiency. Special attention is given to the role of AI and real-time data processing in improving energy management strategies in industry. The results of the analysis indicate the potential implications of AI towards the prediction of energy consumption in industry, gaps in current research, and the direction of future research towards sustainable machining processes.