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The industrial electricity management system (EMS) plays an essential role in organizing energy savings by enabling instructed managerial construction. EMS gathers and examines real-time data from measuring devices and meters to significantly optimize energy usage by merging machine learning procedures and data analytics. To monitor machine performance and energy supply and moderate the early identification of unevenness, the system matches this data with ideal statistics. Furthermore, EMS can reckon use trends, detect prospective for energy reserves, and offer sensible advice that increases productivity by using analytical assessment. Decline of losses, improvement of procedures, and sustainability of the circumstances are the focal objectives of EMS. To contribute to administrations in managing substantial energy administration effects and to encourage sustainability and cost-efficiency going forward, this study recommends a progressive method for an effective EMS.

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Optimizing Industrial Energy Management: Employing Machine Learning and Data Analytics for Sustainable Practices

  • 会议论文

  • 发表日期：

    2025年

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  • H. N. Lakshmi,
  • B. Rupa Devi,
  • P. Anjaiah,
  • C. Nithisha,
  • Konatham Sumalatha,
  • R. Kavitha

摘要

The industrial electricity management system (EMS) plays an essential role in organizing energy savings by enabling instructed managerial construction. EMS gathers and examines real-time data from measuring devices and meters to significantly optimize energy usage by merging machine learning procedures and data analytics. To monitor machine performance and energy supply and moderate the early identification of unevenness, the system matches this data with ideal statistics. Furthermore, EMS can reckon use trends, detect prospective for energy reserves, and offer sensible advice that increases productivity by using analytical assessment. Decline of losses, improvement of procedures, and sustainability of the circumstances are the focal objectives of EMS. To contribute to administrations in managing substantial energy administration effects and to encourage sustainability and cost-efficiency going forward, this study recommends a progressive method for an effective EMS.