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This study examines and evaluates the ways ML might be used in the hydropower industry. This paper explains how machine learning (ML) is used in the hydropower sector. It attempts to highlight the subjects that are discussed the most in the most recent academic publications. The aim is to provide suggestions for other lines of inquiry that may be swiftly pursued in order to address the topics that have not yet been thoroughly investigated. The objective of this paper is to give a detailed examination of how machine learning is used for hydropower scheduling. With the help of latest research, the authors investigate what additional functions machine learning (ML) and cyber-physical systems (CPSs) might possess. How to address short-term hydropower scheduling (STHS) is rightly the key concern.

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A Critical Review of Machine Learning Paradigms and Future Directions in Scheduling of Hydropower Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Khushboo Sharma,
  • Sunil Kumar,
  • Deepak Mehta,
  • Savita,
  • Sandeep Madishetti

摘要

This study examines and evaluates the ways ML might be used in the hydropower industry. This paper explains how machine learning (ML) is used in the hydropower sector. It attempts to highlight the subjects that are discussed the most in the most recent academic publications. The aim is to provide suggestions for other lines of inquiry that may be swiftly pursued in order to address the topics that have not yet been thoroughly investigated. The objective of this paper is to give a detailed examination of how machine learning is used for hydropower scheduling. With the help of latest research, the authors investigate what additional functions machine learning (ML) and cyber-physical systems (CPSs) might possess. How to address short-term hydropower scheduling (STHS) is rightly the key concern.