AI-Based Scheduling Models, Optimization, and Prediction for Hydropower Generation: Opportunities, Issues, and Future Directions

Yoan Villeneuve (), Sara Séguin and Abdellah Chehri
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Yoan Villeneuve: Group for Research in Decision Analysis (GERAD), Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
Sara Séguin: Group for Research in Decision Analysis (GERAD), Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
Abdellah Chehri: Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada

Energies, 2023, vol. 16, issue 8, 1-27

Abstract: Hydropower is the most prevalent source of renewable energy production worldwide. As the global demand for robust and ecologically sustainable energy production increases, developing and enhancing the current energy production processes is essential. In the past decade, machine learning has contributed significantly to various fields, and hydropower is no exception. All three horizons of hydropower models could benefit from machine learning: short-term, medium-term, and long-term. Currently, dynamic programming is used in the majority of hydropower scheduling models. In this paper, we review the present state of the hydropower scheduling problem as well as the development of machine learning as a type of optimization problem and prediction tool. To the best of our knowledge, this is the first survey article that provides a comprehensive overview of machine learning and artificial intelligence applications in the hydroelectric power industry for scheduling, optimization, and prediction.

Keywords: hydropower; hydropower scheduling; machine learning; optimization; stochastic programming; linear regression; random forest; reinforcement learning; deep neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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