IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i8p3335-d1119140.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yoan Villeneuve & Sara Séguin & Abdellah Chehri, 2023. "AI-Based Scheduling Models, Optimization, and Prediction for Hydropower Generation: Opportunities, Issues, and Future Directions," Energies, MDPI, vol. 16(8), pages 1-27, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3335-:d:1119140
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/8/3335/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/8/3335/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Séguin, Sara & Fleten, Stein-Erik & Côté, Pascal & Pichler, Alois & Audet, Charles, 2017. "Stochastic short-term hydropower planning with inflow scenario trees," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1156-1168.
    2. Valentina Sessa & Edi Assoumou & Mireille Bossy & Sofia G. Simões, 2021. "Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation," Clean Technol., MDPI, vol. 3(4), pages 1-23, December.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    4. Ali Thaeer Hammid & Omar I. Awad & Mohd Herwan Sulaiman & Saraswathy Shamini Gunasekaran & Salama A. Mostafa & Nallapaneni Manoj Kumar & Bashar Ahmad Khalaf & Yasir Amer Al-Jawhar & Raed Abdulkareem A, 2020. "A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems," Energies, MDPI, vol. 13(11), pages 1-21, June.
    5. Wei Xu & Xiaoli Zhang & Anbang Peng & Yue Liang, 2020. "Deep Reinforcement Learning for Cascaded Hydropower Reservoirs Considering Inflow Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 3003-3018, July.
    6. Iram Parvez & Jianjian Shen & Ishitaq Hassan & Nannan Zhang, 2021. "Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant," Energies, MDPI, vol. 14(2), pages 1-28, January.
    7. Kao, Shih-Chieh & Sale, Michael J. & Ashfaq, Moetasim & Uria Martinez, Rocio & Kaiser, Dale P. & Wei, Yaxing & Diffenbaugh, Noah S., 2015. "Projecting changes in annual hydropower generation using regional runoff data: An assessment of the United States federal hydropower plants," Energy, Elsevier, vol. 80(C), pages 239-250.
    8. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Danyang Gao & Albert S. Chen & Fayyaz Ali Memon, 2024. "A Systematic Review of Methods for Investigating Climate Change Impacts on Water-Energy-Food Nexus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 1-43, January.
    2. Tulika Saha & Sriparna Saha & Pushpak Bhattacharyya, 2020. "Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-28, July.
    3. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    4. Lixiang Zhang & Yan Yan & Yaoguang Hu, 2024. "Deep reinforcement learning for dynamic scheduling of energy-efficient automated guided vehicles," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3875-3888, December.
    5. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    6. Woo Jae Byun & Bumkyu Choi & Seongmin Kim & Joohyun Jo, 2023. "Practical Application of Deep Reinforcement Learning to Optimal Trade Execution," FinTech, MDPI, vol. 2(3), pages 1-16, June.
    7. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    8. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    9. Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
    10. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    11. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    12. Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
    13. Ande Chang & Yuting Ji & Chunguang Wang & Yiming Bie, 2024. "CVDMARL: A Communication-Enhanced Value Decomposition Multi-Agent Reinforcement Learning Traffic Signal Control Method," Sustainability, MDPI, vol. 16(5), pages 1-17, March.
    14. Hegazy Rezk & A. G. Olabi & Mohammad Ali Abdelkareem & Abdul Hai Alami & Enas Taha Sayed, 2023. "Optimal Parameter Determination of Membrane Bioreactor to Boost Biohydrogen Production-Based Integration of ANFIS Modeling and Honey Badger Algorithm," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    15. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    16. Zhang, Yang & Yang, Qingyu & Li, Donghe & An, Dou, 2022. "A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility," Applied Energy, Elsevier, vol. 323(C).
    17. Schaefli, Bettina & Manso, Pedro & Fischer, Mauro & Huss, Matthias & Farinotti, Daniel, 2017. "The role of glacier retreat for Swiss hydropower production," Earth Arxiv 7z96d, Center for Open Science.
    18. He, Jing & Liu, Xinglu & Duan, Qiyao & Chan, Wai Kin (Victor) & Qi, Mingyao, 2023. "Reinforcement learning for multi-item retrieval in the puzzle-based storage system," European Journal of Operational Research, Elsevier, vol. 305(2), pages 820-837.
    19. Holger Mohr & Katharina Zwosta & Dimitrije Markovic & Sebastian Bitzer & Uta Wolfensteller & Hannes Ruge, 2018. "Deterministic response strategies in a trial-and-error learning task," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-19, November.
    20. Zhang, Tianhao & Dong, Zhe & Huang, Xiaojin, 2024. "Multi-objective optimization of thermal power and outlet steam temperature for a nuclear steam supply system with deep reinforcement learning," Energy, Elsevier, vol. 286(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3335-:d:1119140. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.