IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i9p1292-d1382136.html
   My bibliography  Save this article

Intelligent Low-Consumption Optimization Strategies: Economic Operation of Hydropower Stations Based on Improved LSTM and Random Forest Machine Learning Algorithm

Author

Listed:
  • Hong Pan

    (School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China)

  • Jie Yang

    (School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China)

  • Yang Yu

    (China Water Northeast Survey Design and Research Co., Ltd., Changchun 130021, China)

  • Yuan Zheng

    (School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China)

  • Xiaonan Zheng

    (School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China)

  • Chenyang Hang

    (School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China)

Abstract

The economic operation of hydropower stations has the potential to increase water use efficiency. However, there are some challenges, such as the fixed and unchangeable flow characteristic curve of the hydraulic turbines, and the large number of variables in optimal load distribution, which limit the progress of research. In this paper, we propose a new optimal method of the economic operation of hydropower stations based on improved Long Short-Term Memory neural network (I-LSTM) and Random Forest (RF) algorithm. Firstly, in order to accurately estimate the water consumption, the LSTM model’s hyperparameters are optimized using improved particle swarm optimization, and the I-LSTM method is proposed to fit the flow characteristic curve of the hydraulic turbines. Secondly, the Random Forest machine learning algorithm is introduced to establish a load-distribution model with its powerful feature extraction and learning ability. To improve the accuracy of the load-distribution model, we use the K-means algorithm to cluster the historical data and optimize the parameters of the Random Forest model. A Hydropower Station in China is selected for a case study. It is shown that (1) the I-LSTM method fits the operating characteristics under various working conditions and actual operating characteristics of hydraulic turbines, ensuring that they are closest to the actual operating state; (2) the I-LSTM method is compared with Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Long Short-Term Memory neural network (LSTM). The prediction results of SVM have a large error, but compared with ELM and LSTM, MSE is reduced by about 46% and 38% respectively. MAE is reduced by about 25% and 21%, respectively. RMSE is reduced by about 27% and 24%, respectively; (3) the RF algorithm performs better than the traditional dynamic programming algorithm in load distribution. With the passage of time and the increase in training samples, the prediction accuracy of the Random Forest model has steadily improved, which helps to achieve optimal operation of the units, reducing their average total water consumption by 1.24%. This study provides strong support for the application of intelligent low-consumption optimization strategies in hydropower fields, which can bring higher economic benefits and resource savings to renewable energy production.

Suggested Citation

  • Hong Pan & Jie Yang & Yang Yu & Yuan Zheng & Xiaonan Zheng & Chenyang Hang, 2024. "Intelligent Low-Consumption Optimization Strategies: Economic Operation of Hydropower Stations Based on Improved LSTM and Random Forest Machine Learning Algorithm," Mathematics, MDPI, vol. 12(9), pages 1-20, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1292-:d:1382136
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/9/1292/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/9/1292/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shrestha, Anil & Mustafa, Andy Ali & Htike, Myo Myo & You, Vithyea & Kakinaka, Makoto, 2022. "Evolution of energy mix in emerging countries: Modern renewable energy, traditional renewable energy, and non-renewable energy," Renewable Energy, Elsevier, vol. 199(C), pages 419-432.
    2. Stefanizzi, Michele & Capurso, Tommaso & Balacco, Gabriella & Binetti, Mario & Camporeale, Sergio Mario & Torresi, Marco, 2020. "Selection, control and techno-economic feasibility of Pumps as Turbines in Water Distribution Networks," Renewable Energy, Elsevier, vol. 162(C), pages 1292-1306.
    3. Alireza Amani & Hosein Alizadeh, 2021. "Solving Hydropower Unit Commitment Problem Using a Novel Sequential Mixed Integer Linear Programming Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1711-1729, April.
    4. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
    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. Sascha O. Becker, Sascha O & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," The Warwick Economics Research Paper Series (TWERPS) 1478, University of Warwick, Department of Economics.
    2. Kandi, Ali & Meirelles, Gustavo & Brentan, Bruno, 2022. "Employing demand prediction in pump as turbine plant design regarding energy recovery enhancement," Renewable Energy, Elsevier, vol. 187(C), pages 223-236.
    3. Song, Yuguang & Xia, Mingchao & Yang, Liu & Chen, Qifang & Su, Su, 2023. "Multi-granularity source-load-storage cooperative dispatch based on combined robust optimization and stochastic optimization for a highway service area micro-energy grid," Renewable Energy, Elsevier, vol. 205(C), pages 747-762.
    4. Assareh, Ehsanolah & Mousavi Asl, Seyed Sajad & Agarwal, Neha & Ahmadinejad, Mehrdad & Ghodrat, Maryam & Lee, Moonyong, 2023. "New optimized configuration for a hybrid PVT solar/electrolyzer/absorption chiller system utilizing the response surface method as a machine learning technique and multi-objective optimization," Energy, Elsevier, vol. 281(C).
    5. Sascha O. Becker & Hans-Joachim Voth, 2023. "From the Death of God to the Rise of Hitler," CESifo Working Paper Series 10730, CESifo.
    6. Tomasz Rymarczyk & Konrad Niderla & Edward Kozłowski & Krzysztof Król & Joanna Maria Wyrwisz & Sylwia Skrzypek-Ahmed & Piotr Gołąbek, 2021. "Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control," Energies, MDPI, vol. 14(23), pages 1-21, December.
    7. Moazeni, Faegheh & Khazaei, Javad, 2021. "Optimal energy management of water-energy networks via optimal placement of pumps-as-turbines and demand response through water storage tanks," Applied Energy, Elsevier, vol. 283(C).
    8. Forbes, Kevin F., 2023. "Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City," Utilities Policy, Elsevier, vol. 80(C).
    9. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
    10. Kun Yang & Kan Yang, 2022. "Improved Whale Algorithm for Economic Load Dispatch Problem in Hydropower Plants and Comprehensive Performance Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5823-5838, December.
    11. Hillebrecht, Michael & Klonner, Stefan & Pacere, Noraogo A., 2020. "Dynamic Properties of Poverty Targeting," Working Papers 0696, University of Heidelberg, Department of Economics.
    12. Ivan Brandić & Alan Antonović & Lato Pezo & Božidar Matin & Tajana Krička & Vanja Jurišić & Karlo Špelić & Mislav Kontek & Juraj Kukuruzović & Mateja Grubor & Ana Matin, 2023. "Energy Potentials of Agricultural Biomass and the Possibility of Modelling Using RFR and SVM Models," Energies, MDPI, vol. 16(2), pages 1-10, January.
    13. Davi Edson Sales Souza & André Luiz Amarante Mesquita & Claudio José Cavalcante Blanco, 2023. "Pressure Regulation in a Water Distribution Network Using Pumps as Turbines at Variable Speed for Energy Recovery," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1183-1206, February.
    14. Kang, Lili & Zhao, Guangchuan, 2022. "Financial support for unmet need for personal assistance with daily activities: Implications from China's long-term care insurance pilots," Finance Research Letters, Elsevier, vol. 45(C).
    15. Merike Kukk & Jaanika Meriküll & Tairi Rõõm, 2023. "The Gender Wealth Gap in Europe: Application of Machine Learning to Predict Individual‐level Wealth," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 289-317, June.
    16. Kun Yang & Kan Yang, 2021. "Short-Term Hydro Generation Scheduling of the Three Gorges Hydropower Station Using Improver Binary-coded Whale Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3771-3790, September.
    17. Wang, Sicheng & Noland, Robert B., 2021. "What is the elasticity of sharing a ridesourcing trip?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 284-305.
    18. Uttam Khatri & Ji-In Kim & Goo-Rak Kwon, 2023. "Genetics Information with Functional Brain Networks for Dementia Classification," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    19. Bu, Fan & wu, Hong & Mahmoud, Haitham A. & Alzoubi, Haitham M. & Ramazanovna, Nargiza Kuzieva & Gao, Yirui, 2023. "Do financial inclusion, natural resources and urbanization affect the sustainable environment in emerging economies," Resources Policy, Elsevier, vol. 87(PA).
    20. Telikani, Akbar & Rossi, Mosé & Khajehali, Naghmeh & Renzi, Massimiliano, 2023. "Pumps-as-Turbines’ (PaTs) performance prediction improvement using evolutionary artificial neural networks," Applied Energy, Elsevier, vol. 330(PA).

    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:jmathe:v:12:y:2024:i:9:p:1292-:d:1382136. 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.