IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i3p697-d1331018.html

Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

Author

Listed:
  • Boudy Bilal

    (Electrical Engineering Department, UR-EEDD, Ecole Supérieure Polytechnique, Nouakchott BP 4303, Mauritania
    URAER/FST, Université de Nouakchott, Nouakchott BP 5026, Mauritania)

  • Kaan Yetilmezsoy

    (Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa, Esenler, 34220 Istanbul, Turkey)

  • Mohammed Ouassaid

    (Electrical Engineering Department, Engineering for Smart and Sustainable Systems Research Centre, Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat 10090, Morocco)

Abstract

This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models’ predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator’s rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting.

Suggested Citation

  • Boudy Bilal & Kaan Yetilmezsoy & Mohammed Ouassaid, 2024. "Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power," Energies, MDPI, vol. 17(3), pages 1-36, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:697-:d:1331018
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/3/697/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/3/697/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Wanying & He, Yaoyao & Yang, Shanlin, 2023. "A multi-step probability density prediction model based on gaussian approximation of quantiles for offshore wind power," Renewable Energy, Elsevier, vol. 202(C), pages 992-1011.
    2. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    3. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    4. Liang, Tao & Chai, Chunjie & Sun, Hexu & Tan, Jianxin, 2022. "Wind speed prediction based on multi-variable Capsnet-BILSTM-MOHHO for WPCCC," Energy, Elsevier, vol. 250(C).
    5. Farid Saberi-Movahed & Mohammad Najafzadeh & Adel Mehrpooya, 2020. "Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 529-561, January.
    6. Ye, Lin & Dai, Binhua & Li, Zhuo & Pei, Ming & Zhao, Yongning & Lu, Peng, 2022. "An ensemble method for short-term wind power prediction considering error correction strategy," Applied Energy, Elsevier, vol. 322(C).
    7. Korkos, Panagiotis & Linjama, Matti & Kleemola, Jaakko & Lehtovaara, Arto, 2022. "Data annotation and feature extraction in fault detection in a wind turbine hydraulic pitch system," Renewable Energy, Elsevier, vol. 185(C), pages 692-703.
    8. Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
    9. Wang, Gang & Jia, Ru & Liu, Jinhai & Zhang, Huaguang, 2020. "A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning," Renewable Energy, Elsevier, vol. 145(C), pages 2426-2434.
    10. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    11. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
    12. Yang, Mao & Shi, Chaoyu & Liu, Huiyu, 2021. "Day-ahead wind power forecasting based on the clustering of equivalent power curves," Energy, Elsevier, vol. 218(C).
    13. Alkinoos Psarras & Theodoros Anagnostopoulos & Ioannis Salmon & Yannis Psaromiligkos & Lazaros Vryzidis, 2022. "A Change Management Approach with the Support of the Balanced Scorecard and the Utilization of Artificial Neural Networks," Administrative Sciences, MDPI, vol. 12(2), pages 1-15, May.
    14. Wu, Qiang & Zheng, Hongling & Guo, Xiaozhu & Liu, Guangqiang, 2022. "Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks," Renewable Energy, Elsevier, vol. 199(C), pages 977-992.
    15. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).
    16. Stock-Williams, Clym & Swamy, Siddharth Krishna, 2019. "Automated daily maintenance planning for offshore wind farms," Renewable Energy, Elsevier, vol. 133(C), pages 1393-1403.
    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. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    2. Liang, Yang & Zhang, Dongqin & Zhang, Jize & Hu, Gang, 2024. "A state-of-the-art analysis on decomposition method for short-term wind speed forecasting using LSTM and a novel hybrid deep learning model," Energy, Elsevier, vol. 313(C).
    3. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    4. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
    5. Phong B. Dao, 2023. "On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data," Energies, MDPI, vol. 16(5), pages 1-17, March.
    6. Yang, Shixi & Zhou, Jiaxuan & Gu, Xiwen & Mei, Yiming & Duan, Jiangman, 2024. "A comprehensive framework of the decomposition-based hybrid method for ultra-short-term wind power forecasting with on-site application," Energy, Elsevier, vol. 313(C).
    7. Leng, Zhiyuan & Chen, Lu & Yi, Bin & Liu, Fanqian & Xie, Tao & Mei, Ziyi, 2025. "Short-term wind speed forecasting based on a novel KANInformer model and improved dual decomposition," Energy, Elsevier, vol. 322(C).
    8. Wang, Yufeng & Yang, Zihan & Ma, Jianhua & Jin, Qun, 2024. "A wind speed forecasting framework for multiple turbines based on adaptive gate mechanism enhanced multi-graph attention networks," Applied Energy, Elsevier, vol. 372(C).
    9. Jie Du & Shuaizhi Chen & Linlin Pan & Yubao Liu, 2025. "A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model," Energies, MDPI, vol. 18(5), pages 1-26, February.
    10. Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
    11. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update," Applied Energy, Elsevier, vol. 340(C).
    12. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
    13. Yang, Mao & Han, Chao & Zhang, Wei & Wang, Bo, 2024. "A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information," Energy, Elsevier, vol. 294(C).
    14. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
    15. Qingquan Lv & Jialin Zhang & Jianmei Zhang & Zhenzhen Zhang & Qiang Zhou & Pengfei Gao & Haozhe Zhang, 2025. "Short-Term Wind Power Prediction Model Based on PSO-CNN-LSTM," Energies, MDPI, vol. 18(13), pages 1-18, June.
    16. Yang, Mao & Huang, Yutong & Xu, Chuanyu & Liu, Chenyu & Dai, Bozhi, 2025. "Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations," Applied Energy, Elsevier, vol. 377(PC).
    17. Liu, Tianhong & Qi, Shengli & Qiao, Xianzhu & Liu, Sixing, 2024. "A hybrid short-term wind power point-interval prediction model based on combination of improved preprocessing methods and entropy weighted GRU quantile regression network," Energy, Elsevier, vol. 288(C).
    18. Upma Singh & Mohammad Rizwan & Hasmat Malik & Fausto Pedro García Márquez, 2022. "Wind Energy Scenario, Success and Initiatives towards Renewable Energy in India—A Review," Energies, MDPI, vol. 15(6), pages 1-39, March.
    19. Aisha Blfgeh & Hanadi Alkhudhayr, 2024. "A Machine Learning-Based Sustainable Energy Management of Wind Farms Using Bayesian Recurrent Neural Network," Sustainability, MDPI, vol. 16(19), pages 1-21, September.
    20. Bi, Yubo & Wu, Qiulan & Wang, Shilu & Shi, Jihao & Cong, Haiyong & Ye, Lili & Gao, Wei & Bi, Mingshu, 2023. "Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning," Energy, Elsevier, vol. 284(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:17:y:2024:i:3:p:697-:d:1331018. 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.