IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v10y2025i3p38-d1615777.html
   My bibliography  Save this article

Historical Hourly Information of Four European Wind Farms for Wind Energy Forecasting and Maintenance

Author

Listed:
  • Javier Sánchez-Soriano

    (Escuela Politécnica Superior, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain)

  • Pedro Jose Paniagua-Falo

    (Escuela Politécnica Superior, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain)

  • Carlos Quiterio Gómez Muñoz

    (HCTLab Research Group, Universidad Autónoma de Madrid, 28049 Madrid, Spain)

Abstract

For an electric company, having an accurate forecast of the expected electrical production and maintenance from its wind farms is crucial. This information is essential for operating in various existing markets, such as the Iberian Energy Market Operator—Spanish Hub (OMIE in its Spanish acronym), the Portuguese Hub (OMIP in its Spanish acronym), and the Iberian electricity market between the Kingdom of Spain and the Portuguese Republic (MIBEL in its Spanish acronym), among others. The accuracy of these forecasts is vital for estimating the costs and benefits of handling electricity. This article explains the process of creating the complete dataset, which includes the acquisition of the hourly information of four European wind farms as well as a description of the structure and content of the dataset, which amounts to 2 years of hourly information. The wind farms are in three countries: Auvergne-Rhône-Alpes (France), Aragon (Spain), and the Piemonte region (Italy). The dataset was built and validated following the CRISP-DM methodology, ensuring a structured and replicable approach to data processing and preparation. To confirm its reliability, the dataset was tested using a basic predictive model, demonstrating its suitability for wind energy forecasting and maintenance optimization. The dataset presented is available and accessible for improving the forecasting and management of wind farms, especially for the detection of faults and the elaboration of a preventive maintenance plan.

Suggested Citation

  • Javier Sánchez-Soriano & Pedro Jose Paniagua-Falo & Carlos Quiterio Gómez Muñoz, 2025. "Historical Hourly Information of Four European Wind Farms for Wind Energy Forecasting and Maintenance," Data, MDPI, vol. 10(3), pages 1-14, March.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:3:p:38-:d:1615777
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/10/3/38/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/10/3/38/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sergio Bemposta Rosende & Javier Sánchez-Soriano & Carlos Quiterio Gómez Muñoz & Javier Fernández Andrés, 2020. "Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants," Energies, MDPI, vol. 13(21), pages 1-23, November.
    2. Gökhan Ekinci & Harun Kemal Ozturk, 2025. "Forecasting Wind Farm Production in the Short, Medium, and Long Terms Using Various Machine Learning Algorithms," Energies, MDPI, vol. 18(5), pages 1-27, February.
    3. Md. Ahasan Habib & M. J. Hossain, 2024. "Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering," Energies, MDPI, vol. 17(5), pages 1-23, March.
    4. Monica Borunda & Adrián Ramírez & Raul Garduno & Carlos García-Beltrán & Rito Mijarez, 2023. "Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data," Energies, MDPI, vol. 16(23), pages 1-34, December.
    5. 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, Jianguo & Yuan, Weiru & Zhang, Shude & Cheng, Shun & Han, Lincheng, 2024. "Implementing ultra-short-term wind power forecasting without information leakage through cascade decomposition and attention mechanism," Energy, Elsevier, vol. 312(C).
    2. 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.
    3. Md Tariqul Islam & M. Jahangir Hossain & Md. Ahasan Habib & Muhammad Ahsan Zamee, 2025. "Adaptive Hosting Capacity Forecasting in Distribution Networks with Distributed Energy Resources," Energies, MDPI, vol. 18(2), pages 1-25, January.
    4. Shuo-Yan Chou & Xuan Loc Pham & Thi Anh Tuyet Nguyen & Tiffany Hui-Kuang Yu, 2023. "Optimal maintenance planning with special emphasis on deterioration process and vessel routing for offshore wind systems," Energy & Environment, , vol. 34(4), pages 739-763, June.
    5. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    6. Pei Zhang & Shugeng Yang & Yan Li & Jiayang Gu & Zhiqiang Hu & Ruoyu Zhang & Yougang Tang, 2020. "Dynamic Response of Articulated Offshore Wind Turbines under Different Water Depths," Energies, MDPI, vol. 13(11), pages 1-20, June.
    7. Chandra Ade Irawan & Majid Eskandarpour & Djamila Ouelhadj & Dylan Jones, 2019. "Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm," Post-Print hal-02509382, HAL.
    8. Scarf, P.A. & Cavalcante, C.A.V. & Lopes, R.S., 2019. "Delay-time modelling of a critical system subject to random inspections," European Journal of Operational Research, Elsevier, vol. 278(3), pages 772-782.
    9. Samir Benammar & Kong Fah Tee, 2021. "Criticality Analysis and Maintenance of Solar Tower Power Plants by Integrating the Artificial Intelligence Approach," Energies, MDPI, vol. 14(18), pages 1-27, September.
    10. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
    11. Zhou, Yifan & Miao, Jindan & Yan, Bin & Zhang, Zhisheng, 2020. "Bio-objective long-term maintenance scheduling for wind turbines in multiple wind farms," Renewable Energy, Elsevier, vol. 160(C), pages 1136-1147.
    12. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Ouassaid, Mohammed, 2023. "Wind turbine output power prediction and optimization based on a novel adaptive neuro-fuzzy inference system with the moving window," Energy, Elsevier, vol. 263(PE).
    13. Saleh, Ali & Chiachío, Manuel & Salas, Juan Fernández & Kolios, Athanasios, 2023. "Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    14. Ren, Zhengru & Verma, Amrit Shankar & Li, Ye & Teuwen, Julie J.E. & Jiang, Zhiyu, 2021. "Offshore wind turbine operations and maintenance: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    15. Lee, Namkyoung & Woo, Joohyun & Kim, Sungryul, 2025. "A deep reinforcement learning ensemble for maintenance scheduling in offshore wind farms," Applied Energy, Elsevier, vol. 377(PA).
    16. Irawan, Chandra Ade & Eskandarpour, Majid & Ouelhadj, Djamila & Jones, Dylan, 2021. "Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm," European Journal of Operational Research, Elsevier, vol. 289(3), pages 912-926.
    17. Sergio Bemposta Rosende & Sergio Ghisler & Javier Fernández-Andrés & Javier Sánchez-Soriano, 2022. "Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management," Data, MDPI, vol. 7(5), pages 1-10, April.

    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:jdataj:v:10:y:2025:i:3:p:38-:d:1615777. 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.