IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v340y2025ics0360544225047930.html

The economic value of wind power forecasting: a data-driven method and its application in various scenarios

Author

Listed:
  • Yan, Jie
  • Li, Xiuyu
  • Wang, Han
  • Si, Fangyuan
  • Qiao, Wenjie
  • Liu, Yongqian

Abstract

The penetration of wind power is rapidly increasing under the carbon neutrality target, and the natural wind's randomness and volatility bring significant operational uncertainties to the system. Enhancing the accuracy of wind power forecasting is one of the effective means to reduce the impact of uncertainty. However, it's currently unclear how forecasting errors impact under different scenarios and to what extent the enhancement in forecasting accuracy can contribute to reducing operating costs. To address these issues, this paper proposes a method for conducting quantitative study on the economic value of wind power forecasting. Firstly, a data-driven method is developed to generate wind power time series under specified forecasting errors, serving as crucial inputs for subsequent evaluation. Compared to the traditional Monte Carlo generation model, the proposed method can maintain the mapping relationship between forecasted and measured data in actual conditions, thereby achieving better generation results. Then, a novel quantitative method is constructed from the perspectives of day-ahead economic dispatch and post-evaluation of real operating costs. Finally, the influence of wind power forecasting under various wind penetrations and energy storage allocation ratios is quantitatively studied. IEEE Three-machine Nine-bus system is taken as an example, the results indicate that when wind power penetration is no more than 50 % and the forecasting error is already less than 12 %, the economic benefits brought by further improving accuracy are limited. When the penetration exceeds 50 %, operating costs show a nearly linear downward trend as the forecasting error decreases, improving the accuracy is an eternal theme.

Suggested Citation

  • Yan, Jie & Li, Xiuyu & Wang, Han & Si, Fangyuan & Qiao, Wenjie & Liu, Yongqian, 2025. "The economic value of wind power forecasting: a data-driven method and its application in various scenarios," Energy, Elsevier, vol. 340(C).
    • Handle: RePEc:eee:energy:v:340:y:2025:i:c:s0360544225047930
    DOI: 10.1016/j.energy.2025.139151
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225047930
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.139151?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Bo & Wang, Shuming & Zhou, Xianzhong & Watada, Junzo, 2016. "Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties," Energy, Elsevier, vol. 111(C), pages 18-31.
    2. Zhang, Yagang & Pan, Zhiya & Wang, Hui & Wang, Jingchao & Zhao, Zheng & Wang, Fei, 2023. "Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach," Energy, Elsevier, vol. 283(C).
    3. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
    4. Yi, Yong & Wang, Liming & Chen, Zhengying, 2021. "Adaptive global kernel interval SVR-based machine learning for accelerated dielectric constant prediction of polymer-based dielectric energy storage," Renewable Energy, Elsevier, vol. 176(C), pages 81-88.
    5. 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).
    6. Cheng, Junhao & Luo, Xing & Jin, Zhi, 2024. "Integrating domain knowledge into transformer for short-term wind power forecasting," Energy, Elsevier, vol. 312(C).
    7. Liu, Fan & Bie, Zhaohong & Liu, Shiyu & Ding, Tao, 2017. "Day-ahead optimal dispatch for wind integrated power system considering zonal reserve requirements," Applied Energy, Elsevier, vol. 188(C), pages 399-408.
    8. 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.
    9. Zhao, Jing & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Zhu, Wenjin, 2021. "A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions," Energy, Elsevier, vol. 218(C).
    10. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    11. Duarte Jacondino, William & Nascimento, Ana Lucia da Silva & Calvetti, Leonardo & Fisch, Gilberto & Augustus Assis Beneti, Cesar & da Paz, Sheila Radman, 2021. "Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model," Energy, Elsevier, vol. 230(C).
    12. Shin, Heesoo & Rüttgers, Mario & Lee, Sangseung, 2023. "Effects of spatiotemporal correlations in wind data on neural network-based wind predictions," Energy, Elsevier, vol. 279(C).
    13. Hlalele, Thabo G. & Naidoo, Raj M. & Bansal, Ramesh C. & Zhang, Jiangfeng, 2020. "Multi-objective stochastic economic dispatch with maximal renewable penetration under renewable obligation," Applied Energy, Elsevier, vol. 270(C).
    14. Ye, Lin & Peng, Yishu & Li, Yilin & Li, Zhuo, 2024. "A novel informer-time-series generative adversarial networks for day-ahead scenario generation of wind power," Applied Energy, Elsevier, vol. 364(C).
    15. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    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. Munyao, Jackson Ndoto & Oluoch, Lillian Achola & Iftikhar, Hasnain & Rodrigues, Paulo Canas, 2025. "Recurrent neural networks for hierarchical time series forecasting: An application to the S&P 500 market value," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 678(C).
    2. Mario Tovar & Miguel Robles & Felipe Rashid, 2020. "PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México," Energies, MDPI, vol. 13(24), pages 1-15, December.
    3. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Chang, Mingheng & Chen, Xiangyue & Wang, Wenpeng & Ma, Lei & Chen, Bolong, 2023. "A multistep short-term solar radiation forecasting model using fully convolutional neural networks and chaotic aquila optimization combining WRF-Solar model results," Energy, Elsevier, vol. 271(C).
    4. Rizk M Rizk-Allah & Lobna M Abouelmagd & Ashraf Darwish & Vaclav Snasel & Aboul Ella Hassanien, 2024. "Explainable AI and optimized solar power generation forecasting model based on environmental conditions," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-33, October.
    5. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    6. Hao Zhen & Dongxiao Niu & Min Yu & Keke Wang & Yi Liang & Xiaomin Xu, 2020. "A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    7. Zhang, Yijie & Ma, Tao & Yang, Hongxing, 2025. "Prediction-based optimal system design of an improved building-to-vehicle-to-building energy community under different operation strategies concerning three PV installation types," Energy, Elsevier, vol. 341(C).
    8. Elham M. Al-Ali & Yassine Hajji & Yahia Said & Manel Hleili & Amal M. Alanzi & Ali H. Laatar & Mohamed Atri, 2023. "Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    9. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
    10. Xu, Fang Yuan & Tang, Rui Xin & Xu, Si Bin & Fan, Yi Liang & Zhou, Ya & Zhang, Hao Tian, 2021. "Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification," Energy, Elsevier, vol. 223(C).
    11. John Kamwele Mutinda & Li Yong, 2026. "Deep Learning Applications in Hierarchical Time Series Forecasting of Market Value," Computational Economics, Springer;Society for Computational Economics, vol. 67(6), pages 4765-4803, June.
    12. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    13. Ze Wu & Feifan Pan & Dandan Li & Hao He & Tiancheng Zhang & Shuyun Yang, 2022. "Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    14. Zhang, Can & Xiao, Xianyong & Wang, Ying & Hou, Michael Z. & Huang, Shudong & Hu, Wenxi & Hu, Ming & Huang, Rui, 2025. "Fine-grained ultra-short-term wind power forecasting based on Temporal Fusion Transformers integrated with turbine power time series clustering," Energy, Elsevier, vol. 335(C).
    15. Zhou, Zhengda & Dai, Yeming & Leng, Mingming, 2025. "A photovoltaic power forecasting framework based on Attention mechanism and parallel prediction architecture," Applied Energy, Elsevier, vol. 391(C).
    16. Zhu, Yinlong & Li, Guoliang & Jiang, Yonglei & Li, Ming & Wang, Yunfeng & Zhang, Ying & Liu, Yali & Yao, Muchi, 2025. "Predicting photovoltaic greenhouse irradiance at low-latitudes of plateau based on ultra-short-term time series," Renewable Energy, Elsevier, vol. 239(C).
    17. Aamer A. Shah & Almani A. Aftab & Xueshan Han & Mazhar Hussain Baloch & Mohamed Shaik Honnurvali & Sohaib Tahir Chauhdary, 2023. "Prediction Error-Based Power Forecasting of Wind Energy System Using Hybrid WT–ROPSO–NARMAX Model," Energies, MDPI, vol. 16(7), pages 1-15, April.
    18. Shang, Jingyi & Gao, Jinfeng & Jiang, Xin & Liu, Mingguang & Liu, Dunnan, 2023. "Optimal configuration of hybrid energy systems considering power to hydrogen and electricity-price prediction: A two-stage multi-objective bi-level framework," Energy, Elsevier, vol. 263(PF).
    19. Li, Qing & Zhang, Xinyan & Ma, Tianjiao & Jiao, Chunlei & Wang, Heng & Hu, Wei, 2021. "A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine," Energy, Elsevier, vol. 224(C).
    20. Ma, Huixin & Zhang, Chu & Peng, Tian & Nazir, Muhammad Shahzad & Li, Yiman, 2022. "An integrated framework of gated recurrent unit based on improved sine cosine algorithm for photovoltaic power forecasting," Energy, Elsevier, vol. 256(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:eee:energy:v:340:y:2025:i:c:s0360544225047930. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.