IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v211y2023icp938-947.html
   My bibliography  Save this article

Wind power predictions from nowcasts to 4-hour forecasts: A learning approach with variable selection

Author

Listed:
  • Bouche, Dimitri
  • Flamary, Rémi
  • d’Alché-Buc, Florence
  • Plougonven, Riwal
  • Clausel, Marianne
  • Badosa, Jordi
  • Drobinski, Philippe

Abstract

We study short-term prediction of wind speed and wind power (every 10 min up to 4 h ahead). Accurate forecasts for these quantities are crucial to mitigate the negative effects of wind farms’ intermittent production on energy systems and markets. We use machine learning to combine outputs from numerical weather prediction models with local observations. The former provide valuable information on higher scales dynamics while the latter gives the model fresher and location-specific data. So as to make the results usable for practitioners, we focus on well-known methods which can handle a high volume of data. We study first variable selection using both a linear technique and a nonlinear one. Then we exploit these results to forecast wind speed and wind power still with an emphasis on linear models versus nonlinear ones. For the wind power prediction, we also compare the indirect approach (wind speed predictions passed through a power curve) and the direct one (directly predict wind power).

Suggested Citation

  • Bouche, Dimitri & Flamary, Rémi & d’Alché-Buc, Florence & Plougonven, Riwal & Clausel, Marianne & Badosa, Jordi & Drobinski, Philippe, 2023. "Wind power predictions from nowcasts to 4-hour forecasts: A learning approach with variable selection," Renewable Energy, Elsevier, vol. 211(C), pages 938-947.
    • Handle: RePEc:eee:renene:v:211:y:2023:i:c:p:938-947
    DOI: 10.1016/j.renene.2023.05.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123006201
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.05.005?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    2. Peter Bauer & Alan Thorpe & Gilbert Brunet, 2015. "The quiet revolution of numerical weather prediction," Nature, Nature, vol. 525(7567), pages 47-55, September.
    3. Hoolohan, Victoria & Tomlin, Alison S. & Cockerill, Timothy, 2018. "Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data," Renewable Energy, Elsevier, vol. 126(C), pages 1043-1054.
    4. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    5. Dupré, Aurore & Drobinski, Philippe & Alonzo, Bastien & Badosa, Jordi & Briard, Christian & Plougonven, Riwal, 2020. "Sub-hourly forecasting of wind speed and wind energy," Renewable Energy, Elsevier, vol. 145(C), pages 2373-2379.
    6. Aurore Dupré & Philippe Drobinski & Jordi Badosa & Christian Briard & Peter Tankov, 2020. "The Economic Value of Wind Energy Nowcasting," Energies, MDPI, vol. 13(20), pages 1-20, October.
    7. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(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. Sameer Al-Dahidi & Piero Baraldi & Enrico Zio & Lorenzo Montelatici, 2021. "Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production," Sustainability, MDPI, vol. 13(11), pages 1-19, June.
    2. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    3. Hu, Jianming & Heng, Jiani & Wen, Jiemei & Zhao, Weigang, 2020. "Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm," Renewable Energy, Elsevier, vol. 162(C), pages 1208-1226.
    4. Liu, Jiarui & Fu, Yuchen, 2023. "Decomposition spectral graph convolutional network based on multi-channel adaptive adjacency matrix for renewable energy prediction," Energy, Elsevier, vol. 284(C).
    5. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    6. Zhao, Xinyu & Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Yu, Daren & Chang, Juntao, 2021. "Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation," Energy, Elsevier, vol. 234(C).
    7. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    8. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    9. Héctor Manuel Zárate S., 2005. "Cambios en la estructura salarial: una historia desde la regresión cuanfílica," Monetaria, CEMLA, vol. 0(4), pages 339-364, octubre-d.
    10. Efobi, Uchenna & Asongu, Simplice & Okafor, Chinelo & Tchamyou, Vanessa & Tanankem, Belmondo, 2016. "Diaspora Remittance Inflow, Financial Development and the Industrialisation of Africa," MPRA Paper 76121, University Library of Munich, Germany.
    11. Leon Zolotoy & Don O’Sullivan & Keke Song, 2021. "The Role of Ethical Standards in the Relationship Between Religious Social Norms and M&A Announcement Returns," Journal of Business Ethics, Springer, vol. 170(4), pages 721-742, May.
    12. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    13. Chitsazan, Mohammad Amin & Sami Fadali, M. & Trzynadlowski, Andrzej M., 2019. "Wind speed and wind direction forecasting using echo state network with nonlinear functions," Renewable Energy, Elsevier, vol. 131(C), pages 879-889.
    14. Trojanek, Radoslaw & Huderek-Glapska, Sonia, 2018. "Measuring the noise cost of aviation – The association between the Limited Use Area around Warsaw Chopin Airport and property values," Journal of Air Transport Management, Elsevier, vol. 67(C), pages 103-114.
    15. Paulo M.M. Rodrigues & Rita Fradique Lourenço, 2015. "House prices: bubbles, exuberance or something else? Evidence from euro area countries," Working Papers w201517, Banco de Portugal, Economics and Research Department.
    16. repec:rre:publsh:v:39:y:2009:i:2:p:149-69 is not listed on IDEAS
    17. Jinhua Wen & Yian Hua & Chenkai Cai & Shiwu Wang & Helong Wang & Xinyan Zhou & Jian Huang & Jianqun Wang, 2023. "Probabilistic Forecast and Risk Assessment of Flash Droughts Based on Numeric Weather Forecast: A Case Study in Zhejiang, China," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    18. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    19. Juan Mora & Antonia Febrer, 2005. "Wage Distribution In Spain, 1994-1999: An Application Of A Flexible Estimator Of Conditional Distributions," Working Papers. Serie EC 2005-04, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    20. Xiaoying Liu & Jere R. Behrman & Emily Hannum & Fan Wang & Qingguo Zhao, 2022. "Same environment, stratified impacts? Air pollution, extreme temperatures, and birth weight in south China," Papers 2204.00219, arXiv.org.
    21. Asongu, Simplice A., 2017. "Assessing marginal, threshold, and net effects of financial globalisation on financial development in Africa," Journal of Multinational Financial Management, Elsevier, vol. 40(C), pages 103-114.

    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:renene:v:211:y:2023:i:c:p:938-947. 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/renewable-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.