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

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

Renewable Energy, 2023, vol. 211, issue C, 938-947

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).

Keywords: Wind speed forecasting; Wind power forecasting; Machine learning; Numerical weather prediction; Downscaling (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:211:y:2023:i:c:p:938-947

DOI: 10.1016/j.renene.2023.05.005

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