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This work has been partially supported by "Agencia Espanola de Investigacion (Espana)" (NEXO project, grant reference: PID2023-150663NB-C21).

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Perez-Aracil, JAuthorCasanova-Mateo, CAuthor

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January 19, 2025
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Article

Hybridizing Machine Learning Algorithms With Numerical Models for Accurate Wind Power Forecasting

Publicated to:Expert Systems. 42 (2): e13830- - 2025-02-01 42(2), DOI: 10.1111/exsy.13830

Authors: Abad-Santjago, A; Peláez-Rodríguez, C; Pérez-Aracil, J; Sanz-Justo, J; Casanova-Mateo, C; Salcedo-Sanz, S

Affiliations

Univ Alcala, Dept Signal Proc & Commun, Alcala De Henares, Spain - Author
Univ Politecn Madrid, Dept Comp Syst Engn, Madrid, Spain - Author
Univ Valladolid, Lab Teledetecc, Valladolid, Spain - Author

Abstract

An accurate prediction of wind power generation is crucial for optimizing the integration of wind energy into the power grid, ensuring energy reliability. This research focuses on enhancing the accuracy of wind power generation forecasts by combining data from mesoscale and reanalysis models with Machine Learning (ML) approaches. We utilized WRF forecast data alongside ERA5 reanalysis data to estimate wind power generation for a wind farm located at Valladolid, Spain. The study evaluated the performance of ML models based on WRF and ERA5 data individually, as well as a combined model using inputs from both datasets. The hybrid model combining WRF and ERA5 data with ML resulted in a 15% improvement in root mean square error (RMSE) and a 10% increase in R2$$ {R}2 $$ compared with standalone models, providing a more reliable 1-h forecast of wind power generation. Additionally, the availability of data over time was addressed: WRF provides the advantage of projecting data into the future, whereas ERA5 offers retrospective data.

Keywords

BankEnergyEra5 reanalysisHybrid approachesMachine learningPerformanceResourceSensitivitySimulationSpeed predictionWeather researchWind power forecastingWrf and mesoscale modelWrf and mesoscale modelsWrf model

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Expert Systems due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 124/204, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Computer Science, Artificial Intelligence. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Control and Systems Engineering.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-31:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 10 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.