Artificial Intelligence in Wind Speed Forecasting: A Review

Sandra Minerva Valdivia-Bautista, José Antonio Domínguez-Navarro, Marco Pérez-Cisneros, Carlos Jesahel Vega-Gómez () and Beatriz Castillo-Téllez ()
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Sandra Minerva Valdivia-Bautista: Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Guadalajara 44430, Mexico
José Antonio Domínguez-Navarro: Department of Electrical Engineering, School of Engineering and Architecture, University of Zaragoza, C/María de Luna, 50018 Zaragoza, Spain
Marco Pérez-Cisneros: Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Guadalajara 44430, Mexico
Carlos Jesahel Vega-Gómez: Centro Universitario de CuTlajomulco, Universidad de Guadalajara, Tlajomulco de Zuñiga 45670, Mexico
Beatriz Castillo-Téllez: Centro Universitario de CuTonalá, Universidad de Guadalajara, Tonalá 45425, Mexico

Energies, 2023, vol. 16, issue 5, 1-28

Abstract: Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values.

Keywords: wind speed forecasting; artificial neural networks; artificial intelligence; ensemble prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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