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Publication
The Potential of Machine Learning for Wind Speed and Direction Short-Term Forecasting: A Systematic Review
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Advisor(s)
Abstract(s)
Wind forecasting, which is essential for numerous services and safety, has significantly
improved in accuracy due to machine learning advancements. This study reviews 23 articles from
1983 to 2023 on machine learning for wind speed and direction nowcasting. The wind prediction
ranged from 1 min to 1 week, with more articles at lower temporal resolutions. Most works employed
neural networks, focusing recently on deep learning models. Among the reported performance
metrics, the most prevalent were mean absolute error, mean squared error, and mean absolute
percentage error. Considering these metrics, the mean performance of the examined works was
0.56 m/s, 1.10 m/s, and 6.72%, respectively. The results underscore the novel effectiveness of machine
learning in predicting wind conditions using high-resolution time data and demonstrated that deep
learning models surpassed traditional methods, improving the accuracy of wind speed and direction
forecasts. Moreover, it was found that the inclusion of non-wind weather variables does not benefit
the model’s overall performance. Further studies are recommended to predict both wind speed and
direction using diverse spatial data points, and high-resolution data are recommended along with
the usage of deep learning models.
Description
Keywords
Deep learning Machine learning Nowcast Wind speed Wind direction Wind . Faculdade de Ciências Exatas e da Engenharia
Citation
Alves, D.; Mendonça, F.; Mostafa, S.S.; Morgado-Dias, F. The Potential of Machine Learning for Wind Speed and Direction Short-Term Forecasting: A Systematic Review. Computers 2023, 12, 206. https://doi.org/10.3390/ computers12100206
Publisher
MDPI