The Potential of Machine Learning for Wind Speed and Direction Short-Term Forecasting: A Systematic Review
In one sentence. Systematic review of 23 studies finds deep learning outperforms traditional methods for short-term wind speed and direction forecasting, with mean MAE of 0.56 m/s.
Abstract
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.
Labels
- Topic
- Local & station forecasting
- Type
- Review, survey or tutorial
- Method
- Classical ML (trees, GP, linear, kernels), RNN / LSTM / ConvLSTM
- Time scale
- 0–6 h, 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.3390/computers12100206 OpenAlex:W4387616626
- Added
- 2026-10-22
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