What does this research mean for the field?

Optimal machine learning model selection in wind power forecasting is task-dependent, with tree-based ensemble methods and feature engineering excelling at wind speed correction, while deep learning architectures, particularly attention-based models, substantially outperform traditional methods in actual power forecasting. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate whether increasing machine learning model complexity reliably improves accuracy across wind speed correction and wind power forecasting stages.

September 8, 2026ForecastingOpen Access

Does Machine Learning Improve Wind Power Forecasting? An Experimental Investigation

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Authors

ZLZ. M. LiChina Meteorological Administration YCYu ChenThe University of Tokyo TYTingzhao YuChina Meteorological Administration

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Implication

Comparative benchmark reveals task-dependent advantages of tree ensembles and deep learning in wind power forecasting, indicating that model complexity must align with specific operational stages.

Key Points

To evaluate whether increasing machine learning model complexity reliably improves accuracy across wind speed correction and wind power forecasting stages.
Evaluated 10 machine learning models across four progressively enriched feature configurations for wind speed correction.
Benchmarked 20 traditional, time-series deep learning, and Transformer-based models for wind power forecasting across two geographically distinct wind farms.
Tree-based ensemble learners, especially gradient boosting variants, achieved superior accuracy in wind speed correction, where feature engineering yielded larger gains than model selection.
Deep learning architectures consistently outperformed traditional approaches in power forecasting, with attention-based models demonstrating the most robust generalization across regimes and recurrent networks showing the greatest sensitivity to regime shifts.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6a9fd7d558e84d0ff5b46ed1 https://doi.org/10.3390/forecast8050079

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Implication

Comparative benchmark reveals task-dependent advantages of tree ensembles and deep learning in wind power forecasting, indicating that model complexity must align with specific operational stages.

Does Machine Learning Improve Wind Power Forecasting? An Experimental Investigation

Discussion

Member takes

Implication

Key Points

Cite This Study

Does Machine Learning Improve Wind Power Forecasting? An Experimental Investigation

Discussion

Member takes

Implication

Key Points

Cite This Study

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