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[2602.13010] Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles
Summary
This paper presents a method for probabilistic wind power forecasting using tree-based machine learning and weather ensembles, demonstrating significant accuracy improvements over traditional methods.
Why It Matters
As renewable energy sources become increasingly integrated into power grids, accurate forecasting of wind power generation is crucial. This research highlights advanced machine learning techniques that enhance forecasting accuracy, which can lead to better energy management and grid stability.
Key Takeaways
- The study compares three probabilistic prediction methods for wind power forecasting.
- Machine learning methods improved mean absolute error by up to 53% compared to traditional approaches.
- The conditional diffusion model yielded the best overall forecasts.
- Using an ensemble of weather forecasts can enhance point forecast accuracy by up to 23%.
- This research supports the integration of renewable energy into the power grid through improved forecasting.
Computer Science > Machine Learning arXiv:2602.13010 (cs) [Submitted on 13 Feb 2026] Title:Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles Authors:Max Bruninx, Diederik van Binsbergen, Timothy Verstraeten, Ann Nowé, Jan Helsen View a PDF of the paper titled Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles, by Max Bruninx and 4 other authors View PDF HTML (experimental) Abstract:Accurate production forecasts are essential to continue facilitating the integration of renewable energy sources into the power grid. This paper illustrates how to obtain probabilistic day-ahead forecasts of wind power generation via gradient boosting trees using an ensemble of weather forecasts. To this end, we perform a comparative analysis across three state-of-the-art probabilistic prediction methods-conformalised quantile regression, natural gradient boosting and conditional diffusion models-all of which can be combined with tree-based machine learning. The methods are validated using four years of data for all wind farms present within the Belgian offshore zone. Additionally, the point forecasts are benchmarked against deterministic engineering methods, using either the power curve or an advanced approach incorporating a calibrated analytical wake model. The experimental results show that the machine learning methods improve the mean absolute error by up to 53% and 33% compared to the power curve and the cal...