Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy
This addresses the need for transparent and reliable forecasting in the renewable energy domain, though it is incremental as it builds on existing interpretable modeling techniques.
The paper tackled the problem of low interpretability in high-accuracy machine learning models for wind power forecasting by proposing a glass-box approach that sums feature effects and includes interaction terms, achieving comparable performance to the best neural networks.
Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box approach that combines high accuracy with transparency for wind power forecasting. Specifically, the core is to sum up the feature effects by constructing shape functions, which effectively map the intricate non-linear relationships between wind power output and input features. Furthermore, the forecasting model is enriched by incorporating interaction terms that adeptly capture interdependencies and synergies among the input features. The additive nature of the proposed glass-box approach ensures its interpretability. Simulation results show that the proposed glass-box approach effectively interprets the results of wind power forecasting from both global and instance perspectives. Besides, it outperforms most benchmark models and exhibits comparable performance to the best-performing neural networks. This dual strength of transparency and high accuracy positions the proposed glass-box approach as a compelling choice for reliable wind power forecasting.