  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.
