Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy

TL;DR

This paper introduces a transparent, interpretable wind power forecasting model that combines high accuracy with explainability by using shape functions and interaction terms, outperforming many benchmarks.

Contribution

The paper presents a novel glass-box model that achieves high accuracy and interpretability in wind power forecasting, bridging the gap between black-box models and transparency.

Findings

Outperforms most benchmark models in accuracy

Provides effective global and local interpretability

Achieves comparable performance to neural networks

Abstract

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.