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

arXiv:2310.18629v2 Announce Type: replace
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 …

abstract accuracy arxiv black boxes box cs.lg cs.sy eess.sy forecasting glass interpretability issue machine machine learning machine learning models modeling networks neural networks paper power transparency type wind