arXiv AI By Robbie Southam, Zhong Fan, Ke Li

Multistep and Multivariate Time Series Explainability with GradientSHAP: A Wind Power Case Study

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The paper presents a tailored GradientSHAP method to explain multivariate, multistep wind power forecasts, addressing the opacity of AI models in renewable energy. It introduces new visualizations that reveal local and global relationships among environmental factors, site conditions, and time dynamics. The study claims to be the first to apply GradientSHAP to wind power forecasting, highlighting its potential for day‑ahead grid management while noting implementation challenges.

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