An explainable artificial intelligence feature selection framework for transparent, trustworthy, and cost-efficient energy forecasting

Accurate forecasting of renewable power generation is crucial for grid stability and cost efficiency. Feature selection in AI-based forecasting remains challenging due to high data acquisition cost, lack of transparency, and limited user control. We introduce a transparent and cost-sensitive feature selection framework for renewable power forecasting that leverages Explainable Artificial Intelligence (XAI). We integrate SHapley Additive exPlanations (SHAP) and Explain Like I'm 5 (ELI5) to identify dominant and redundant features. This approach enables systematic dataset reduction without compromising model performance. Our case study, based on Photovoltaic (PV) generation data, evaluates the approach across four experimental setups. Experimental results indicate that our XAI-based feature selection reduces the dominance index from 0.37 to 0.17, maintains high predictive accuracy (R² = 0.94, drop < 0.04), and lowers data acquisition costs. Furthermore, eliminating dominant features improves robustness to noise and reduces performance variance by a factor of three compared to the baseline scenario. The developed framework enhances interpretability, supports human-in-the-loop decision-making, and introduces a cost-sensitive objective function for feature selection. By combining transparency, robustness, and efficiency, we contribute to the development and implementation of Trustworthy AI (TAI) applications in energy forecasting, providing a scalable solution for industrial deployment.