Accurate forecasting of renewable power generation is crucial for grid stability and cost efficiency.Feature se-lection in AI-based forecasting remains challenging due to high data acquisition cost,lack of transparenc...Accurate forecasting of renewable power generation is crucial for grid stability and cost efficiency.Feature se-lection 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 ex-Planations(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(R^(2)=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 de-cisionmaking,and introduces a cost-sensitive objective function for feature selection.By combining trans-parency,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.展开更多
文摘Accurate forecasting of renewable power generation is crucial for grid stability and cost efficiency.Feature se-lection 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 ex-Planations(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(R^(2)=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 de-cisionmaking,and introduces a cost-sensitive objective function for feature selection.By combining trans-parency,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.
