Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction

This paper presents an approach integrating explainable artificial intelligence (XAI) techniques with adaptive learning to enhance energy consumption prediction models, with a focus on handling data distribution shifts. Leveraging SHAP clustering, our method provides interpretable explanations for model predictions and uses these insights to adaptively refine the model, balancing model complexity with predictive performance. We introduce a three-stage process: (1) obtaining SHAP values to explain model predictions, (2) clustering SHAP values to identify distinct patterns and outliers, and (3) refining …

artificial artificial intelligence beyond clustering consumption cs.db cs.lg data distribution energy explainable artificial intelligence focus insights intelligence paper prediction prediction models predictions refine shap xai