  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 the model based on the derived SHAP clustering characteristics. Our
approach mitigates overfitting and ensures robustness in handling data
distribution shifts. We evaluate our method on a comprehensive dataset
comprising energy consumption records of buildings, as well as two additional
datasets to assess the transferability of our approach to other domains,
regression, and classification problems. Our experiments demonstrate the
effectiveness of our approach in both task types, resulting in improved
predictive performance and interpretable model explanations.
