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.

翻译：本文提出了一种将可解释人工智能（XAI）技术与自适应学习相结合的方法，以增强能耗预测模型，重点解决数据分布偏移问题。通过利用SHAP聚类，该方法为模型预测提供可解释性说明，并利用这些洞察自适应地优化模型，平衡模型复杂度与预测性能。我们引入了一个三阶段流程：（1）获取SHAP值以解释模型预测；（2）对SHAP值进行聚类以识别不同模式和异常值；（3）基于推导出的SHAP聚类特征优化模型。该方法能缓解过拟合，确保在处理数据分布偏移时的鲁棒性。我们使用包含建筑物能耗记录的综合数据集以及两个额外数据集来评估该方法，以检验其向其他领域、回归及分类问题的可迁移性。实验结果表明，该方法在两类任务中均有效，提升了预测性能并提供了可解释的模型说明。