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

TL;DR

This paper introduces a novel XAI-based adaptive learning method using SHAP clustering to improve energy consumption prediction, effectively handling data shifts and enhancing model interpretability and robustness.

Contribution

It presents a three-stage SHAP clustering approach that adaptively refines models, addressing data distribution shifts and improving prediction accuracy in energy consumption modeling.

Findings

Enhanced predictive accuracy across datasets

Improved interpretability of model explanations

Robustness to data distribution shifts

Abstract

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.