Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data

TL;DR

This paper introduces a context-focused explainability method for deep learning anomaly detection in energy data, reducing explanation variability and improving robustness by selecting relevant features and background data.

Contribution

It proposes a novel explainability approach that enhances stability and consistency of explanations by leveraging context-aware feature selection and background data in energy consumption anomaly detection.

Findings

Reduces explanation variability by approximately 38% on average.

Demonstrates consistent explanations across 10 models and multiple datasets.

Improves robustness of XAI techniques in energy anomaly detection.

Abstract

Detecting anomalies in energy consumption data is crucial for identifying energy waste, equipment malfunction, and overall, for ensuring efficient energy management. Machine learning, and specifically deep learning approaches, have been greatly successful in anomaly detection; however, they are black-box approaches that do not provide transparency or explanations. SHAP and its variants have been proposed to explain these models, but they suffer from high computational complexity (SHAP) or instability and inconsistency (e.g., Kernel SHAP). To address these challenges, this paper proposes an explainability approach for anomalies in energy consumption data that focuses on context-relevant information. The proposed approach leverages existing explainability techniques, focusing on SHAP variants, together with global feature importance and weighted cosine similarity to select background dataset based on the context of each anomaly point. By focusing on the context and most relevant features, this approach mitigates the instability of explainability algorithms. Experimental results across 10 different machine learning models, five datasets, and five XAI techniques, demonstrate that our method reduces the variability of explanations providing consistent explanations. Statistical analyses confirm the robustness of our approach, showing an average reduction in variability of approximately 38% across multiple datasets.