Toward Explainable NILM: Real-Time Event-Based NILM Framework for High-Frequency Data

TL;DR

This paper introduces an explainable, real-time NILM framework that uses high-frequency data and machine learning to accurately identify appliance usage with transparency and low latency.

Contribution

It presents a novel, transparent NILM framework combining event detection, feature extraction, classification, and explainability tailored for high-frequency data.

Findings

Achieved 90% classification accuracy on PLAID dataset.

Maintained low computational requirements and under-one-second latency.

Enhanced explainability with SHAP analysis of feature contributions.

Abstract

Non-Intrusive Load Monitoring (NILM) is an advanced, and cost-effective technique for monitoring appliance-level energy consumption. However, its adaptability is hindered by the lack of transparency and explainability. To address this challenge, this paper presents an explainable, real-time, event-based NILM framework specifically designed for high-frequency datasets. The proposed framework ensures transparency at every stage by integrating a z-score-based event detector, appliance signature estimation, Fourier-based feature extraction, an XG-Boost classifier, and post hoc SHAP analysis. The SHAP analysis further quantifies the contribution of individual features, such as cosine of specific harmonic phases, to appliance classification. The framework is trained and evaluated on the PLAID dataset, and achieved a classification accuracy of 90% while maintaining low computational requirements and a latency of less than one second.