Non-Intrusive Load Monitoring Based on Image Load Signatures and Continual Learning

TL;DR

This paper introduces a novel NILM approach that transforms electrical signals into visual signatures and employs continual learning to enhance device identification accuracy amidst changing load conditions.

Contribution

It combines image load signatures with continual learning and self-supervised pre-training to improve robustness and adaptability of NILM models.

Findings

Significant accuracy improvements over existing methods

Effective adaptation to new load types

Enhanced feature robustness and generalization

Abstract

Non-Intrusive Load Monitoring (NILM) identifies the operating status and energy consumption of each electrical device in the circuit by analyzing the electrical signals at the bus, which is of great significance for smart power management. However, the complex and changeable load combinations and application environments lead to the challenges of poor feature robustness and insufficient model generalization of traditional NILM methods. To this end, this paper proposes a new non-intrusive load monitoring method that integrates "image load signature" and continual learning. This method converts multi-dimensional power signals such as current, voltage, and power factor into visual image load feature signatures, and combines deep convolutional neural networks to realize the identification and classification of multiple devices; at the same time, self-supervised pre-training is introduced to improve feature generalization, and continual online learning strategies are used to overcome model forgetting to adapt to the emergence of new loads. This paper conducts a large number of experiments on high-sampling rate load datasets, and compares a variety of existing methods and model variants. The results show that the proposed method has achieved significant improvements in recognition accuracy.