Fusion-ResNet: A Lightweight multi-label NILM Model Using PCA-ICA Feature Fusion
Abstract
Non-intrusive load monitoring (NILM) is an advanced load monitoring technique that uses data-driven algorithms to disaggregate the total power consumption of a household into the consumption of individual appliances. However, real-world NILM deployment still faces major challenges, including overfitting, low model generalization, and disaggregating a large number of appliances operating at the same time. To address these challenges, this work proposes an end-to-end framework for the NILM classification task, which consists of high-frequency labeled data, a feature extraction method, and a lightweight neural network. Within this framework, we introduce a novel feature extraction method that fuses Independent Component Analysis (ICA) and Principal Component Analysis (PCA) features. Moreover, we propose a lightweight architecture for multi-label NILM classification (Fusion-ResNet). The proposed feature-based model achieves a higher score on average and across different appliances compared to state-of-the-art NILM classifiers while minimizing the training and inference time. Finally, we assessed the performance of our model against baselines with a varying number of simultaneously active devices. Results demonstrate that Fusion-ResNet is relatively robust to stress conditions with up to 15 concurrently active appliances.
Cite
@article{arxiv.2511.12139,
title = {Fusion-ResNet: A Lightweight multi-label NILM Model Using PCA-ICA Feature Fusion},
author = {Sahar Moghimian Hoosh and Ilia Kamyshev and Henni Ouerdane},
journal= {arXiv preprint arXiv:2511.12139},
year = {2025}
}
Comments
Extended version of the conference paper "Enhancing Non-Intrusive Load Monitoring with Features Extracted by Independent Component Analysis" -- arXiv:2501.16817. Instead of solely using ICA or PCA for feature extraction, we propose the fusion of ICA and PCA, which outperforms other baseline models. This extended version is meant for journal publication