Fusion-ResNet: A Lightweight multi-label NILM Model Using PCA-ICA Feature Fusion

• URL: http://arxiv.org/abs/2511.12139v1
• Date: Sat, 15 Nov 2025 10:10:46 GMT
• Title: Fusion-ResNet: A Lightweight multi-label NILM Model Using PCA-ICA Feature Fusion
• Authors: Sahar Moghimian Hoosh, Ilia Kamyshev, Henni Ouerdane,
• Abstract summary: Non-intrusive load monitoring (NILM) uses data-driven algorithms to disaggregate the total power consumption of a household into the consumption of individual appliances.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $F1$ 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.

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