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

arXiv: https://arxiv.org/abs/2511.12139 · HTML: https://mlsanity.com/paper/2511.12139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 15 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.02352/) (2021)
- [Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning](https://mlsanity.com/paper/2209.03759/) (2022)
- [Enhancing Non-Intrusive Load Monitoring with Features Extracted by Independent Component Analysis](https://mlsanity.com/paper/2501.16817/) (2025)

## Abstract (arXiv)

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.