# Enhancing Non-Intrusive Load Monitoring with Features Extracted by Independent Component Analysis

arXiv: https://arxiv.org/abs/2501.16817 · HTML: https://mlsanity.com/paper/2501.16817/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 2 of 2)

- [COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.02352/) (2021)
- [Physics-informed appliance signatures generator for energy disaggregation](https://mlsanity.com/paper/2401.01828/) (2024)

## Built on by (most cited first, 1 of 1)

- [Fusion-ResNet: A Lightweight multi-label NILM Model Using PCA-ICA Feature Fusion](https://mlsanity.com/paper/2511.12139/) (2025)

## Abstract (arXiv)

In this paper, a novel neural network architecture is proposed to address the challenges in energy disaggregation algorithms. These challenges include the limited availability of data and the complexity of disaggregating a large number of appliances operating simultaneously. The proposed model utilizes independent component analysis as the backbone of the neural network and is evaluated using the F1-score for varying numbers of appliances working concurrently. Our results demonstrate that the model is less prone to overfitting, exhibits low complexity, and effectively decomposes signals with many individual components. Furthermore, we show that the proposed model outperforms existing algorithms when applied to real-world data.