# COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring

arXiv: https://arxiv.org/abs/2106.02352 · HTML: https://mlsanity.com/paper/2106.02352/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [A System for Massively Parallel Hyperparameter Tuning](https://mlsanity.com/paper/1810.05934/) (2018)
- [Approximating Continuous Functions by ReLU Nets of Minimal Width](https://mlsanity.com/paper/1710.11278/) (2017)
- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)

## Built on by (most cited first, 5 of 5)

- [Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2306.05012/) (2023)
- [Enhancing Non-Intrusive Load Monitoring with Features Extracted by Independent Component Analysis](https://mlsanity.com/paper/2501.16817/) (2025)
- [Towards a Deeper Understanding of Transformer for Residential Non-intrusive Load Monitoring](https://mlsanity.com/paper/2410.03758/) (2024)
- [Fusion-ResNet: A Lightweight multi-label NILM Model Using PCA-ICA Feature Fusion](https://mlsanity.com/paper/2511.12139/) (2025)
- [Real Time NILM Based Power Monitoring of Identical Induction Motors Representing Cutting Machines in Textile Industry](https://mlsanity.com/paper/2601.01616/) (2026)

## Abstract (arXiv)

The global effort toward renewable energy and the electrification of energy-intensive sectors have significantly increased the demand for electricity, making energy efficiency a critical focus. Non-intrusive load monitoring (NILM) enables detailed analyses of household electricity usage by disaggregating the total power consumption into individual appliance-level data. In this paper, we propose COLD (Concurrent Loads Disaggregator), a transformer-based model specifically designed to address the challenges of disaggregating high-frequency data with multiple simultaneously working devices. COLD supports up to 42 devices and accurately handles scenarios with up to 11 concurrent loads, achieving 95% load identification accuracy and 82% disaggregation performance on the test data. In addition, we introduce a new fully labeled high-frequency NILM dataset for load disaggregation derived from the UK-DALE 16 kHz dataset. Finally, we analyze the decline in NILM model performance as the number of concurrent loads increases.