# Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation

arXiv: https://arxiv.org/abs/2506.20525 · HTML: https://mlsanity.com/paper/2506.20525/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [Data Augmentation techniques in time series domain: A survey and taxonomy](https://mlsanity.com/paper/2206.13508/) (2022)
- [Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning](https://mlsanity.com/paper/2011.06296/) (2020)
- [Transfer Learning for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/1902.08835/) (2019)
- [Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise](https://mlsanity.com/paper/2105.00349/) (2021)
- [Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation](https://mlsanity.com/paper/2001.07708/) (2020)
- [Data augmentation for dealing with low sampling rates in NILM](https://mlsanity.com/paper/2104.02055/) (2021)
- [Federated Loss Exploration for Improved Convergence on Non-IID Data](https://mlsanity.com/paper/2506.18640/) (2025)
- [A Comprehensive Review on the NILM Algorithms for Energy Disaggregation](https://mlsanity.com/paper/2102.12578/) (2021)
- [A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning](https://mlsanity.com/paper/2503.11469/) (2025)

## Abstract (arXiv)

Industrial Non-Intrusive Load Monitoring (NILM) is limited by the scarcity of high-quality datasets and the complex variability of industrial energy consumption patterns. To address data scarcity and privacy issues, we introduce the Synthetic Industrial Dataset for Energy Disaggregation (SIDED), an open-source dataset generated using Digital Twin simulations. SIDED includes three types of industrial facilities across three different geographic locations, capturing diverse appliance behaviors, weather conditions, and load profiles. We also propose the Appliance-Modulated Data Augmentation (AMDA) method, a computationally efficient technique that enhances NILM model generalization by intelligently scaling appliance power contributions based on their relative impact. We show in experiments that NILM models trained with AMDA-augmented data significantly improve the disaggregation of energy consumption of complex industrial appliances like combined heat and power systems. Specifically, in our out-of-sample scenarios, models trained with AMDA achieved a Normalized Disaggregation Error of 0.093, outperforming models trained without data augmentation (0.451) and those trained with random data augmentation (0.290). Data distribution analyses confirm that AMDA effectively aligns training and test data distributions, enhancing model generalization.