# Transfer Learning for Non-Intrusive Load Monitoring

arXiv: https://arxiv.org/abs/1902.08835 · HTML: https://mlsanity.com/paper/1902.08835/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2019.

## Built on by (most cited first, 14 of 14)

- [FedNILM: Applying Federated Learning to NILM Applications at the Edge](https://mlsanity.com/paper/2106.07751/) (2021)
- [Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2207.02009/) (2022)
- [Energy Disaggregation using Variational Autoencoders](https://mlsanity.com/paper/2103.12177/) (2021)
- [FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning](https://mlsanity.com/paper/2108.03591/) (2021)
- [Learning Task-Aware Energy Disaggregation: a Federated Approach](https://mlsanity.com/paper/2204.06767/) (2022)
- [ALARM: Automated MLLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty Quantification](https://mlsanity.com/paper/2512.03101/) (2025)
- [DP2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring](https://mlsanity.com/paper/2207.00041/) (2022)
- [Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances](https://mlsanity.com/paper/2307.09244/) (2023)
- [Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2505.06330/) (2025)
- [Lightweight and Scalable Transfer Learning Framework for Load Disaggregation](https://mlsanity.com/paper/2603.04998/) (2026)
- [Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS](https://mlsanity.com/paper/2204.02802/) (2022)
- [Energy Disaggregation & Appliance Identification in a Smart Home: Transfer Learning enables Edge Computing](https://mlsanity.com/paper/2301.03018/) (2023)
- [Preventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters](https://mlsanity.com/paper/2412.16893/) (2024)
- [Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation](https://mlsanity.com/paper/2506.20525/) (2025)

## Abstract (arXiv)

Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various methods incorporating domain knowledge into NILM have been proposed and shown effective experimentally. Recently, among these methods, deep neural networks are shown performing best. Arguably, the recently proposed sequence-to-point (seq2point) learning is promising for NILM. However, the results were only carried out on the same data domain. It is not clear if the method could be generalised or transferred to different domains, e.g., the test data were drawn from a different country comparing to the training data. We address this issue in the paper, and two transfer learning schemes are proposed, i.e., appliance transfer learning (ATL) and cross-domain transfer learning (CTL). For ATL, our results show that the latent features learnt by a `complex' appliance, e.g., washing machine, can be transferred to a `simple' appliance, e.g., kettle. For CTL, our conclusion is that the seq2point learning is transferable. Precisely, when the training and test data are in a similar domain, seq2point learning can be directly applied to the test data without fine tuning; when the training and test data are in different domains, seq2point learning needs fine tuning before applying to the test data. Interestingly, we show that only the fully connected layers need fine tuning for transfer learning. Source code can be found at https://github.com/MingjunZhong/transferNILM.