# Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring

arXiv: https://arxiv.org/abs/2207.02009 · HTML: https://mlsanity.com/paper/2207.02009/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 139 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 24 of 24)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Explainable Artificial Intelligence: a Systematic Review](https://mlsanity.com/paper/2006.00093/) (2020)
- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [Subtask Gated Networks for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/1811.06692/) (2018)
- [Transfer Learning for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/1902.08835/) (2019)
- [FedNILM: Applying Federated Learning to NILM Applications at the Edge](https://mlsanity.com/paper/2106.07751/) (2021)
- [Convolutional Sequence to Sequence Non-intrusive Load Monitoring](https://mlsanity.com/paper/1806.02078/) (2018)
- [Wavenilm: A causal neural network for power disaggregation from the complex power signal](https://mlsanity.com/paper/1902.08736/) (2019)
- [Non-Intrusive Energy Disaggregation Using Non-negative Matrix Factorization with Sum-to-k Constraint](https://mlsanity.com/paper/1704.07308/) (2017)
- [Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification](https://mlsanity.com/paper/2104.05048/) (2021)
- [Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring](https://mlsanity.com/paper/1911.07183/) (2019)
- [Deep Learning Based Energy Disaggregation and On/Off Detection of Household Appliances](https://mlsanity.com/paper/1908.00941/) (2019)
- [Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation](https://mlsanity.com/paper/2001.07708/) (2020)
- [Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2002.05593/) (2020)
- [NILM as a regression versus classification problem: the importance of thresholding](https://mlsanity.com/paper/2010.16050/) (2020)
- [Neighbourhood NILM: A Big-data Approach to Household Energy Disaggregation](https://mlsanity.com/paper/1511.02900/) (2015)
- [Non-intrusive Load Monitoring via Multi-label Sparse Representation based Classification](https://mlsanity.com/paper/1912.07360/) (2019)
- [Simultaneous Detection of Multiple Appliances from Smart-meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning](https://mlsanity.com/paper/1912.07568/) (2019)
- [Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring](https://mlsanity.com/paper/2006.00250/) (2020)
- [On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring](https://mlsanity.com/paper/1912.06200/) (2019)

## Built on by (most cited first, 7 of 7)

- [Appliance Detection Using Very Low-Frequency Smart Meter Time Series](https://mlsanity.com/paper/2305.10352/) (2023)
- [ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series](https://mlsanity.com/paper/2401.05381/) (2023)
- [NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity](https://mlsanity.com/paper/2506.05880/) (2025)
- [Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series](https://mlsanity.com/paper/2506.05895/) (2025)
- [On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks](https://mlsanity.com/paper/2307.10209/) (2023)
- [Occupancy Detection Based on Electricity Consumption](https://mlsanity.com/paper/2312.08535/) (2023)
- [Learning Defensive Policies against Diverse Inference Attacks for Smart Meter Privacy](https://mlsanity.com/paper/2609.25484/) (2026)

## Abstract (arXiv)

Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. A lot of publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach on the desirable performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. Nowadays, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios. Thus, complexity of the algorithms, transferability, reliability, practicality and in general trustworthiness are the main issues of interest. This review narrows the gap between the early immature NILM era and the mature one. In particular, the paper provides a comprehensive literature review of the NILM methods for residential appliances only. The paper analyzes, summarizes and presents the outcomes of a large number of recently published scholarly articles. Also, the paper discusses the highlights of these methods and introduces the research dilemmas that should be taken into consideration by researchers to apply NILM methods. Finally, we show the need for transferring the traditional disaggregation models into a practical and trustworthy framework.