# Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review

arXiv: https://arxiv.org/abs/2306.05017 · HTML: https://mlsanity.com/paper/2306.05017/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [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)
- [Electricity Price Forecasting Model based on Gated Recurrent Units](https://mlsanity.com/paper/2207.14225/) (2022)

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

- [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)

Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics.