# Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

arXiv: https://arxiv.org/abs/1507.06594 · HTML: https://mlsanity.com/paper/1507.06594/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2015.

## Built on by (most cited first, 30 of 54)

- [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)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.02352/) (2021)
- [Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection](https://mlsanity.com/paper/2105.11085/) (2021)
- [MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model](https://mlsanity.com/paper/2302.05565/) (2023)
- [Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation](https://mlsanity.com/paper/2203.16537/) (2022)
- [MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data](https://mlsanity.com/paper/2307.14778/) (2023)
- [Non-Intrusive Load Monitoring for Feeder-Level EV Charging Detection: Sliding Window-based Approaches to Offline and Online Detection](https://mlsanity.com/paper/2312.01887/) (2023)
- [NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity](https://mlsanity.com/paper/2506.05880/) (2025)
- [Energy Disaggregation using Variational Autoencoders](https://mlsanity.com/paper/2103.12177/) (2021)
- [A Federated Learning Framework for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2104.01618/) (2021)
- [Data augmentation for dealing with low sampling rates in NILM](https://mlsanity.com/paper/2104.02055/) (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)
- [Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series](https://mlsanity.com/paper/2506.05895/) (2025)
- [A Comprehensive Review on the NILM Algorithms for Energy Disaggregation](https://mlsanity.com/paper/2102.12578/) (2021)
- [Trimming Feature Extraction and Inference for MCU-based Edge NILM: a Systematic Approach](https://mlsanity.com/paper/2105.10302/) (2021)
- [Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.09158/) (2021)
- [DP2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring](https://mlsanity.com/paper/2207.00041/) (2022)
- [Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring](https://mlsanity.com/paper/2208.10638/) (2022)
- [Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review](https://mlsanity.com/paper/2306.05017/) (2023)
- [Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances](https://mlsanity.com/paper/2307.09244/) (2023)
- [Towards Data-Driven Electricity Management: Multi-Region Harmonized Data and Knowledge Graph](https://mlsanity.com/paper/2405.18869/) (2024)
- [Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning](https://mlsanity.com/paper/2405.18888/) (2024)
- [Benchmarking Active Learning for NILM](https://mlsanity.com/paper/2411.15805/) (2024)
- [Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2505.06330/) (2025)
- [Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning](https://mlsanity.com/paper/2508.14600/) (2025)
- [Cluster Aggregated GAN (CAG): A Cluster-Based Hybrid Model for Appliance Pattern Generation](https://mlsanity.com/paper/2512.22287/) (2025)
- [Incorporating Coincidental Water Data into Non-intrusive Load Monitoring](https://mlsanity.com/paper/2101.07190/) (2021)

## Abstract (arXiv)

Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.