# Sequence-to-point learning with neural networks for nonintrusive load monitoring

arXiv: https://arxiv.org/abs/1612.09106 · HTML: https://mlsanity.com/paper/1612.09106/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2016.

## Built on by (most cited first, 30 of 48)

- [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)
- [Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection](https://mlsanity.com/paper/2105.11085/) (2021)
- [QuickSkill: Novice Skill Estimation in Online Multiplayer Games](https://mlsanity.com/paper/2208.07704/) (2022)
- [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)
- [CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System](https://mlsanity.com/paper/2406.19720/) (2024)
- [Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series](https://mlsanity.com/paper/2506.05895/) (2025)
- [Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.09158/) (2021)
- [SpiroMask: Measuring Lung Function Using Consumer-Grade Masks](https://mlsanity.com/paper/2201.09280/) (2022)
- [Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater](https://mlsanity.com/paper/2203.11268/) (2022)
- [DP2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring](https://mlsanity.com/paper/2207.00041/) (2022)
- [Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2306.05012/) (2023)
- [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)
- [Towards Real-world Deployment of NILM Systems: Challenges and Practices](https://mlsanity.com/paper/2409.14821/) (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)
- [Incorporating Coincidental Water Data into Non-intrusive Load Monitoring](https://mlsanity.com/paper/2101.07190/) (2021)
- [A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks](https://mlsanity.com/paper/2104.07809/) (2021)
- [More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.00297/) (2021)
- [Adversarial Energy Disaggregation for Non-intrusive Load Monitoring](https://mlsanity.com/paper/2108.01998/) (2021)

## Abstract (arXiv)

Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a single-channel blind source separation problem, aims to decompose the mains which records the whole house electricity consumption into appliance-wise readings. This problem is difficult because it is inherently unidentifiable. Recent approaches have shown that the identifiability problem could be reduced by introducing domain knowledge into the model. Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in signal processing problems, which raises issues about how to combine predictions from different sliding windows. In this paper, we propose sequence-to-point learning, where the input is a window of the mains and the output is a single point of the target appliance. We use convolutional neural networks to train the model. Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem. We applied the proposed neural network approaches to real-world household energy data, and show that the methods achieve state-of-the-art performance, improving two standard error measures by 84% and 92%.