# Subtask Gated Networks for Non-Intrusive Load Monitoring

arXiv: https://arxiv.org/abs/1811.06692 · HTML: https://mlsanity.com/paper/1811.06692/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2018.

## 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)
- [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)
- [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)
- [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)
- [An Initialization Schema for Neuronal Networks on Tabular Data](https://mlsanity.com/paper/2311.03996/) (2023)
- [Preventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters](https://mlsanity.com/paper/2412.16893/) (2024)
- [Ask for Any Appliance: A Prompt-Programmable Foundation Model for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2609.23146/) (2026)

## Abstract (arXiv)

Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household's aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous household appliances can be avoided, and only one device needs to be installed. The problem has been well-known since Hart's seminal paper in 1992, and recently significant performance improvements have been achieved by adopting deep networks. In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements. Specifically, we propose a subtask gated network that combines the main regression network with an on/off classification subtask network. Unlike typical multitask learning algorithms where multiple tasks simply share the network parameters to take advantage of the relevance among tasks, the subtask gated network multiply the main network's regression output with the subtask's classification probability. When standby-power is additionally learned, the proposed solution surpasses the state-of-the-art performance for most of the benchmark cases. The subtask gated network can be very effective for any problem that inherently has on/off states.