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UNet-NILM: A Deep Neural Network for Multi-tasks Appliances State Detection and Power Estimation in NILM

Authors:

Anthony Faustine,

Hafsa Bousbiat,

Shridhar KulkarniAuthors Info & Claims

NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring

Pages 84 - 88

https://doi.org/10.1145/3427771.3427859

Published: 18 November 2020 Publication History

Abstract

Over the years, an enormous amount of research has been exploring Deep Neural Networks (DNN), particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for estimating the energy consumption of appliances from a single point source such as smart meters - Non-Intrusive Load Monitoring (NILM). However, most of the existing DNNs models for NILM use a single-task learning approach in which a neural network is trained exclusively for each appliance. This strategy is computationally expensive and ignores the fact that multiple appliances can be active simultaneously and dependencies between them. In this work, we propose UNet-NILM for multi-task appliances' state detection and power estimation, applying a multi-label learning strategy and multi-target quantile regression. The UNet-NILM is a one-dimensional CNN based on the U-Net architecture initially proposed for image segmentation. Empirical evaluation on the UK-DALE dataset suggests promising performance against traditional single-task learning.

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Cited By

Zhao MChen SXiong ZBai ZYang Y(2025)Enhancing Nonintrusive Load Monitoring With Targeted Adaptive NetworksIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2025.354166574(1-13)Online publication date: 2025
https://doi.org/10.1109/TIM.2025.3541665
Jung GKim GAhn S(2025)Sub-Nyquist Harmonic Current Component Extraction using Band Pass Filters for NILMInternational Journal of Precision Engineering and Manufacturing-Green Technology10.1007/s40684-024-00691-zOnline publication date: 7-Feb-2025
https://doi.org/10.1007/s40684-024-00691-z
Ramesh Palakurti N(2024)Computational Biology and Chemistry with AI and MLInternational Journal of Research in Medical Sciences and Technology10.37648/ijrmst.v17i01.00617:1(29-39)Online publication date: 2024
https://doi.org/10.37648/ijrmst.v17i01.006
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Index Terms

UNet-NILM: A Deep Neural Network for Multi-tasks Appliances State Detection and Power Estimation in NILM
1. Computing methodologies
  1. Machine learning

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NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring

November 2020

109 pages

ISBN:9781450381918

DOI:10.1145/3427771

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 November 2020

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BuildSys '20: The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 18, 2020

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Cited By

Zhao MChen SXiong ZBai ZYang Y(2025)Enhancing Nonintrusive Load Monitoring With Targeted Adaptive NetworksIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2025.354166574(1-13)Online publication date: 2025
https://doi.org/10.1109/TIM.2025.3541665
Jung GKim GAhn S(2025)Sub-Nyquist Harmonic Current Component Extraction using Band Pass Filters for NILMInternational Journal of Precision Engineering and Manufacturing-Green Technology10.1007/s40684-024-00691-zOnline publication date: 7-Feb-2025
https://doi.org/10.1007/s40684-024-00691-z
Ramesh Palakurti N(2024)Computational Biology and Chemistry with AI and MLInternational Journal of Research in Medical Sciences and Technology10.37648/ijrmst.v17i01.00617:1(29-39)Online publication date: 2024
https://doi.org/10.37648/ijrmst.v17i01.006
Criado-Ramón DRuiz LIruela JPegalajar M(2024)A Novel Non-Intrusive Load Monitoring Algorithm for Unsupervised Disaggregation of Household AppliancesInformation10.3390/info1502008715:2(87)Online publication date: 5-Feb-2024
https://doi.org/10.3390/info15020087
Laceda Taloma RComminiello DPisani PCuomo F(2024)UNet-WD: Deep Learning for Multi-Appliance Water Disaggregation2024 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking62109.2024.10619811(702-707)Online publication date: 3-Jun-2024
https://doi.org/10.23919/IFIPNetworking62109.2024.10619811
Xia PZhou HYang TZhou WLiu ZWang XLi X(2024)ESATED: Leveraging Extra-weak Supervision with Auxiliary Task for Enhanced Non-intrusiveness in Energy DisaggregationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997298:4(1-32)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699729
Wang ZZhang XWang YPeng XXu ZShu YLiu JTan RHe YChen J(2024)Hawk: An Efficient NALM System for Accurate Low-Power Appliance RecognitionProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699359(578-591)Online publication date: 4-Nov-2024
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Zhou KZhang ZLu X(2024)Non-Intrusive Load Monitoring Based on an Efficient Deep Learning Model With Local Feature ExtractionIEEE Transactions on Industrial Informatics10.1109/TII.2024.338352120:7(9497-9507)Online publication date: Jul-2024
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Xiong JHong TZhao DZhang Y(2024)MATNilm: Multi-Appliance-Task Non-Intrusive Load Monitoring With Limited Labeled DataIEEE Transactions on Industrial Informatics10.1109/TII.2023.330102620:3(3177-3187)Online publication date: Mar-2024
https://doi.org/10.1109/TII.2023.3301026
Laceda Taloma RPisani PCuomo F(2024)MANTRA: a Multi-Appliance Transformer for Non-Intrusive Load Monitoring2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)10.1109/SmartGridComm60555.2024.10738029(432-437)Online publication date: 17-Sep-2024
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