research-article

Nonintrusive appliance load monitoring: Review and outlook

Authors:

M. Zeifman,

K. RothAuthors Info & Claims

IEEE Transactions on Consumer Electronics, Volume 57, Issue 1

Pages 76 - 84

https://doi.org/10.1109/TCE.2011.5735484

Published: 01 February 2011 Publication History

Abstract

Consumer systems for home energy management can provide significant energy saving. Such systems may be based on nonintrusive appliance load monitoring (NIALM), in which individual appliance power consumption information is disaggregated from single-point measurements. The disaggregation methods constitute the most important part of NIALM systems. This paper reviews the methodology of consumer systems for NIALM in residential buildings.

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Alonso SMorán APérez DPrada MFuertes JDomínguez M(2024)Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoderIntegrated Computer-Aided Engineering10.3233/ICA-23072831:2(157-172)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/ICA-230728
Akbar MAmayri MBouguila NWurtz FDelinchant B(2023)Assessing the Effectiveness of Supervised and Semi-supervised NILM Approaches in an Industrial ContextProceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems10.1145/3638209.3638211(7-13)Online publication date: 25-Nov-2023
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Shi JLiu JShi SMa XZhang ZNong G(2023)Comparative Research on Non-intrusive Load MonitoringProceedings of the 2023 7th International Conference on High Performance Compilation, Computing and Communications10.1145/3606043.3606077(239-245)Online publication date: 17-Jun-2023
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cover image IEEE Transactions on Consumer Electronics

IEEE Transactions on Consumer Electronics Volume 57, Issue 1

February 2011

279 pages

ISSN:0098-3063

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Publisher

IEEE Press

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Published: 01 February 2011

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Alonso SMorán APérez DPrada MFuertes JDomínguez M(2024)Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoderIntegrated Computer-Aided Engineering10.3233/ICA-23072831:2(157-172)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/ICA-230728
Akbar MAmayri MBouguila NWurtz FDelinchant B(2023)Assessing the Effectiveness of Supervised and Semi-supervised NILM Approaches in an Industrial ContextProceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems10.1145/3638209.3638211(7-13)Online publication date: 25-Nov-2023
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Batic DStankovic VStankovic L(2023)Toward Transparent Load Disaggregation—A Framework for Quantitative Evaluation of Explainability Using Explainable AIIEEE Transactions on Consumer Electronics10.1109/TCE.2023.330053070:1(4345-4356)Online publication date: 1-Aug-2023
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Circuit-Level Load Monitoring for Household Energy Management

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