Results 21 to 30 of about 3,621 (255)
Thresholding methods in non-intrusive load monitoring
The Journal of Supercomputing, 2023 AbstractNon-intrusive load monitoring (NILM) is the problem of predicting the status or consumption of individual domestic appliances only from the knowledge of the aggregated power load. NILM is often formulated as a classification (ON/OFF) problem for each device.
Precioso Garcelán, Daniel +1 more
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Deep Sparse Coding for Non–Intrusive Load Monitoring [PDF]
IEEE Transactions on Smart Grid, 2018 Final version accepted at IEEE Transactions on ...
Shikha Singh, Angshul Majumdar
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Scattering Transform for Classification in Non-Intrusive Load Monitoring
Energies, 2021 Nonintrusive Load Monitoring (NILM) uses computational methods to disaggregate and classify electrical appliances signals. The classification is usually based on the power signatures of the appliances obtained by a feature extractor.
Everton Luiz de Aguiar +3 more
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Deep Learning Application to Non-Intrusive Load Monitoring [PDF]
2019 IEEE Milan PowerTech, 2019 This work was partially supported by the Spanish Ministry of Economy and Competitivity under Grant MINECO-17-ENE2016-77919-R (CONCIALIATOR Energy conversion technologies in resilient hybrid AC/DC distribution networks).
Linh, N. V., Arboleya Arboleya, Pablo
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Sensors, 2021 Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management.
Yu Liu +5 more
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IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings
Sensors, 2020 Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development.
Mohamed Aymane Ahajjam +3 more
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Transfer Learning for Non-Intrusive Load Monitoring [PDF]
IEEE Transactions on Smart Grid, 2020 Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique.
Michele D'Incecco +2 more
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Two-stage Non-Intrusive Load Monitoring method for multi-state loads. [PDF]
PLoS OneThe loads that have several working states cannot be accurately distinguished by the conventional Non-Intrusive Load Monitoring (NILM) methods. This paper proposed an improved NILM method based on the Resnet18 Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithm to address the misidentification of multi-state appliances.
Wang L +5 more
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PB-NILM: Pinball Guided Deep Non-Intrusive Load Monitoring
IEEE Access, 2020 The work in this paper proposes the application of the pinball quantile loss function to guide a deep neural network for Non-Intrusive Load Monitoring. The proposed architecture leverages concepts such as Convolution Neural Networks and Recurrent Neural ...
Eduardo Gomes, Lucas Pereira
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Convolutional sequence to sequence non‐intrusive load monitoring
The Journal of Engineering, 2018 A convolutional sequence to sequence non‐intrusive load monitoring model is proposed in this study. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network.
Chen, Kunjin +5 more
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