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A Convolutional neural network and sequence-to- sequence mo..:

, In: 22nd International Symposium on High Voltage Engineering (ISH 2021),

Lian, W. ; Wu, T. ; He, Y... - p. None , 2021

Link: https://doi.org/10.1049/icp.2022.0242

RT 
T1 <cTitle>22nd International Symposium on High Voltage Engineering (ISH 2021)</cTitle>: T1 <Title>A Convolutional neural network and sequence-to- sequence model based energy disaggregation algorithm for non-intrusive load monitoring</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-9800622&Exemplar=1&LAN=DE
				
A1 Lian, W.
A1 Wu, T.
A1 He, Y.
A1 Shan, Z.
A1 Si, G.
YR 2021
K1 building management systems
K1 computational complexity
K1 domestic appliances
K1 energy consumption
K1 learning (artificial intelligence)
K1 power consumption
K1 convolutional neural nets
K1 load monitoring
K1 sequence-to-sequence model
K1 nonintrusive load monitoring
K1 communication network
K1 smart monitoring
K1 computing equipment
K1 electricity usage data
K1 load features
K1 artificial intelligence
K1 machine learning algorithms
K1 appliance-level energy consumption information
K1 energy disaggregation algorithm
K1 long source sequence
K1 convolutional neural network
K1 sampling frequency
K1 CNN
K1 Seq2Seq model
K1 energy consumption information
K1 local attention mechanism
K1 LSTM method
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.0242
DO https://doi.org/10.1049/icp.2022.0242
SF ELIB - SuUB Bremen

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