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1
Lightweight Non-Intrusive Load Monitoring Employing Pruned ..:
, In:
Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring
,
Barber, Jack
;
Cuayáhuitl, Heriberto
;
Zhong, Mingjun
. - p. 11-15 , 2020
Link:
https://dl.acm.org/doi/10.1145/3427771.3427845
RT T1 <cTitle>Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring</cTitle>: T1 <Title>Lightweight Non-Intrusive Load Monitoring Employing Pruned Sequence-to-Point Learning</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3427845&Exemplar=1&LAN=DE A1 Barber, Jack A1 Cuayáhuitl, Heriberto A1 Zhong, Mingjun A1 Luan, Wenpeng PB ACM YR 2020 K1 Energy disaggregation K1 deep learning K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks K1 Learning paradigms K1 Supervised learning K1 Supervised learning by regression SP 11 OP 15 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3427771.3427845 DO https://dl.acm.org/doi/10.1145/3427771.3427845 SF ELIB - SuUB Bremen
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