A new approach for supervised power disaggregation by using a deep recurrent LSTM network

2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Volumn , Issue , 2016, Pages 63-67

  Mauch, Lukas ^a   Yang, Bin ^a  

^a UNIVERSITY OF STUTTGART   (Germany)

Author keywords

deep recurrent neural network (RNN); long short term memory (LSTM); Non intrusive load monitoring (NILM); supervised power disaggregation

Indexed keywords

BRAIN; FEATURE EXTRACTION; INFORMATION SCIENCE; RECURRENT NEURAL NETWORKS; SIGNAL PROCESSING;

DISAGGREGATION; EVENT DETECTION; LONG SHORT TERM MEMORY; MULTIPLE NETWORKS; NONINTRUSIVE LOAD MONITORING; RECURRENT NEURAL NETWORK (RNN); REFERENCE ENERGY; SAMPLING FREQUENCIES;

ELECTRIC LOAD MANAGEMENT;

EID: 84964766310     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/GlobalSIP.2015.7418157     Document Type: Conference Paper

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