TY - GEN
T1 - Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads
AU - Chang, Hsueh Hsien
AU - Yang, Hong Tzer
AU - Lin, Ching Lung
PY - 2008/12/1
Y1 - 2008/12/1
N2 - This paper proposes the use of neural network classifiers to evaluate back propagation (BP) and learning vector quantization (LVQ) for feature selection of load identification in a non-intrusive load monitoring (NILM) system. To test the performance of the proposed approach, data sets for electrical loads were analyzed and established using a computer supported program - Electromagnetic Transient Program (EMTP) and onsite load measurement. Load identification techniques were applied in neural networks. The efficiency of load identification and computational requirements was analyzed and compared using BP or LVQ classifiers method. This paper revealed some contributions below. The turn-on transient energy signatures can improve the efficiency of load identification and computational time under multiple operations. The turn-on transient energy has repeatability when used as a power signature to recognize industrial loads in a NILM system. Moreover, the BP classifier is better than the LVQ classifier in the efficiency of load identification and computational requirements.
AB - This paper proposes the use of neural network classifiers to evaluate back propagation (BP) and learning vector quantization (LVQ) for feature selection of load identification in a non-intrusive load monitoring (NILM) system. To test the performance of the proposed approach, data sets for electrical loads were analyzed and established using a computer supported program - Electromagnetic Transient Program (EMTP) and onsite load measurement. Load identification techniques were applied in neural networks. The efficiency of load identification and computational requirements was analyzed and compared using BP or LVQ classifiers method. This paper revealed some contributions below. The turn-on transient energy signatures can improve the efficiency of load identification and computational time under multiple operations. The turn-on transient energy has repeatability when used as a power signature to recognize industrial loads in a NILM system. Moreover, the BP classifier is better than the LVQ classifier in the efficiency of load identification and computational requirements.
UR - http://www.scopus.com/inward/record.url?scp=58349119360&partnerID=8YFLogxK
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U2 - 10.1007/978-3-540-92719-8_60
DO - 10.1007/978-3-540-92719-8_60
M3 - Conference contribution
AN - SCOPUS:58349119360
SN - 3540927182
SN - 9783540927181
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 664
EP - 674
BT - Computer Supported Cooperative Work in Design IV - 11th International Conference, CSCWD 2007, Revised Selected Papers
T2 - 11th International Conference on Computer Supported Cooperative Work in Design IV, CSCWD 2007
Y2 - 26 April 2007 through 28 April 2007
ER -