Load Identification in Neural Networks for a Non-intrusive Monitoring of Industrial Electrical Loads

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.