Identification residential appliance using NIALM

Abstract

Smart meters are required to identify home appliances to fulfill various tasks in the smart grid environment. On the other hand, techniques using non-intrusive appliance load monitoring (NIALM) are yet to result in meaningful practical implementation. Our experimental setup, on the recommended specifications of the internal electrical wiring in Indian residences, used common household appliances' load signatures of active and reactive powers, harmonic components and their magnitudes. We have introduced a new approach of ‘multi point sensing’ and ‘group control’ rather than the ‘single point sensing’ and ‘individual control’, used so far in NIALM techniques. The disaggregation system based on Central Public Works Department of India (CPWD). One feature i.e. amplitude of first 8 odd harmonics of current signature of home appliances were selected for the classification. Further principle component analysis (PCA) is used. A comparison between these classification algorithms has been done and it is revealed that artificial neural network classifier and Bayes classifier provides 99.18% and 98.08% accuracy respectively for the experimental data.