DC equipment identification using K means clustering and kNN classification techniques
Detection of steady states and identification of small electrical loads in a household or office grid are important in efficient smart energy management. This paper proposes a method that combines two machine learning techniques, unsupervised K means clustering, and supervised k Nearest Neighbours classification techniques, to train a system that can effectively identify the low voltage DC electrical load, and at the same time detect whether it is in its steady state. This is done by comparing the features extracted from signatures of the electric current waveforms of equipment. The combination of K means and kNN in the initialisation stage removes the need to know all the training elements beforehand, and thus, considerably simplifies the process. In the normal operation stage, kNN was used to identify the new unknown test element to the cluster that has the majority votes from its nearest neighbours. The centroids obtained from the K means clustering aided in the determination of whether the system is in steady state. The method has been successfully implemented on a low voltage DC office grid, with commonly used office equipment.
2016 IEEE Region 10 Conference (TENCON)