Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering

Widespread inculcation of smart meter data in modern grid is motivating stakeholders to utilize it for demand response management and achieving energy sustainability goals. One of the methods being used in this regard is Non-Intrusive Load Monitoring (NILM); for disaggregating individual devices from a combined load profile. This study combines two spectral clustering strategies using voting-based consensus clustering technique in such a way as to achieve the benefits of both parent strategies. The voters in the consensus are taken to be the solutions proposed by Spectral Cluster-Mean (SC-M) and Spectral Cluster-Eigen Vector (SC-EV) algorithms with different window sizes to achieve diversity. Currently, Spectral Clustering for NILM has been used by few research works and so far, no one technique has achieved higher accuracy in detecting various kinds of devices. The proposed strategy was evaluated on real world data set (REFIT). The results have shown enhanced overall performance by up to 6%. An in-depth analysis of various tuning parameters of SC-M and SC-EV is also presented. These novel contributions increase the feasibility of using spectral clustering and voting based consensus clustering for NILM and may open further avenues of research in this direction.