Feature based clustering technique for investigation of domestic load profiles and probabilistic variation assessment: Smart meter dataset

Dimensions of electrical distribution network datasets have been increasing exponentially as result of global acceptance of toward smart metering projects for secure implementation of demand response strategies and attaining satisfactory operation of electrical distribution network. Traditional approaches of analyzing these datasets have often been prone to losing of important information for instance averaging and aggregating of data; such loss of information can prove imperative in this era of demand side management and demand response. High dimensionality of distribution dataset is prominent factor for popularity of such conventional perspective toward large datasets. However, recent evolution in data mining have tossed various dimensionality reduction techniques expressing minimal loss of information. This paper proposes a feature based clustering algorithm aimed at dimensionality reduction, load profile characterization and probabilistic load variation assessment as a case study for smart village project of Nana Kajaliyala village, Gujarat, India. Proposed algorithm attains profile characterization using classical k-means alongside an empirical feature selection countering high dimensionality. A comparative evaluation of proposed algorithm with other popular techniques like self-organizing map (SOM) and classical k-means is presented in this paper. Moreover, a novel probabilistic analysis approach is conferred, which is directed at assessment of load variation, peak risk analysis of individual consumers. Determined statistical assessment measures in this paper would aid the utility with capability to execute cognitive decision making and reduce aggregate technical and commercial losses. Furthermore, load labels assigned to each characteristic profile could help managing load requirements, and planning future operations.