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

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dc.contributor.author Choksi, Kushan Ajay
dc.contributor.author Jain, Sonal
dc.contributor.author Pindoriya, Naran M.
dc.date.accessioned 2020-04-07T09:58:45Z
dc.date.available 2020-04-07T09:58:45Z
dc.date.issued 2020-06
dc.identifier.citation Choksi, Kushan Ajay; Jain, Sonal and Pindoriya, Naran M., "Feature based clustering technique for investigation of domestic load profiles and probabilistic variation assessment: smart meter dataset", Sustainable Energy, Grids and Networks, DOI: 10.1016/j.segan.2020.100346, vol. 22, Jun. 2020. en_US
dc.identifier.issn 2352-4677
dc.identifier.uri http://dx.doi.org/10.1016/j.segan.2020.100346
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/5281
dc.description.abstract 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.
dc.description.statementofresponsibility by Kushan Ajay Choksi, Sonal Jain and Naran M.Pindoriya
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.subject Clustering approach en_US
dc.subject Feature selection en_US
dc.subject Load profile segregation en_US
dc.subject Load labeling en_US
dc.subject Profile characterization en_US
dc.subject Peak probability analysis en_US
dc.subject State transition probability en_US
dc.title Feature based clustering technique for investigation of domestic load profiles and probabilistic variation assessment: smart meter dataset en_US
dc.type Article en_US
dc.relation.journal Sustainable Energy, Grids and Networks


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