Oguz, ErayTekdemir, İbrahim GürsuGozel, Tuba2024-06-072024-06-0720220948-79211432-0487https://hdl.handle.net/20.500.12885/2248Residential consumers have a significant share in total energy demand today. Demand-side management is a collection of processes which makes providing large amounts of energy less problematic. Identifying demand characteristics of energy consumers is a remarkable part of this process. Data clustering methods have recently been proposed as beneficial tools at that point. In this study, a novel parametric representation of residential energy consumption data is proposed. For that purpose, eleven specific parameters are proposed first for extraction of features in data. Next, principal component analysis is used for dimension reduction. Finally, k-means algorithm is applied for clustering. Two residential energy consumption datasets are used for validation. Analyses are carried out in MATLAB and R. Data clustering is realized on a monthly basis by using daily load curves and clustering performance is compared with another study. It is found that the proposed approach leads to the formation of meaningful clusters of residential consumers. It is also possible to observe demand tendency on a daily basis since daily consumption data is used during the process. Performance evaluation scores show that energy consumption data fit better into clusters when it is compared with another study in the literature.eninfo:eu-repo/semantics/closedAccessDemand-side managementResidential energy consumptionData clusteringIdentification of energy demand characteristicsarticle10.1007/s00202-022-01681-7WOS:000884155700001Q3Q2