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Electricity time series clustering is an effective tool for identifying useful information in various practical applications, including the forecasting of electricity usage, which is important for providing more data to smart meters. This paper presents a comprehensive study of clustering methods for residential electricity demand profiles and further applications focused on the creation of more accurate electricity forecasts for residential customers. The contributions of this paper are threefold: (1) using data from 46 homes in Austin, Texas, the similarity measures from different time series are analyzed; (2) the optimal number of clusters for representing residential electricity use profiles is determined; and (3) an extensive load forecasting study using different segmentation\u2010enhanced forecasting algorithms is undertaken. Finally, from the operator\u2019s perspective, the implications of the results are discussed in terms of the use of clustering methods for grouping electrical load patterns.<\/jats:p>","DOI":"10.1155\/2018\/3683969","type":"journal-article","created":{"date-parts":[[2018,4,26]],"date-time":"2018-04-26T23:30:59Z","timestamp":1524785459000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Simulation Study on Clustering Approaches for Short\u2010Term Electricity Forecasting"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6953-8907","authenticated-orcid":false,"given":"Krzysztof","family":"Gajowniczek","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomasz","family":"Z\u0105bkowski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,4,26]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0174098"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2011.12.031"},{"key":"e_1_2_10_3_2","volume-title":"Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data","author":"Jin L.","year":"2017"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v062.i01"},{"key":"e_1_2_10_5_2","first-page":"455","article-title":"Distance measures for time series in R","volume":"8","author":"Mori U.","year":"2016","journal-title":"The TSdist Package R journal"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2426723"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.4945420"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-28349-8_2"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40745-015-0040-1"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012801612483"},{"key":"e_1_2_10_11_2","unstructured":"Pecanstreet Dataport 2014 https:\/\/dataport.pecanstreet.org\/."},{"key":"e_1_2_10_12_2","unstructured":"YiB.andFaloutsosC. 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