{"title":"聚类家庭用电概况","authors":"John R. Williams","doi":"10.1145/2542652.2542656","DOIUrl":null,"url":null,"abstract":"An attempt was made to cluster the load profiles of a sample (n ≈ 380) of New Zealand households. An extensive range of approaches was evaluated, including the approach of clustering on \"features\" of the data rather than the raw data. A semi-automatic search of the problem space (cluster base, distance measure, cluster/partitioning method and k) resulted in a k = 3-cluster solution with acceptable quality indices and face validity. Although a particular combination of base, distance metric and clustering method was found to work well in this case, it is the practice of searching the problem space, rather than a particular solution, that is discussed and advocated.","PeriodicalId":248909,"journal":{"name":"MLSDA '13","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Clustering Household Electricity Use Profiles\",\"authors\":\"John R. Williams\",\"doi\":\"10.1145/2542652.2542656\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An attempt was made to cluster the load profiles of a sample (n ≈ 380) of New Zealand households. An extensive range of approaches was evaluated, including the approach of clustering on \\\"features\\\" of the data rather than the raw data. A semi-automatic search of the problem space (cluster base, distance measure, cluster/partitioning method and k) resulted in a k = 3-cluster solution with acceptable quality indices and face validity. Although a particular combination of base, distance metric and clustering method was found to work well in this case, it is the practice of searching the problem space, rather than a particular solution, that is discussed and advocated.\",\"PeriodicalId\":248909,\"journal\":{\"name\":\"MLSDA '13\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MLSDA '13\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2542652.2542656\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MLSDA '13","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2542652.2542656","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An attempt was made to cluster the load profiles of a sample (n ≈ 380) of New Zealand households. An extensive range of approaches was evaluated, including the approach of clustering on "features" of the data rather than the raw data. A semi-automatic search of the problem space (cluster base, distance measure, cluster/partitioning method and k) resulted in a k = 3-cluster solution with acceptable quality indices and face validity. Although a particular combination of base, distance metric and clustering method was found to work well in this case, it is the practice of searching the problem space, rather than a particular solution, that is discussed and advocated.