{"title":"客车短期负荷预测系统","authors":"R. M. Salgado, T. Ohishi, R. Ballini","doi":"10.1109/HIS.2010.5600075","DOIUrl":null,"url":null,"abstract":"This paper proposes a methodology for a short-term bus load forecasting. This approach calculates the short-term bus load demand forecast using few aggregated models. The idea is to cluster the buses in groups with similar daily load profile and for each cluster one bus load forecasting model is adjusted. For each cluster, aggregated forecasting model is built based on the analysis of individual bus load data. The solution obtained through aggregated approach is similar to the solution obtained by individual bus load forecasting model, but requiring much less computational time. This proposed methodology was implemented in a friendly computational forecasting support system described in this paper.","PeriodicalId":174618,"journal":{"name":"2010 10th International Conference on Hybrid Intelligent Systems","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"A short-term bus load forecasting system\",\"authors\":\"R. M. Salgado, T. Ohishi, R. Ballini\",\"doi\":\"10.1109/HIS.2010.5600075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a methodology for a short-term bus load forecasting. This approach calculates the short-term bus load demand forecast using few aggregated models. The idea is to cluster the buses in groups with similar daily load profile and for each cluster one bus load forecasting model is adjusted. For each cluster, aggregated forecasting model is built based on the analysis of individual bus load data. The solution obtained through aggregated approach is similar to the solution obtained by individual bus load forecasting model, but requiring much less computational time. This proposed methodology was implemented in a friendly computational forecasting support system described in this paper.\",\"PeriodicalId\":174618,\"journal\":{\"name\":\"2010 10th International Conference on Hybrid Intelligent Systems\",\"volume\":\"85 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 10th International Conference on Hybrid Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HIS.2010.5600075\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 10th International Conference on Hybrid Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HIS.2010.5600075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper proposes a methodology for a short-term bus load forecasting. This approach calculates the short-term bus load demand forecast using few aggregated models. The idea is to cluster the buses in groups with similar daily load profile and for each cluster one bus load forecasting model is adjusted. For each cluster, aggregated forecasting model is built based on the analysis of individual bus load data. The solution obtained through aggregated approach is similar to the solution obtained by individual bus load forecasting model, but requiring much less computational time. This proposed methodology was implemented in a friendly computational forecasting support system described in this paper.