{"title":"大型公共建筑电力能耗模型研究","authors":"Yuan Ma, Jun-qi Yu, Chuang-ye Yang, Lei Wang","doi":"10.1109/IWISA.2010.5473608","DOIUrl":null,"url":null,"abstract":"Through the analysis of the relevant power energy consumption factors of the large-scale public buildings, the integrated model based on multiple linear regression and self-regression methods is attained, of which the least square algorithm is chosen to make parameters estimation. -test and -test are applied to verify its availability. After power consumption data of large-scale public buildings acquired in Xi'an city, Simulation process is operated by Matlab software. The result reveals that the model above has high accuracy to describe and predict trend of power consumption in the large-scale public buildings quantitatively, meanwhile, it provides an essential part to future energy saving management system.","PeriodicalId":298764,"journal":{"name":"2010 2nd International Workshop on Intelligent Systems and Applications","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"47","resultStr":"{\"title\":\"Study on Power Energy Consumption Model for Large-Scale Public Building\",\"authors\":\"Yuan Ma, Jun-qi Yu, Chuang-ye Yang, Lei Wang\",\"doi\":\"10.1109/IWISA.2010.5473608\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Through the analysis of the relevant power energy consumption factors of the large-scale public buildings, the integrated model based on multiple linear regression and self-regression methods is attained, of which the least square algorithm is chosen to make parameters estimation. -test and -test are applied to verify its availability. After power consumption data of large-scale public buildings acquired in Xi'an city, Simulation process is operated by Matlab software. The result reveals that the model above has high accuracy to describe and predict trend of power consumption in the large-scale public buildings quantitatively, meanwhile, it provides an essential part to future energy saving management system.\",\"PeriodicalId\":298764,\"journal\":{\"name\":\"2010 2nd International Workshop on Intelligent Systems and Applications\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"47\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Workshop on Intelligent Systems and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWISA.2010.5473608\",\"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 2nd International Workshop on Intelligent Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWISA.2010.5473608","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on Power Energy Consumption Model for Large-Scale Public Building
Through the analysis of the relevant power energy consumption factors of the large-scale public buildings, the integrated model based on multiple linear regression and self-regression methods is attained, of which the least square algorithm is chosen to make parameters estimation. -test and -test are applied to verify its availability. After power consumption data of large-scale public buildings acquired in Xi'an city, Simulation process is operated by Matlab software. The result reveals that the model above has high accuracy to describe and predict trend of power consumption in the large-scale public buildings quantitatively, meanwhile, it provides an essential part to future energy saving management system.