{"title":"综合模型在建筑行业节能评价中的应用","authors":"O. Olanrewaju","doi":"10.7166/32-2-2321","DOIUrl":null,"url":null,"abstract":"The activities of the building and construction industry have made it one of the highest energy consumers and thus one of the highest emitters of greenhouse gases. The main objective of this study was to develop a system to determine energy saving in the industry. This was achieved through an integrated model of index decomposition analysis, an artificial neural network, and data envelopment analysis. Index decomposition analysis is used to understand the contribution of the factors responsible for energy consumption. These factors are inputs to the artificial neural network to predict the baseline energy consumption. The energy saving is finally determined through data envelopment analysis. The results showed that the integrated model presents a reasonable amount of energy saving in the building and construction industry.","PeriodicalId":404746,"journal":{"name":"The South African Journal of Industrial Engineering","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"APPLICATION OF AN INTEGRATED MODEL TO A CONSTRUCTION AND BUILDING INDUSTRY FOR ENERGY- SAVING ASSESSMENT\",\"authors\":\"O. Olanrewaju\",\"doi\":\"10.7166/32-2-2321\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The activities of the building and construction industry have made it one of the highest energy consumers and thus one of the highest emitters of greenhouse gases. The main objective of this study was to develop a system to determine energy saving in the industry. This was achieved through an integrated model of index decomposition analysis, an artificial neural network, and data envelopment analysis. Index decomposition analysis is used to understand the contribution of the factors responsible for energy consumption. These factors are inputs to the artificial neural network to predict the baseline energy consumption. The energy saving is finally determined through data envelopment analysis. The results showed that the integrated model presents a reasonable amount of energy saving in the building and construction industry.\",\"PeriodicalId\":404746,\"journal\":{\"name\":\"The South African Journal of Industrial Engineering\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The South African Journal of Industrial Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7166/32-2-2321\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The South African Journal of Industrial Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7166/32-2-2321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
APPLICATION OF AN INTEGRATED MODEL TO A CONSTRUCTION AND BUILDING INDUSTRY FOR ENERGY- SAVING ASSESSMENT
The activities of the building and construction industry have made it one of the highest energy consumers and thus one of the highest emitters of greenhouse gases. The main objective of this study was to develop a system to determine energy saving in the industry. This was achieved through an integrated model of index decomposition analysis, an artificial neural network, and data envelopment analysis. Index decomposition analysis is used to understand the contribution of the factors responsible for energy consumption. These factors are inputs to the artificial neural network to predict the baseline energy consumption. The energy saving is finally determined through data envelopment analysis. The results showed that the integrated model presents a reasonable amount of energy saving in the building and construction industry.