Energy-Efficiency Model for Residential Buildings Using Supervised Machine Learning Algorithm
DC Field | Value | Language |
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dc.contributor.author | Aslam, M.S. | - |
dc.contributor.author | Ghazal, T.M. | - |
dc.contributor.author | Fatima, A. | - |
dc.contributor.author | Said, R.A. | - |
dc.contributor.author | Abbas, S. | - |
dc.contributor.author | Khan, M.A. | - |
dc.contributor.author | Siddiqui, S.Y. | - |
dc.contributor.author | Ahmad, M. | - |
dc.date.accessioned | 2021-09-09T00:40:40Z | - |
dc.date.available | 2021-09-09T00:40:40Z | - |
dc.date.created | 2021-09-07 | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 1079-8587 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82092 | - |
dc.description.abstract | The real-time management and control of heating-system networks in residential buildings has tremendous energy-saving potential, and accurate load prediction is the basis for system monitoring. In this regard, selecting the appro-priate input parameters is the key to accurate heating-load forecasting. In existing models for forecasting heating loads and selecting input parameters, with an increase in the length of the prediction cycle, the heating-load rate gradually decreases, and the influence of the outside temperature gradually increases. In view of different types of solutions for improving buildings’ energy efficiency, this study proposed a Energy-efficiency model for residential buildings based on gradient descent optimization (E2B-GDO). This model can predict a building’s heating-load conservation based on a building energy performance dataset. The input layer includes area (distribution of the glazing area, wall area, and surface area), relative density, and overall elevation. The proposed E2B-GDO model achieved an accuracy of 99.98% for training and 98.00% for validation. © 2021, Tech Science Press. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | TSI PRESS | - |
dc.relation.isPartOf | Intelligent Automation and Soft Computing | - |
dc.title | Energy-Efficiency Model for Residential Buildings Using Supervised Machine Learning Algorithm | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000689388400011 | - |
dc.identifier.doi | 10.32604/iasc.2021.017920 | - |
dc.identifier.bibliographicCitation | Intelligent Automation and Soft Computing, v.30, no.3, pp.881 - 888 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85113908784 | - |
dc.citation.endPage | 888 | - |
dc.citation.startPage | 881 | - |
dc.citation.title | Intelligent Automation and Soft Computing | - |
dc.citation.volume | 30 | - |
dc.citation.number | 3 | - |
dc.contributor.affiliatedAuthor | Khan, M.A. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Gradient descent optimization | - |
dc.subject.keywordAuthor | Heating-load prediction | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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