Publication:
Estimating residential buildings� energy usage utilising a combination of Teaching�Learning�Based Optimization (TLBO) method with conventional prediction techniques

Simple item page
dc.citedby4
dc.contributor.authorZheng S.en_US
dc.contributor.authorXu H.en_US
dc.contributor.authorMukhtar A.en_US
dc.contributor.authorHizam Md Yasir A.S.en_US
dc.contributor.authorKhalilpoor N.en_US
dc.contributor.authorid57169261400en_US
dc.contributor.authorid58677718100en_US
dc.contributor.authorid57195426549en_US
dc.contributor.authorid58677017600en_US
dc.contributor.authorid56397128000en_US
dc.date.accessioned2024-10-14T03:19:51Z
dc.date.available2024-10-14T03:19:51Z
dc.date.issued2023
dc.description.abstractAmong the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R2 (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R 2 value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R2 (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings� cooling load. � 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2276347
dc.identifier.doi10.1080/19942060.2023.2276347
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85175577394
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85175577394&doi=10.1080%2f19942060.2023.2276347&partnerID=40&md5=84bab5e50e382a64ef47edd2099075f2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34447
dc.identifier.volume17
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.subjectadaptive neuro-fuzzy inference system (ANFIS)
dc.subjectArtificial neural network (ANN)
dc.subjectcooling-load
dc.subjectresidential buildings
dc.subjectteaching-learning-based optimization (TLBO)
dc.titleEstimating residential buildings� energy usage utilising a combination of Teaching�Learning�Based Optimization (TLBO) method with conventional prediction techniquesen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS