Estimating residential buildings� energy usage utilising a combination of Teaching�Learning�Based Optimization (TLBO) method with conventional prediction techniques
Among 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...
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my.uniten.dspace-344472024-10-14T11:19:51Z Estimating residential buildings� energy usage utilising a combination of Teaching�Learning�Based Optimization (TLBO) method with conventional prediction techniques Zheng S. Xu H. Mukhtar A. Hizam Md Yasir A.S. Khalilpoor N. 57169261400 58677718100 57195426549 58677017600 56397128000 adaptive neuro-fuzzy inference system (ANFIS) Artificial neural network (ANN) cooling-load residential buildings teaching-learning-based optimization (TLBO) Among 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. Final 2024-10-14T03:19:51Z 2024-10-14T03:19:51Z 2023 Article 10.1080/19942060.2023.2276347 2-s2.0-85175577394 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175577394&doi=10.1080%2f19942060.2023.2276347&partnerID=40&md5=84bab5e50e382a64ef47edd2099075f2 https://irepository.uniten.edu.my/handle/123456789/34447 17 1 2276347 All Open Access Gold Open Access Taylor and Francis Ltd. Scopus |
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adaptive neuro-fuzzy inference system (ANFIS) Artificial neural network (ANN) cooling-load residential buildings teaching-learning-based optimization (TLBO) |
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adaptive neuro-fuzzy inference system (ANFIS) Artificial neural network (ANN) cooling-load residential buildings teaching-learning-based optimization (TLBO) Zheng S. Xu H. Mukhtar A. Hizam Md Yasir A.S. Khalilpoor N. Estimating residential buildings� energy usage utilising a combination of Teaching�Learning�Based Optimization (TLBO) method with conventional prediction techniques |
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Among 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. |
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57169261400 |
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57169261400 Zheng S. Xu H. Mukhtar A. Hizam Md Yasir A.S. Khalilpoor N. |
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Article |
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Zheng S. Xu H. Mukhtar A. Hizam Md Yasir A.S. Khalilpoor N. |
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Zheng S. |
title |
Estimating residential buildings� energy usage utilising a combination of Teaching�Learning�Based Optimization (TLBO) method with conventional prediction techniques |
title_short |
Estimating residential buildings� energy usage utilising a combination of Teaching�Learning�Based Optimization (TLBO) method with conventional prediction techniques |
title_full |
Estimating residential buildings� energy usage utilising a combination of Teaching�Learning�Based Optimization (TLBO) method with conventional prediction techniques |
title_fullStr |
Estimating residential buildings� energy usage utilising a combination of Teaching�Learning�Based Optimization (TLBO) method with conventional prediction techniques |
title_full_unstemmed |
Estimating residential buildings� energy usage utilising a combination of Teaching�Learning�Based Optimization (TLBO) method with conventional prediction techniques |
title_sort |
estimating residential buildings� energy usage utilising a combination of teaching�learning�based optimization (tlbo) method with conventional prediction techniques |
publisher |
Taylor and Francis Ltd. |
publishDate |
2024 |
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1814061180998647808 |
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13.209306 |