Integrative soft computing approaches for optimizing thermal energy performance in residential buildings.
As is known, early prediction of thermal load in buildings can give valuable insight to engineers and energy experts in order to optimize the building design. Although different machine learning models have been promisingly employed for this problem, newer sophisticated techniques still require prop...
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ftdoajarticles:oai:doaj.org/article:73b500d29edd41caacc1e7c285c00148 2023-10-09T21:50:17+02:00 Integrative soft computing approaches for optimizing thermal energy performance in residential buildings. Yao Peng Yang Chen 2023-01-01T00:00:00Z https://doi.org/10.1371/journal.pone.0290719 https://doaj.org/article/73b500d29edd41caacc1e7c285c00148 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pone.0290719 https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0290719 https://doaj.org/article/73b500d29edd41caacc1e7c285c00148 PLoS ONE, Vol 18, Iss 9, p e0290719 (2023) Medicine R Science Q article 2023 ftdoajarticles https://doi.org/10.1371/journal.pone.0290719 2023-09-17T00:37:58Z As is known, early prediction of thermal load in buildings can give valuable insight to engineers and energy experts in order to optimize the building design. Although different machine learning models have been promisingly employed for this problem, newer sophisticated techniques still require proper attention. This study aims at introducing novel hybrid algorithms for estimating building thermal load. The predictive models are artificial neural networks exposed to five optimizer algorithms, namely Archimedes optimization algorithm (AOA), Beluga whale optimization (BWO), forensic-based investigation (FBI), snake optimizer (SO), and transient search algorithm (TSO), for attaining optimal trainings. These five integrations aim at predicting the annual thermal energy demand. The accuracy of the models is broadly assessed using mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) indicators and a ranking system is accordingly developed. As the MAPE and R2 reported, all obtained relative errors were below 5% and correlations were above 92% which confirm the general acceptability of the results and all used models. While the models exhibited different performances in training and testing stages, referring to the overall results, the BWO emerged as the most accurate algorithm, followed by the AOA and SO simultaneously in the second position, the FBI as the third, and TSO as the fourth accurate model. Mean absolute error (MAPE) and Considering the wide variety of artificial intelligence techniques that are used nowadays, the findings of this research may shed light on the selection of proper techniques for reliable energy performance analysis in complex buildings. Article in Journal/Newspaper Beluga Beluga whale Beluga* Directory of Open Access Journals: DOAJ Articles PLOS ONE 18 9 e0290719 |
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Medicine R Science Q |
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Medicine R Science Q Yao Peng Yang Chen Integrative soft computing approaches for optimizing thermal energy performance in residential buildings. |
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Medicine R Science Q |
description |
As is known, early prediction of thermal load in buildings can give valuable insight to engineers and energy experts in order to optimize the building design. Although different machine learning models have been promisingly employed for this problem, newer sophisticated techniques still require proper attention. This study aims at introducing novel hybrid algorithms for estimating building thermal load. The predictive models are artificial neural networks exposed to five optimizer algorithms, namely Archimedes optimization algorithm (AOA), Beluga whale optimization (BWO), forensic-based investigation (FBI), snake optimizer (SO), and transient search algorithm (TSO), for attaining optimal trainings. These five integrations aim at predicting the annual thermal energy demand. The accuracy of the models is broadly assessed using mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) indicators and a ranking system is accordingly developed. As the MAPE and R2 reported, all obtained relative errors were below 5% and correlations were above 92% which confirm the general acceptability of the results and all used models. While the models exhibited different performances in training and testing stages, referring to the overall results, the BWO emerged as the most accurate algorithm, followed by the AOA and SO simultaneously in the second position, the FBI as the third, and TSO as the fourth accurate model. Mean absolute error (MAPE) and Considering the wide variety of artificial intelligence techniques that are used nowadays, the findings of this research may shed light on the selection of proper techniques for reliable energy performance analysis in complex buildings. |
format |
Article in Journal/Newspaper |
author |
Yao Peng Yang Chen |
author_facet |
Yao Peng Yang Chen |
author_sort |
Yao Peng |
title |
Integrative soft computing approaches for optimizing thermal energy performance in residential buildings. |
title_short |
Integrative soft computing approaches for optimizing thermal energy performance in residential buildings. |
title_full |
Integrative soft computing approaches for optimizing thermal energy performance in residential buildings. |
title_fullStr |
Integrative soft computing approaches for optimizing thermal energy performance in residential buildings. |
title_full_unstemmed |
Integrative soft computing approaches for optimizing thermal energy performance in residential buildings. |
title_sort |
integrative soft computing approaches for optimizing thermal energy performance in residential buildings. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2023 |
url |
https://doi.org/10.1371/journal.pone.0290719 https://doaj.org/article/73b500d29edd41caacc1e7c285c00148 |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
PLoS ONE, Vol 18, Iss 9, p e0290719 (2023) |
op_relation |
https://doi.org/10.1371/journal.pone.0290719 https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0290719 https://doaj.org/article/73b500d29edd41caacc1e7c285c00148 |
op_doi |
https://doi.org/10.1371/journal.pone.0290719 |
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PLOS ONE |
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18 |
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