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Predicting energy use in construction using Extreme Gradient Boosting

Annual increases in global energy consumption are an unavoidable consequence of a growing global economy and population. Among different sectors, the construction industry consumes an average of 20.1% of the world’s total energy. Therefore, exploring methods for estimating the amount of energy used...

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Detalles Bibliográficos
Autores principales: Han, Jiaming, Shu, Kunxin, Wang, Zhenyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496006/
https://www.ncbi.nlm.nih.gov/pubmed/37705620
http://dx.doi.org/10.7717/peerj-cs.1500
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author Han, Jiaming
Shu, Kunxin
Wang, Zhenyu
author_facet Han, Jiaming
Shu, Kunxin
Wang, Zhenyu
author_sort Han, Jiaming
collection PubMed
description Annual increases in global energy consumption are an unavoidable consequence of a growing global economy and population. Among different sectors, the construction industry consumes an average of 20.1% of the world’s total energy. Therefore, exploring methods for estimating the amount of energy used is critical. There are several approaches that have been developed to address this issue. The proposed methods are expected to contribute to energy savings as well as reduce the risks of global warming. There are diverse types of computational approaches to predicting energy use. These existing approaches belong to the statistics-based, engineering-based, and machine learning-based categories. Machine learning-based frameworks showed better performance compared to these other approaches. In our study, we proposed using Extreme Gradient Boosting (XGB), a tree-based ensemble learning algorithm, to tackle the issue. We used a dataset containing energy consumption hourly recorded in an office building in Shanghai, China, from January 1, 2015, to December 31, 2016. The experimental results demonstrated that the XGB model developed using both historical and date features worked better than those developed using only one type of feature. The best-performing model achieved RMSE and MAPE values of 109.00 and 0.24, respectively.
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spelling pubmed-104960062023-09-13 Predicting energy use in construction using Extreme Gradient Boosting Han, Jiaming Shu, Kunxin Wang, Zhenyu PeerJ Comput Sci Artificial Intelligence Annual increases in global energy consumption are an unavoidable consequence of a growing global economy and population. Among different sectors, the construction industry consumes an average of 20.1% of the world’s total energy. Therefore, exploring methods for estimating the amount of energy used is critical. There are several approaches that have been developed to address this issue. The proposed methods are expected to contribute to energy savings as well as reduce the risks of global warming. There are diverse types of computational approaches to predicting energy use. These existing approaches belong to the statistics-based, engineering-based, and machine learning-based categories. Machine learning-based frameworks showed better performance compared to these other approaches. In our study, we proposed using Extreme Gradient Boosting (XGB), a tree-based ensemble learning algorithm, to tackle the issue. We used a dataset containing energy consumption hourly recorded in an office building in Shanghai, China, from January 1, 2015, to December 31, 2016. The experimental results demonstrated that the XGB model developed using both historical and date features worked better than those developed using only one type of feature. The best-performing model achieved RMSE and MAPE values of 109.00 and 0.24, respectively. PeerJ Inc. 2023-08-07 /pmc/articles/PMC10496006/ /pubmed/37705620 http://dx.doi.org/10.7717/peerj-cs.1500 Text en © 2023 Han et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Han, Jiaming
Shu, Kunxin
Wang, Zhenyu
Predicting energy use in construction using Extreme Gradient Boosting
title Predicting energy use in construction using Extreme Gradient Boosting
title_full Predicting energy use in construction using Extreme Gradient Boosting
title_fullStr Predicting energy use in construction using Extreme Gradient Boosting
title_full_unstemmed Predicting energy use in construction using Extreme Gradient Boosting
title_short Predicting energy use in construction using Extreme Gradient Boosting
title_sort predicting energy use in construction using extreme gradient boosting
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496006/
https://www.ncbi.nlm.nih.gov/pubmed/37705620
http://dx.doi.org/10.7717/peerj-cs.1500
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