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Electrical load prediction of healthcare buildings through single and ensemble learning

Healthcare buildings are characterized by complex energy systems and high energy usage, therefore serving as the key areas for achieving energy conservation goals in the building sector. An accurate load prediction of hospital energy consumption is of paramount importance to a successful healthcare...

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Autores principales: Cao, Lingyan, Li, Yongkui, Zhang, Jiansong, Jiang, Yi, Han, Yilong, Wei, Jianjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560125/
http://dx.doi.org/10.1016/j.egyr.2020.10.005
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author Cao, Lingyan
Li, Yongkui
Zhang, Jiansong
Jiang, Yi
Han, Yilong
Wei, Jianjun
author_facet Cao, Lingyan
Li, Yongkui
Zhang, Jiansong
Jiang, Yi
Han, Yilong
Wei, Jianjun
author_sort Cao, Lingyan
collection PubMed
description Healthcare buildings are characterized by complex energy systems and high energy usage, therefore serving as the key areas for achieving energy conservation goals in the building sector. An accurate load prediction of hospital energy consumption is of paramount importance to a successful healthcare building energy management. In this study, eight machine learning models of single learning and ensemble learning were developed for predicting healthcare facilities’ energy consumption. To validate the performance of the proposed model, an experiment was conducted on a general hospital in Shanghai, China. It was found that the two ensemble models, Extreme Gradient Boosting (XGBoost) model and Random Forest (RF) model, outperformed single models in daily electrical load prediction. A further comparison between models trained with daily and weekly temporal resolution electrical data shows that it is more likely to achieve higher accuracy with finer time granularity. Through feature importance analysis, the most influential features under the daily and weekly electrical load prediction were identified. Based on the prediction results, it is expected that hospital facility managers will be able to conveniently assess the expected energy usage of their hospitals with the machine learning models.
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spelling pubmed-75601252020-10-16 Electrical load prediction of healthcare buildings through single and ensemble learning Cao, Lingyan Li, Yongkui Zhang, Jiansong Jiang, Yi Han, Yilong Wei, Jianjun Energy Reports Article Healthcare buildings are characterized by complex energy systems and high energy usage, therefore serving as the key areas for achieving energy conservation goals in the building sector. An accurate load prediction of hospital energy consumption is of paramount importance to a successful healthcare building energy management. In this study, eight machine learning models of single learning and ensemble learning were developed for predicting healthcare facilities’ energy consumption. To validate the performance of the proposed model, an experiment was conducted on a general hospital in Shanghai, China. It was found that the two ensemble models, Extreme Gradient Boosting (XGBoost) model and Random Forest (RF) model, outperformed single models in daily electrical load prediction. A further comparison between models trained with daily and weekly temporal resolution electrical data shows that it is more likely to achieve higher accuracy with finer time granularity. Through feature importance analysis, the most influential features under the daily and weekly electrical load prediction were identified. Based on the prediction results, it is expected that hospital facility managers will be able to conveniently assess the expected energy usage of their hospitals with the machine learning models. The Authors. Published by Elsevier Ltd. 2020-11 2020-10-15 /pmc/articles/PMC7560125/ http://dx.doi.org/10.1016/j.egyr.2020.10.005 Text en © 2020 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Cao, Lingyan
Li, Yongkui
Zhang, Jiansong
Jiang, Yi
Han, Yilong
Wei, Jianjun
Electrical load prediction of healthcare buildings through single and ensemble learning
title Electrical load prediction of healthcare buildings through single and ensemble learning
title_full Electrical load prediction of healthcare buildings through single and ensemble learning
title_fullStr Electrical load prediction of healthcare buildings through single and ensemble learning
title_full_unstemmed Electrical load prediction of healthcare buildings through single and ensemble learning
title_short Electrical load prediction of healthcare buildings through single and ensemble learning
title_sort electrical load prediction of healthcare buildings through single and ensemble learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560125/
http://dx.doi.org/10.1016/j.egyr.2020.10.005
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