Estimation of renewable energy and built environment-related variables using neural networks – A review
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/80198 https://doi.org/10.1016/j.rser.2018.05.060 |
Resumo: | This paper presents a review on the application of neural networks for the estimation, forecasting, monitoring, and classification of exogenous environmental variables that affect the performance, salubrity, and security of cities, buildings, and infrastructures. The forecast of these variables allows to explore renewable energy and water resources, to prevent potentially hazardous construction locations, and to find the healthiest places, thus promoting a more sustainable future. Five research themes are covered—solar, atmospheric, hydrologic, geologic, and climate change. The solar section comprises solar radiation, direct and diffuse radiation, infrared and ultraviolet radiation, clearness index, and sky luminance and luminous efficacy. The atmospheric section reviews wind, temperature, humidity, cloud classification, and storm prediction. The hydrologic section focuses on precipitation, rainfall-runoff, hail, snow, drought, flood, tides, water levels, and other variables. The geologic section covers works on landslides, earthquakes, liquefaction, erosion, soil classification, soil mechanics, and other properties. Finally, climate change forecasting and downscaling of climate models are reviewed. This work demonstrates the wide range of applications of these methods in different research fields. Some research gaps and interdisciplinary research opportunities are identified for future development of comprehensive forecast and evaluation approaches regarding the estimation of renewable energy and built environment-related variables. |
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Estimation of renewable energy and built environment-related variables using neural networks – A reviewNeural networkSolar variablesHHydrologic variablesAtmospheric variablesGeologic variablesClimate changeThis paper presents a review on the application of neural networks for the estimation, forecasting, monitoring, and classification of exogenous environmental variables that affect the performance, salubrity, and security of cities, buildings, and infrastructures. The forecast of these variables allows to explore renewable energy and water resources, to prevent potentially hazardous construction locations, and to find the healthiest places, thus promoting a more sustainable future. Five research themes are covered—solar, atmospheric, hydrologic, geologic, and climate change. The solar section comprises solar radiation, direct and diffuse radiation, infrared and ultraviolet radiation, clearness index, and sky luminance and luminous efficacy. The atmospheric section reviews wind, temperature, humidity, cloud classification, and storm prediction. The hydrologic section focuses on precipitation, rainfall-runoff, hail, snow, drought, flood, tides, water levels, and other variables. The geologic section covers works on landslides, earthquakes, liquefaction, erosion, soil classification, soil mechanics, and other properties. Finally, climate change forecasting and downscaling of climate models are reviewed. This work demonstrates the wide range of applications of these methods in different research fields. Some research gaps and interdisciplinary research opportunities are identified for future development of comprehensive forecast and evaluation approaches regarding the estimation of renewable energy and built environment-related variables.Elsevier2018info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/80198http://hdl.handle.net/10316/80198https://doi.org/10.1016/j.rser.2018.05.060eng1364-0321https://www.sciencedirect.com/science/article/pii/S1364032118304076Rodrigues, EugénioGomes, ÁlvaroGaspar, Adélio RodriguesHenggeler Antunes, Carlosinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2020-05-29T10:04:14Zoai:estudogeral.uc.pt:10316/80198Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:02:41.652451Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Estimation of renewable energy and built environment-related variables using neural networks – A review |
title |
Estimation of renewable energy and built environment-related variables using neural networks – A review |
spellingShingle |
Estimation of renewable energy and built environment-related variables using neural networks – A review Rodrigues, Eugénio Neural network Solar variables HHydrologic variables Atmospheric variables Geologic variables Climate change |
title_short |
Estimation of renewable energy and built environment-related variables using neural networks – A review |
title_full |
Estimation of renewable energy and built environment-related variables using neural networks – A review |
title_fullStr |
Estimation of renewable energy and built environment-related variables using neural networks – A review |
title_full_unstemmed |
Estimation of renewable energy and built environment-related variables using neural networks – A review |
title_sort |
Estimation of renewable energy and built environment-related variables using neural networks – A review |
author |
Rodrigues, Eugénio |
author_facet |
Rodrigues, Eugénio Gomes, Álvaro Gaspar, Adélio Rodrigues Henggeler Antunes, Carlos |
author_role |
author |
author2 |
Gomes, Álvaro Gaspar, Adélio Rodrigues Henggeler Antunes, Carlos |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Rodrigues, Eugénio Gomes, Álvaro Gaspar, Adélio Rodrigues Henggeler Antunes, Carlos |
dc.subject.por.fl_str_mv |
Neural network Solar variables HHydrologic variables Atmospheric variables Geologic variables Climate change |
topic |
Neural network Solar variables HHydrologic variables Atmospheric variables Geologic variables Climate change |
description |
This paper presents a review on the application of neural networks for the estimation, forecasting, monitoring, and classification of exogenous environmental variables that affect the performance, salubrity, and security of cities, buildings, and infrastructures. The forecast of these variables allows to explore renewable energy and water resources, to prevent potentially hazardous construction locations, and to find the healthiest places, thus promoting a more sustainable future. Five research themes are covered—solar, atmospheric, hydrologic, geologic, and climate change. The solar section comprises solar radiation, direct and diffuse radiation, infrared and ultraviolet radiation, clearness index, and sky luminance and luminous efficacy. The atmospheric section reviews wind, temperature, humidity, cloud classification, and storm prediction. The hydrologic section focuses on precipitation, rainfall-runoff, hail, snow, drought, flood, tides, water levels, and other variables. The geologic section covers works on landslides, earthquakes, liquefaction, erosion, soil classification, soil mechanics, and other properties. Finally, climate change forecasting and downscaling of climate models are reviewed. This work demonstrates the wide range of applications of these methods in different research fields. Some research gaps and interdisciplinary research opportunities are identified for future development of comprehensive forecast and evaluation approaches regarding the estimation of renewable energy and built environment-related variables. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/80198 http://hdl.handle.net/10316/80198 https://doi.org/10.1016/j.rser.2018.05.060 |
url |
http://hdl.handle.net/10316/80198 https://doi.org/10.1016/j.rser.2018.05.060 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1364-0321 https://www.sciencedirect.com/science/article/pii/S1364032118304076 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133919293997056 |