Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/80198
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rodrigues, Eugénio | - |
dc.contributor.author | Gomes, Álvaro | - |
dc.contributor.author | Gaspar, Adélio Rodrigues | - |
dc.contributor.author | Henggeler Antunes, Carlos | - |
dc.date.accessioned | 2018-07-16T10:26:45Z | - |
dc.date.available | 2018-07-16T10:26:45Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 1364-0321 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/80198 | - |
dc.description.abstract | 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. | pt |
dc.language.iso | eng | pt |
dc.publisher | Elsevier | pt |
dc.relation | Ren4EEnIEQ (PTDC/EMS-ENE/3238/2014) | pt |
dc.relation | SUSpENsE (CENTRO-01-0145-FEDER-000006) | pt |
dc.relation | FCT SFRH/BPD/99668/2014 | pt |
dc.relation | POCI-01-0145-FEDER-016760 | - |
dc.relation | LISBOA-01-0145-FEDER-016760 | - |
dc.relation | UID/MULTI/00308/2013 | - |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | Neural network | pt |
dc.subject | Solar variables | pt |
dc.subject | HHydrologic variables | pt |
dc.subject | Atmospheric variables | pt |
dc.subject | Geologic variables | pt |
dc.subject | Climate change | pt |
dc.title | Estimation of renewable energy and built environment-related variables using neural networks – A review | pt |
dc.type | article | - |
degois.publication.firstPage | 959 | pt |
degois.publication.lastPage | 988 | pt |
degois.publication.title | Renewable and Sustainable Energy Reviews | pt |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1364032118304076 | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1016/j.rser.2018.05.060 | pt |
degois.publication.volume | 94 | pt |
dc.date.embargo | 2018-01-01 | * |
dc.date.periodoembargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
crisitem.project.grantno | Institute for Systems Engineering and Computers at Coimbra | - |
crisitem.author.researchunit | ADAI - Association for the Development of Industrial Aerodynamics | - |
crisitem.author.researchunit | INESC Coimbra – Institute for Systems Engineering and Computers at Coimbra | - |
crisitem.author.researchunit | ADAI - Association for the Development of Industrial Aerodynamics | - |
crisitem.author.researchunit | INESC Coimbra – Institute for Systems Engineering and Computers at Coimbra | - |
crisitem.author.orcid | 0000-0001-7023-4484 | - |
crisitem.author.orcid | 0000-0003-1229-6243 | - |
crisitem.author.orcid | 0000-0001-6947-4579 | - |
crisitem.author.orcid | 0000-0003-4754-2168 | - |
Appears in Collections: | FCTUC Eng.Mecânica - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
ANN_Review_manuscript.pdf | Preprint manuscript | 454.75 kB | Adobe PDF | View/Open |
SCOPUSTM
Citations
47
checked on Oct 7, 2024
WEB OF SCIENCETM
Citations
5
43
checked on Oct 2, 2024
Page view(s)
423
checked on Oct 15, 2024
Download(s) 50
914
checked on Oct 15, 2024
Google ScholarTM
Check
Altmetric
Altmetric
This item is licensed under a Creative Commons License