Assessment of Renewable Energy Resources with Remote Sensing

Detalhes bibliográficos
Autor(a) principal: Martins, Fernando Ramos [UNIFESP]
Data de Publicação: 2021
Tipo de documento: Livro
Idioma: eng
Título da fonte: Repositório Institucional da UNIFESP
Texto Completo: https://repositorio.unifesp.br/handle/11600/60099
https://www.mdpi.com/journal/remotesensing
Resumo: The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.
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spelling Martins, Fernando Ramos [UNIFESP]Martins, Fernando Ramos [UNIFESP]http://lattes.cnpq.br/9012359647335296Basel2021-02-02T17:52:07Z2021-02-02T17:52:07Z2021MARTINS, Fernando Ramos. Assessment of Renewable Energy Resources with Remote Sensing. Basel : MDPI, 2021978-3-0365-0481-02072-4292https://repositorio.unifesp.br/handle/11600/60099https://www.mdpi.com/journal/remotesensingThe development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.About the Editor .............................................. vii Fernando Ramos Martins Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing Reprinted from: Remote Sens. 2020, 12, 3748, doi:10.3390/rs12223748 ................. 1 André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil Reprinted from: Remote Sens. 2020, 12, 2793, doi:10.3390/rs12172793 ................. 7 Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region Reprinted from: Remote Sens. 2020, 12, 3509, doi:10.3390/rs12213509 ................. 33 Joaquín Alonso-Montesinos Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera Reprinted from: Remote Sens. 2020, 12, 1382, doi:10.3390/rs12091382 ................. 43 Román Mondragón, Joaquín Alonso-Montesinos, David Riveros-Rosas, Mauro Valdés, Héctor Estévez, Adriana E. González-Cabrera and Wolfgang Stremme Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area Reprinted from: Remote Sens. 2020, 12, 1212, doi:10.3390/rs12071212 ................. 61 Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island Reprinted from: Remote Sens. 2020, 12, 2271, doi:10.3390/rs12142271 ................. 79 Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models Reprinted from: Remote Sens. 2019, 11, 2795, doi:10.3390/rs11232795 ................. 101 Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning Reprinted from: Remote Sens. 2020, 12, 3420, doi:10.3390/rs12203420 ................. 125 Ian R. Young, Ebru Kirezci and Agustinus Ribal The Global Wind Resource Observed by Scatterometer Reprinted from: Remote Sens. 2020, 12, 2920, doi:10.3390/rs12182920 ................. 147 Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura Coastal Wind Measurements Using a Single Scanning LiDAR Reprinted from: Remote Sens. 2020, 12, 1347, doi:10.3390/rs12081347 ................. 165 Cristina Sáez Blázquez, Pedro Carrasco García, Ignacio Martín Nieto, MiguelAngel ´ Maté-González, Arturo Farfán Martín and Diego González-Aguilera Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods Reprinted from: Remote Sens. 2020, 12, 1948, doi:10.3390/rs12121948 ................. 189 Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data Reprinted from: Remote Sens. 2020, 12, 2561, doi:10.3390/rs12162561 ................. 207Instituto do Mar246 f.engMDPIPrinted Edition of the Special Issue Published in Remote SensingRenewable energy resource assessment and forecastingRemote sensingData acquisitionData processingStatistical analysisMachine learning techniquesAssessment of Renewable Energy Resources with Remote Sensinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UNIFESPinstname:Universidade Federal de São Paulo (UNIFESP)instacron:UNIFESPInstituto do Mar (IMar)Ciências do MarORIGINALRemote Sensing Assessment of Renewable Energy Resources with Remote Sensing.pdfRemote Sensing Assessment of Renewable Energy Resources with Remote 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dc.title.en.fl_str_mv Assessment of Renewable Energy Resources with Remote Sensing
title Assessment of Renewable Energy Resources with Remote Sensing
spellingShingle Assessment of Renewable Energy Resources with Remote Sensing
Martins, Fernando Ramos [UNIFESP]
Renewable energy resource assessment and forecasting
Remote sensing
Data acquisition
Data processing
Statistical analysis
Machine learning techniques
title_short Assessment of Renewable Energy Resources with Remote Sensing
title_full Assessment of Renewable Energy Resources with Remote Sensing
title_fullStr Assessment of Renewable Energy Resources with Remote Sensing
title_full_unstemmed Assessment of Renewable Energy Resources with Remote Sensing
title_sort Assessment of Renewable Energy Resources with Remote Sensing
author Martins, Fernando Ramos [UNIFESP]
author_facet Martins, Fernando Ramos [UNIFESP]
author_role author
dc.contributor.editor.none.fl_str_mv Martins, Fernando Ramos [UNIFESP]
dc.contributor.authorLattes.pt_BR.fl_str_mv http://lattes.cnpq.br/9012359647335296
dc.contributor.author.fl_str_mv Martins, Fernando Ramos [UNIFESP]
dc.subject.eng.fl_str_mv Renewable energy resource assessment and forecasting
Remote sensing
Data acquisition
Data processing
Statistical analysis
Machine learning techniques
topic Renewable energy resource assessment and forecasting
Remote sensing
Data acquisition
Data processing
Statistical analysis
Machine learning techniques
description The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.
publishDate 2021
dc.date.accessioned.fl_str_mv 2021-02-02T17:52:07Z
dc.date.available.fl_str_mv 2021-02-02T17:52:07Z
dc.date.issued.fl_str_mv 2021
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/book
format book
status_str publishedVersion
dc.identifier.citation.fl_str_mv MARTINS, Fernando Ramos. Assessment of Renewable Energy Resources with Remote Sensing. Basel : MDPI, 2021
dc.identifier.uri.fl_str_mv https://repositorio.unifesp.br/handle/11600/60099
https://www.mdpi.com/journal/remotesensing
dc.identifier.isbn.pt_BR.fl_str_mv 978-3-0365-0481-0
dc.identifier.issn.none.fl_str_mv 2072-4292
identifier_str_mv MARTINS, Fernando Ramos. Assessment of Renewable Energy Resources with Remote Sensing. Basel : MDPI, 2021
978-3-0365-0481-0
2072-4292
url https://repositorio.unifesp.br/handle/11600/60099
https://www.mdpi.com/journal/remotesensing
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartofseries.pt_BR.fl_str_mv Printed Edition of the Special Issue Published in Remote Sensing
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 246 f.
dc.coverage.spatial.pt_BR.fl_str_mv Basel
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Institucional da UNIFESP
instname:Universidade Federal de São Paulo (UNIFESP)
instacron:UNIFESP
instname_str Universidade Federal de São Paulo (UNIFESP)
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institution UNIFESP
reponame_str Repositório Institucional da UNIFESP
collection Repositório Institucional da UNIFESP
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