Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia

This paper presents wind speed and direction data measured with a weather station located in Puerto Bolivar, department of La Guajira, situated in the extreme north of Colombia, whose geographic coordinates are 12 110 N 71 550 W. A wind speed and direction sensor, a barometric pressure sensor, and a...

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Authors: Valencia Ochoa, Guillermo, Núñez Alvarez, José, Vanegas Chamorro, Marley
Format: article
Status:Versión aceptada para publicación
Publication Date:2019
Country:Colombia
Institution:Corporación Universidad de la Costa
Repository:Repositorio REDICUC
Language:Spanish
OAI Identifier:oai:repositorio.cuc.edu.co:11323/7468
Online Access:https://hdl.handle.net/11323/7468
https://doi.org/10.1016/j.dib.2019.104753
https://repositorio.cuc.edu.co/
Access Level:Open access
Keyword:Wind speed
Wind probability distribution
Wind direction
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spelling Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - ColombiaValencia Ochoa, GuillermoNúñez Alvarez, JoséVanegas Chamorro, MarleyWind speedWind probability distributionWind directionThis paper presents wind speed and direction data measured with a weather station located in Puerto Bolivar, department of La Guajira, situated in the extreme north of Colombia, whose geographic coordinates are 12 110 N 71 550 W. A wind speed and direction sensor, a barometric pressure sensor, and a temperature sensor were used to obtain the presented data. These data were taken at the height of 10 m, which is the highest point of the weather station. The data taken by the meteorological station correspond to a period of 20 years (1993e2013), with hourly frequency. For the missing data, a mathematical model to estimate the Julian averages was developed, allowing to calculate the frequency histograms and four types of probability distributions for these data. Also, the representative wind roses were generated, taking into account the averages in each of the 12 months of the year.Valencia Ochoa, GuillermoNúñez Alvarez, JoséVanegas Chamorro, MarleyCorporación Universidad de la Costa2020-11-24T16:30:00Z2020-11-24T16:30:00Z2019Artículo de revistahttp://purl.org/coar/resource_type/c_6501Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/version/c_ab4af688f83e57aaapplication/pdfapplication/pdfhttps://hdl.handle.net/11323/7468https://doi.org/10.1016/j.dib.2019.104753Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Data in Briefhttps://www.sciencedirect.com/science/article/pii/S2352340919311084?via%3Dihubreponame:Repositorio REDICUCinstname:Corporación Universidad de la Costainstacron:Corporación Universidad de la Costaspa[1] M.P. Pinto, J.K. Moreno, Y.A. Munoz, A. Ospino, Technical and economic evaluation of a small-scale wind power system ~ located in berlin, Colombia, Tecciencia 13 (24) (2018) 63e72.[2] A.-L.J. Luis, An approximation to the probability normal distribution and its inverse, Ing. Invest. Tecnol. 16 (4) (Oct. 2015) 605e611.[3] M. Ordaz, A simple approximation to the Gaussian distribution, Struct. Saf. 9 (4) (Jun. 1991) 315e318.[4] K. Krishnamoorthy, Handbook of the Normal Distribution Distributions with Applications, University of Louisiana Lafayette, 2010.[5] P.-A. Amaya-Martínez, A.-J. Saavedra-Montes, E.-I. Arango-Zuluaga, A statistical analysis of wind speed distribution models in the Aburr a Valley, Colombia, CT&F - Ciencia, Tecnol. y Futur. 5 (5) (2018) 121e136.[6] J.A. Carta, P. Ramírez, Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions, Renew. Energy 32 (3) (Mar. 2007) 518e531.[7] H. Bidaoui, I. El Abbassi, A. El Bouardi, A. Darcherif, Wind speed data analysis using Weibull and Rayleigh distribution functions, case study: five cities northern Morocco, Procedia Manuf. 32 (Jan. 2019) 786e793.Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf22024-09-16T21:44:15Z
dc.title.none.fl_str_mv Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia
title Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia
spellingShingle Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia
Valencia Ochoa, Guillermo
Wind speed
Wind probability distribution
Wind direction
title_short Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia
title_full Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia
title_fullStr Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia
title_full_unstemmed Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia
title_sort Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar - Colombia
dc.creator.none.fl_str_mv Valencia Ochoa, Guillermo
Núñez Alvarez, José
Vanegas Chamorro, Marley
author Valencia Ochoa, Guillermo
author_facet Valencia Ochoa, Guillermo
Núñez Alvarez, José
Vanegas Chamorro, Marley
author_role author
author2 Núñez Alvarez, José
Vanegas Chamorro, Marley
author2_role author
author
dc.subject.none.fl_str_mv Wind speed
Wind probability distribution
Wind direction
topic Wind speed
Wind probability distribution
Wind direction
description This paper presents wind speed and direction data measured with a weather station located in Puerto Bolivar, department of La Guajira, situated in the extreme north of Colombia, whose geographic coordinates are 12 110 N 71 550 W. A wind speed and direction sensor, a barometric pressure sensor, and a temperature sensor were used to obtain the presented data. These data were taken at the height of 10 m, which is the highest point of the weather station. The data taken by the meteorological station correspond to a period of 20 years (1993e2013), with hourly frequency. For the missing data, a mathematical model to estimate the Julian averages was developed, allowing to calculate the frequency histograms and four types of probability distributions for these data. Also, the representative wind roses were generated, taking into account the averages in each of the 12 months of the year.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020-11-24T16:30:00Z
2020-11-24T16:30:00Z
dc.type.none.fl_str_mv Artículo de revista
http://purl.org/coar/resource_type/c_6501
Text
info:eu-repo/semantics/article
http://purl.org/redcol/resource_type/ART
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/version/c_ab4af688f83e57aa
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11323/7468
https://doi.org/10.1016/j.dib.2019.104753
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/7468
https://doi.org/10.1016/j.dib.2019.104753
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv [1] M.P. Pinto, J.K. Moreno, Y.A. Munoz, A. Ospino, Technical and economic evaluation of a small-scale wind power system ~ located in berlin, Colombia, Tecciencia 13 (24) (2018) 63e72.
[2] A.-L.J. Luis, An approximation to the probability normal distribution and its inverse, Ing. Invest. Tecnol. 16 (4) (Oct. 2015) 605e611.
[3] M. Ordaz, A simple approximation to the Gaussian distribution, Struct. Saf. 9 (4) (Jun. 1991) 315e318.
[4] K. Krishnamoorthy, Handbook of the Normal Distribution Distributions with Applications, University of Louisiana Lafayette, 2010.
[5] P.-A. Amaya-Martínez, A.-J. Saavedra-Montes, E.-I. Arango-Zuluaga, A statistical analysis of wind speed distribution models in the Aburr a Valley, Colombia, CT&F - Ciencia, Tecnol. y Futur. 5 (5) (2018) 121e136.
[6] J.A. Carta, P. Ramírez, Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions, Renew. Energy 32 (3) (Mar. 2007) 518e531.
[7] H. Bidaoui, I. El Abbassi, A. El Bouardi, A. Darcherif, Wind speed data analysis using Weibull and Rayleigh distribution functions, case study: five cities northern Morocco, Procedia Manuf. 32 (Jan. 2019) 786e793.
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Corporación Universidad de la Costa
publisher.none.fl_str_mv Corporación Universidad de la Costa
dc.source.none.fl_str_mv Data in Brief
https://www.sciencedirect.com/science/article/pii/S2352340919311084?via%3Dihub
reponame:Repositorio REDICUC
instname:Corporación Universidad de la Costa
instacron:Corporación Universidad de la Costa
instname_str Corporación Universidad de la Costa
instacron_str Corporación Universidad de la Costa
institution Corporación Universidad de la Costa
reponame_str Repositorio REDICUC
collection Repositorio REDICUC
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score 15,812429

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