Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast
Greater incorporation of wind energy into power systems has necessitated the development of accurate and reliable techniques for wind speed forecasting. However, although there are multiple studies, none are set up for the Colombia Caribbean coast. This is a disadvantage because the potential of win...
- Autores:
-
Palomino, Kevin
Reyes, Fabiola
Núñez, José
Valencia, Guillermo
Herrera Acosta, Roberto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/6621
- Acceso en línea:
- https://hdl.handle.net/11323/6621
https://repositorio.cuc.edu.co/
- Palabra clave:
- Wind speed prediction
ARIMA
OLS
Sustainable energy
- Rights
- openAccess
- License
- CC0 1.0 Universal
| id |
RCUC2_0f3a93836dda1c3cfec1abce6ff31d56 |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/6621 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast |
| dc.title.translated.spa.fl_str_mv |
Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast |
| title |
Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast |
| spellingShingle |
Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast Wind speed prediction ARIMA OLS Sustainable energy |
| title_short |
Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast |
| title_full |
Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast |
| title_fullStr |
Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast |
| title_full_unstemmed |
Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast |
| title_sort |
Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast |
| dc.creator.fl_str_mv |
Palomino, Kevin Reyes, Fabiola Núñez, José Valencia, Guillermo Herrera Acosta, Roberto |
| dc.contributor.author.spa.fl_str_mv |
Palomino, Kevin Reyes, Fabiola Núñez, José Valencia, Guillermo Herrera Acosta, Roberto |
| dc.subject.spa.fl_str_mv |
Wind speed prediction ARIMA OLS Sustainable energy |
| topic |
Wind speed prediction ARIMA OLS Sustainable energy |
| description |
Greater incorporation of wind energy into power systems has necessitated the development of accurate and reliable techniques for wind speed forecasting. However, although there are multiple studies, none are set up for the Colombia Caribbean coast. This is a disadvantage because the potential of wind resources in this region is greater than the hydroelectric potential of the whole country, but all this potential has yet to be developed. In this paper, based on time series, Autoregressive Integrated Moving Average (ARIMA), and Multiple Regression with Ordinary Least Squares (OLS) in the study, two models are proposed and their performance for wind speed prediction is compared. The data were collected in the meteorological station located in the experimental farm of the Atlantic University, in Barranquilla, Colombia, and variables analyzed included wind speed, wind direction, temperature, relative humidity, solar radiation, and pressure. The results of the two approaches indicated that among all the involved models, the ARIMA model has the best predicting performance. Also, it is essential to highlight that through this work, decision-makers would explore the local wind potential, allowing for the possibility of predicting future wind speed, and thus giving them the ability to plan the production and the interaction of other sources of energy. |
| publishDate |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2020-07-17T15:15:23Z |
| dc.date.available.none.fl_str_mv |
2020-07-17T15:15:23Z |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
acceptedVersion |
| dc.identifier.issn.spa.fl_str_mv |
1791-2377 1791-9320 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/6621 |
| dc.identifier.doi.spa.fl_str_mv |
doi:10.25103/jestr.133.22 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
1791-2377 1791-9320 doi:10.25103/jestr.133.22 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/6621 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
[1] Taner, T. and Demirci, K.O., “Energy and economic City”, Applied Ecology and Environmental Sciences, analysis of the wind turbine plant’s draft for the Aksaray Vol. 2, No. 3, (2015), 82–85. [2] Boutoubat, M., Mokrani, L., Machmoum, M., “Control of a wind energy conversion system equipped by a DFIG for active power generation and power quality improvement”, Renewable Energy, Vol. 50, (2013), 378–386. [3] Brown, S.P.A. and Huntington, H.G., “Energy security and climate change protection: Complementarity or tradeoff?” Energy Policy, Vol. 36, No. 9, (2008), 3510– 3513. [4] Congress of the Republic of Colombia: Law 1715 - 2014. [5] Valencia, G., Vanegas, M. and Polo, J., "Análisis estadístico de la velocidad y dirección del viento en la Costa Caribe colombiana con énfasis en La Guajira”, Vol. 1, 1st ed., Universidad del Atlántico, Barranquilla, (2016), 150. [6] Valencia, G. and Vanegas, M., “Atlas Eólico de la Región Caribe Colombiana”, Vol. 1, 1st ed., Universidad del Atlántico, Barranquilla, (2016), 1-45. [7] Ouyang, T., Zha, X., Qin, L., Xiong, Y. and Xia, T., “Wind power prediction method based on regime of switching kernel functions”, Journal of Wind Engineering and Industrial Aerodynamics, Vol. 153, (2016), 26–33. [8] Cadenas, E. and Rivera, W., “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model”, Renewable Energy, Vol. 35, No. 12, (2010), 2732–2738. [9] Lawrie, L.K. al., “ENERGYPLUS, a new-generation building energy simulation program”. Proceedings of Building Simulation 1999, Kyoto, Japan, (Aug. 1999), 1999. [10] Liu, H., Erdem, E. and Shi, J., “Comprehensive evaluation of ARMA–GARCH(-M) approach hes for modeling the mean and volatility of wind speed”, Applied Energy, Vol. 88, No. 3, (2011), 724–732. [11] Kavasseri, R.G. and Seetharaman, K., “Day-ahead wind speed forecasting using f-ARIMA models”, Renewable Energy, Vol. 34, No. 5, (2009), 1388–1393. [12] Ariza, A.M., “Métodos utilizados para el pronóstico de demanda de energía eléctrica en sistemas de distribución”, Vol. 1, 1st ed., Universidad Tecnológica de Pereira, Pereira, (2013), 15-145. [13] Wang, X., Guo, P. and Huang, X., “A Review of Wind Power Forecasting Models”, Energy Procedia, Vol. 12, (2011), 770–778. [14] Barrozo, F., Valencia, G. and Escorcia, Y.C., “Hybrid PV and wind grid-connected renewable energy system to reduce the gas emission and operation cost”, Contemporary Engineering Sciences, Vol. 10, No. 26, (2017), 1269-1278. [15] Haque, A.U., Mandal, P., Meng, J. and Negnevitsky, M., “Wind speed forecast model for wind farm based on a hybrid machine learning algorithm”, International Journal of Sustainable Energy, Vol. 34, No. 1, (2015), 38–51. [16] Cassola, F. and Burlando, M., “Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output”, Applied Energy, Vol. 99, (2012), 154–166. [17] Torres, J. L., García, A., De Blas, M. and De Francisco, A., “Forecast of hourly average wind speed with ARMA models in Navarre (Spain)”, Solar Energy, Vol. 79, No. 1, (2005), 65–77. [18] Box, G.E.P and Jenkins, G. M “Time Series Analysis Time series Analysis: Forecasting and control”, Vol. 1, 3rd ed., Prentice-Hall, New Jersey, (1994), 614 [19] Box, G.E.P, Jenkins, G. M. and Reinsel, G., “Time series Analysis: Forecasting and control”, Vol. 1, 2nd ed., Holden-Day, New Jersey, (1976), 586 [20] Makridakis, S. G., Wheelwright, S. C. and Hyndman, R. J., “Forecasting: Methods and Applications”, Vol. 1, 3rd ed., John Wiley & Sons, New York, (1998), 656 [21] Aguado, J., Quevedo, A., Castro, M., Arteaga, R., Vázquez, M.A, and Zamora, B.P., “Meteorological variables prediction through ARIMA models”, Agrociencia, Vol. 50, No. 1, (2016), 1-13. [22] Rojo, J. M., “Regresión lineal multiple”, Instituto de Economía y Geografía Madrid, Madrid, (2007), 32. [23] Herrera, R., Palomino, K., Reyes, F. and Valencia, G., "Análisis Estadístico Descriptivo e Inferencial de la Velocidad y Dirección del viento en la Costa Caribe Colombiana", Revista Espacios, Vol. 39, No. 19, (2018), 3-15. |
| dc.rights.spa.fl_str_mv |
CC0 1.0 Universal |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.publisher.spa.fl_str_mv |
Journal of Engineering Science and Technology Review |
| institution |
Corporación Universidad de la Costa |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/199f9c7c-c653-43e4-9e98-58de9e73b74a/download https://repositorio.cuc.edu.co/bitstreams/0c85812b-e83e-445a-9efa-0ea362f7c977/download https://repositorio.cuc.edu.co/bitstreams/39d7291e-fc8e-4d37-9aaf-7aaac7e74df2/download https://repositorio.cuc.edu.co/bitstreams/0c66cc34-06ba-4cc3-826b-60ac7a31e3ec/download https://repositorio.cuc.edu.co/bitstreams/17f71f43-f4ce-44e9-8f0d-974a125a3400/download |
| bitstream.checksum.fl_str_mv |
abd74ec9fabe1a093ad6fa65fc605b36 42fd4ad1e89814f5e4a476b409eb708c e30e9215131d99561d40d6b0abbe9bad a0cb0d6b0eb127a59a80412540e4f2d4 6ab8fcb5e21a0ea65847ec87dbb6386d |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1811760817496915968 |
| spelling |
Palomino, KevinReyes, FabiolaNúñez, JoséValencia, GuillermoHerrera Acosta, Roberto2020-07-17T15:15:23Z2020-07-17T15:15:23Z20201791-23771791-9320https://hdl.handle.net/11323/6621doi:10.25103/jestr.133.22Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Greater incorporation of wind energy into power systems has necessitated the development of accurate and reliable techniques for wind speed forecasting. However, although there are multiple studies, none are set up for the Colombia Caribbean coast. This is a disadvantage because the potential of wind resources in this region is greater than the hydroelectric potential of the whole country, but all this potential has yet to be developed. In this paper, based on time series, Autoregressive Integrated Moving Average (ARIMA), and Multiple Regression with Ordinary Least Squares (OLS) in the study, two models are proposed and their performance for wind speed prediction is compared. The data were collected in the meteorological station located in the experimental farm of the Atlantic University, in Barranquilla, Colombia, and variables analyzed included wind speed, wind direction, temperature, relative humidity, solar radiation, and pressure. The results of the two approaches indicated that among all the involved models, the ARIMA model has the best predicting performance. Also, it is essential to highlight that through this work, decision-makers would explore the local wind potential, allowing for the possibility of predicting future wind speed, and thus giving them the ability to plan the production and the interaction of other sources of energy.Palomino, KevinReyes, FabiolaNúñez, JoséValencia, GuillermoHerrera Acosta, Roberto-will be generated-orcid-0000-0002-7161-3360-600engJournal of Engineering Science and Technology ReviewCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Wind speed predictionARIMAOLSSustainable energyWind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean CoastWind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean CoastWind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean CoastArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Taner, T. and Demirci, K.O., “Energy and economic City”, Applied Ecology and Environmental Sciences, analysis of the wind turbine plant’s draft for the Aksaray Vol. 2, No. 3, (2015), 82–85.[2] Boutoubat, M., Mokrani, L., Machmoum, M., “Control of a wind energy conversion system equipped by a DFIG for active power generation and power quality improvement”, Renewable Energy, Vol. 50, (2013), 378–386.[3] Brown, S.P.A. and Huntington, H.G., “Energy security and climate change protection: Complementarity or tradeoff?” Energy Policy, Vol. 36, No. 9, (2008), 3510– 3513.[4] Congress of the Republic of Colombia: Law 1715 - 2014.[5] Valencia, G., Vanegas, M. and Polo, J., "Análisis estadístico de la velocidad y dirección del viento en la Costa Caribe colombiana con énfasis en La Guajira”, Vol. 1, 1st ed., Universidad del Atlántico, Barranquilla, (2016), 150.[6] Valencia, G. and Vanegas, M., “Atlas Eólico de la Región Caribe Colombiana”, Vol. 1, 1st ed., Universidad del Atlántico, Barranquilla, (2016), 1-45.[7] Ouyang, T., Zha, X., Qin, L., Xiong, Y. and Xia, T., “Wind power prediction method based on regime of switching kernel functions”, Journal of Wind Engineering and Industrial Aerodynamics, Vol. 153, (2016), 26–33.[8] Cadenas, E. and Rivera, W., “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model”, Renewable Energy, Vol. 35, No. 12, (2010), 2732–2738.[9] Lawrie, L.K. al., “ENERGYPLUS, a new-generation building energy simulation program”. Proceedings of Building Simulation 1999, Kyoto, Japan, (Aug. 1999), 1999.[10] Liu, H., Erdem, E. and Shi, J., “Comprehensive evaluation of ARMA–GARCH(-M) approach hes for modeling the mean and volatility of wind speed”, Applied Energy, Vol. 88, No. 3, (2011), 724–732.[11] Kavasseri, R.G. and Seetharaman, K., “Day-ahead wind speed forecasting using f-ARIMA models”, Renewable Energy, Vol. 34, No. 5, (2009), 1388–1393.[12] Ariza, A.M., “Métodos utilizados para el pronóstico de demanda de energía eléctrica en sistemas de distribución”, Vol. 1, 1st ed., Universidad Tecnológica de Pereira, Pereira, (2013), 15-145.[13] Wang, X., Guo, P. and Huang, X., “A Review of Wind Power Forecasting Models”, Energy Procedia, Vol. 12, (2011), 770–778.[14] Barrozo, F., Valencia, G. and Escorcia, Y.C., “Hybrid PV and wind grid-connected renewable energy system to reduce the gas emission and operation cost”, Contemporary Engineering Sciences, Vol. 10, No. 26, (2017), 1269-1278.[15] Haque, A.U., Mandal, P., Meng, J. and Negnevitsky, M., “Wind speed forecast model for wind farm based on a hybrid machine learning algorithm”, International Journal of Sustainable Energy, Vol. 34, No. 1, (2015), 38–51.[16] Cassola, F. and Burlando, M., “Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output”, Applied Energy, Vol. 99, (2012), 154–166.[17] Torres, J. L., García, A., De Blas, M. and De Francisco, A., “Forecast of hourly average wind speed with ARMA models in Navarre (Spain)”, Solar Energy, Vol. 79, No. 1, (2005), 65–77.[18] Box, G.E.P and Jenkins, G. M “Time Series Analysis Time series Analysis: Forecasting and control”, Vol. 1, 3rd ed., Prentice-Hall, New Jersey, (1994), 614[19] Box, G.E.P, Jenkins, G. M. and Reinsel, G., “Time series Analysis: Forecasting and control”, Vol. 1, 2nd ed., Holden-Day, New Jersey, (1976), 586[20] Makridakis, S. G., Wheelwright, S. C. and Hyndman, R. J., “Forecasting: Methods and Applications”, Vol. 1, 3rd ed., John Wiley & Sons, New York, (1998), 656[21] Aguado, J., Quevedo, A., Castro, M., Arteaga, R., Vázquez, M.A, and Zamora, B.P., “Meteorological variables prediction through ARIMA models”, Agrociencia, Vol. 50, No. 1, (2016), 1-13.[22] Rojo, J. M., “Regresión lineal multiple”, Instituto de Economía y Geografía Madrid, Madrid, (2007), 32.[23] Herrera, R., Palomino, K., Reyes, F. and Valencia, G., "Análisis Estadístico Descriptivo e Inferencial de la Velocidad y Dirección del viento en la Costa Caribe Colombiana", Revista Espacios, Vol. 39, No. 19, (2018), 3-15.PublicationORIGINALWind Speed Prediction Based on Univariate ARIMA and OLS on the Colombian Caribbean Coast.pdfWind Speed Prediction Based on Univariate ARIMA and OLS on the Colombian Caribbean Coast.pdfapplication/pdf576790https://repositorio.cuc.edu.co/bitstreams/199f9c7c-c653-43e4-9e98-58de9e73b74a/downloadabd74ec9fabe1a093ad6fa65fc605b36MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/0c85812b-e83e-445a-9efa-0ea362f7c977/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/39d7291e-fc8e-4d37-9aaf-7aaac7e74df2/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILWind Speed Prediction Based on Univariate ARIMA and OLS on the Colombian Caribbean Coast.pdf.jpgWind Speed Prediction Based on Univariate ARIMA and OLS on the Colombian Caribbean Coast.pdf.jpgimage/jpeg73681https://repositorio.cuc.edu.co/bitstreams/0c66cc34-06ba-4cc3-826b-60ac7a31e3ec/downloada0cb0d6b0eb127a59a80412540e4f2d4MD54TEXTWind Speed Prediction Based on Univariate ARIMA and OLS on the Colombian Caribbean Coast.pdf.txtWind Speed Prediction Based on Univariate ARIMA and OLS on the Colombian Caribbean Coast.pdf.txttext/plain26066https://repositorio.cuc.edu.co/bitstreams/17f71f43-f4ce-44e9-8f0d-974a125a3400/download6ab8fcb5e21a0ea65847ec87dbb6386dMD5511323/6621oai:repositorio.cuc.edu.co:11323/66212024-09-17 12:49:34.398http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |