Wind Speed Prediction Based on Univariate ARIMA and OLS on the Colombian Caribbean Coast
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 potentia...
- Autores:
-
Palomino, Kevin
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad del Atlántico
- Repositorio:
- Repositorio Uniatlantico
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniatlantico.edu.co:20.500.12834/796
- Acceso en línea:
- https://hdl.handle.net/20.500.12834/796
- Palabra clave:
- wind speed prediction, ARIMA, OLS, Sustainable energy.
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc/4.0/
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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 |
| dc.contributor.author.none.fl_str_mv |
Palomino, Kevin |
| dc.contributor.other.none.fl_str_mv |
Reyes, Fabiola Núñez, José Valencia, Guillermo Herrera Acosta, Roberto |
| dc.subject.keywords.spa.fl_str_mv |
wind speed prediction, ARIMA, OLS, Sustainable energy. |
| topic |
wind speed prediction, ARIMA, OLS, Sustainable energy. |
| description |
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. |
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2020 |
| dc.date.issued.none.fl_str_mv |
2020-06-10 |
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2020-01-10 |
| dc.date.accessioned.none.fl_str_mv |
2022-11-15T19:19:41Z |
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2022-11-15T19:19:41Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Artículo |
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https://hdl.handle.net/20.500.12834/796 |
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10.25103/jestr.133.22 |
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Universidad del Atlántico |
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Repositorio Universidad del Atlántico |
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https://hdl.handle.net/20.500.12834/796 |
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10.25103/jestr.133.22 Universidad del Atlántico Repositorio Universidad del Atlántico |
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eng |
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Ingeniería Industrial |
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Sede Norte |
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Palomino, Kevin51e6bf73-5bd7-4787-91cb-1cc7fb94f53eReyes, FabiolaNúñez, JoséValencia, GuillermoHerrera Acosta, Roberto2022-11-15T19:19:41Z2022-11-15T19:19:41Z2020-06-102020-01-10https://hdl.handle.net/20.500.12834/79610.25103/jestr.133.22Universidad del AtlánticoRepositorio Universidad del Atlánticotechniques 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.application/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2jestrWind Speed Prediction Based on Univariate ARIMA and OLS on the Colombian Caribbean CoastPúblico generalwind speed prediction, ARIMA, OLS, Sustainable energy.info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1BarranquillaIngeniería IndustrialSede Norte[1] Taner, T. and Demirci, K.O., “Energy and economic analysis of the wind turbine plant’s draft for the Aksaray City”, Applied Ecology and Environmental Sciences, 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.http://purl.org/coar/resource_type/c_6501ORIGINALfulltext221332020.pdffulltext221332020.pdfapplication/pdf576790https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/796/1/fulltext221332020.pdfabd74ec9fabe1a093ad6fa65fc605b36MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/796/2/license_rdf24013099e9e6abb1575dc6ce0855efd5MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81306https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/796/3/license.txt67e239713705720ef0b79c50b2ececcaMD5320.500.12834/796oai:repositorio.uniatlantico.edu.co:20.500.12834/7962022-11-15 14:19:42.769DSpace de la Universidad de Atlánticosysadmin@mail.uniatlantico.edu.coVMOpcm1pbm9zIGdlbmVyYWxlcyBkZWwgUmVwb3NpdG9yaW8gSW5zdGl0dWNpb25hbCBkZSBsYSBVbml2ZXJzaWRhZCBkZWwgQXRsw6FudGljbwoKRWwgKGxvcykgYXV0b3IgKGVzKSBoYW4gYXNlZ3VyYWRvIChuKSBsbyBzaWd1aWVudGUgc29icmUgbGEgb2JyYSBhIGludGVncmFyIGVuIGVsIFJlcG9zaXRvcmlvIEluc3RpdHVjaW9uYWwsIHF1ZToKCuKXjwlFcyBvcmlnaW5hbCwgZGUgc3UgZXhjbHVzaXZhIGF1dG9yw61hLCBzZSByZWFsaXrDsyBzaW4gdmlvbGFyIG8gdXN1cnBhciBkZXJlY2hvcyBkZSBhdXRvciBkZSB0ZXJjZXJvcyB5IHBvc2VlIGxhIHRpdHVsYXJpZGFkLgril48JQXN1bWlyw6FuIGxhIHJlc3BvbnNhYmlsaWRhZCB0b3RhbCBwb3IgZWwgY29udGVuaWRvIGEgbGEgb2JyYSBhbnRlIGxhIEluc3RpdHVjacOzbiB5IHRlcmNlcm9zLgril48JQXV0b3JpemFuIGEgdMOtdHVsbyBncmF0dWl0byB5IHJlbnVuY2lhcyBhIHJlY2liaXIgZW1vbHVtZW50b3MgcG9yIGxhcyBhY3RpdmlkYWRlcyBxdWUgc2UgcmVhbGljZW4gY29uIGVsbGEsIHNlZ8O6biBzdSBsaWNlbmNpYS4KCgpMYSBVbml2ZXJzaWRhZCBkZWwgQXRsw6FudGljbywgcG9yIHN1IHBhcnRlLCBzZSBjb21wcm9tZXRlIGEgYWN0dWFyIGVuIGxvcyB0w6lybWlub3MgZXN0YWJsZWNpZG9zIGVuIGxhIExleSAyMyBkZSAxOTgyIHkgbGEgRGVjaXNpw7NuIEFuZGluYSAzNTEgZGUgMTk5MywgZGVtw6FzIG5vcm1hcyBnZW5lcmFsZXMgc29icmUgbGEgbWF0ZXJpYSB5IGVsIEFjdWVyZG8gU3VwZXJpb3IgMDAxIGRlIDE3IGRlIG1hcnpvIGRlIDIwMTEsIHBvciBtZWRpbyBkZWwgY3VhbCBzZSBleHBpZGUgZWwgRXN0YXR1dG8gZGUgUHJvcGllZGFkIEludGVsZWN0dWFsIGRlIGxhIFVuaXZlcnNpZGFkIGRlbCBBdGzDoW50aWNvLgoKUG9yIMO6bHRpbW8sIGhhbiBzaWRvIGluZm9ybWFkb3Mgc29icmUgZWwgdHJhdGFtaWVudG8gZGUgZGF0b3MgcGVyc29uYWxlcyBwYXJhIGZpbmVzIGFjYWTDqW1pY29zIHkgZW4gYXBsaWNhY2nDs24gZGUgY29udmVuaW9zIGNvbiB0ZXJjZXJvcyBvIHNlcnZpY2lvcyBjb25leG9zIGNvbiBhY3RpdmlkYWRlcyBwcm9waWFzIGRlIGxhIGFjYWRlbWlhLCBiYWpvIGVsIGVzdHJpY3RvIGN1bXBsaW1pZW50byBkZSBsb3MgcHJpbmNpcGlvcyBkZSBsZXkuCgpMYXMgY29uc3VsdGFzLCBjb3JyZWNjaW9uZXMgeSBzdXByZXNpb25lcyBkZSBkYXRvcyBwZXJzb25hbGVzIHB1ZWRlbiBwcmVzZW50YXJzZSBhbCBjb3JyZW8gZWxlY3Ryw7NuaWNvIGhhYmVhc2RhdGFAbWFpbC51bmlhdGxhbnRpY28uZWR1LmNvCg== |