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;
Wind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast

dc.creatorPalomino, Kevin
dc.creatorReyes, Fabiola
dc.creatorNúñez, José
dc.creatorValencia, Guillermo
dc.creatorHerrera Acosta, Roberto
dc.date2020-07-17T15:15:23Z
dc.date2020-07-17T15:15:23Z
dc.date2020
dc.date.accessioned2023-10-03T19:38:19Z
dc.date.available2023-10-03T19:38:19Z
dc.identifier1791-2377
dc.identifier1791-9320
dc.identifierhttps://hdl.handle.net/11323/6621
dc.identifierdoi:10.25103/jestr.133.22
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9170926
dc.descriptionGreater 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.
dc.formatapplication/pdf
dc.languageeng
dc.publisherJournal of Engineering Science and Technology Review
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[4] Congress of the Republic of Colombia: Law 1715 - 2014.
dc.relation[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.
dc.relation[6] Valencia, G. and Vanegas, M., “Atlas Eólico de la Región Caribe Colombiana”, Vol. 1, 1st ed., Universidad del Atlántico, Barranquilla, (2016), 1-45.
dc.relation[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.
dc.relation[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.
dc.relation[9] Lawrie, L.K. al., “ENERGYPLUS, a new-generation building energy simulation program”. Proceedings of Building Simulation 1999, Kyoto, Japan, (Aug. 1999), 1999.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[13] Wang, X., Guo, P. and Huang, X., “A Review of Wind Power Forecasting Models”, Energy Procedia, Vol. 12, (2011), 770–778.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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
dc.relation[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
dc.relation[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
dc.relation[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.
dc.relation[22] Rojo, J. M., “Regresión lineal multiple”, Instituto de Economía y Geografía Madrid, Madrid, (2007), 32.
dc.relation[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.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subjectWind speed prediction
dc.subjectARIMA
dc.subjectOLS
dc.subjectSustainable energy
dc.titleWind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast
dc.titleWind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast
dc.titleWind speed prediction based on univariate ARIMA and OLS on the Colombian Caribbean Coast
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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