Pronóstico de velocidad de viento para generación eólica Offshore basado en programación genética

dc.contributorRivera Rodríguez, Sergio Raúl
dc.contributorGrupo de Investigación EMC-UN
dc.creatorGarrido Atencia, Oscar Alberto
dc.date.accessioned2021-06-01T17:19:53Z
dc.date.available2021-06-01T17:19:53Z
dc.date.created2021-06-01T17:19:53Z
dc.date.issued2020
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/79586
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.description.abstractLas energías renovables han surgido como la alternativa más viable para solucionar los problemas que presentan las fuentes de generación convencionales. En este sentido, la generación eólica offshore cuenta con gran potencial de crecimiento para los próximos años es por esto que el presente trabajo plantea una metodología que implementa la programación genética para realizar pronósticos de vientos promedio a mediano y largo plazo, con el fin de minimizar la incertidumbre asociada a este tipo de generación. Para esto, inicialmente se realiza el planteamiento del algoritmo regresión simbólica híbrida por medio del cual se realizarán los pronósticos de vientos propuestos; realizando una descripción del funcionamiento de este. Posteriormente se realiza la implementación del algoritmo planteado en cuatro casos de estudio ubicados en zonas costeras y en islas, de tal manera que se disponga de históricos de datos meteorológicos con los cuales poder realizar las pruebas del algoritmo. Posterior a esto, se evaluarán los errores obtenidos para seleccionar una cantidad de datos para entrenamiento y prueba del algoritmo.
dc.description.abstractRenewable energies have emerged as the most viable alternative to solve the problems presented by conventional generation sources. offshore wind generation has great growth potential for the next years, which is why this work proposes a methodology that implements genetic programming to make forecasts of average winds in the medium and long term, to minimize the uncertainty associated with this kind of generation. To that, initially the approach of the hybrid symbolic regression algorithm is carried out by means of which the proposed wind forecasts will be made; making a description of how it works. Subsequently, the implementation of the algorithm proposed in four case studies located in coastal areas and on islands is carried out, so that historical meteorological data are available with which to carry out the algorithm tests. After this, the errors obtained will be evaluated to select an amount of data for training and testing the algorithm.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Eléctrica
dc.publisherDepartamento de Ingeniería Eléctrica y Electrónica
dc.publisherFacultad de Ingeniería
dc.publisherBogotá
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
dc.relation[1] C. Arriagada, “ANALISIS COMPARATIVO DE LA GOBERNABILIDAD DE MERCADOS DE GENERACION ELECTRICA,” PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE ESCUELA DE INGENIERIA, 1998.
dc.relation[2] M. Deveci, E. Ozcan, and R. John, “Offshore wind farms: A fuzzy approach to site selection in a black sea region,” 2020 IEEE Texas Power Energy Conf. TPEC 2020, 2020, doi: 10.1109/TPEC48276.2020.9042530.
dc.relation[3] U. Cali, N. Erdogan, S. Kucuksari, and M. Argin, “TECHNO-ECONOMIC analysis of high potential offshore wind farm locations in Turkey,” Energy Strateg. Rev., vol. 22, no. November 2017, pp. 325–336, 2018, doi: 10.1016/j.esr.2018.10.007.
dc.relation[4] L. A. Barroso and A. J. Conejo, Decision making under uncertainty in electricity markets. 2006.
dc.relation[5] Y. Zhao, L. Ye, Z. Li, X. Song, Y. Lang, and J. Su, “A novel bidirectional mechanism based on time series model for wind power forecasting,” Appl. Energy, vol. 177, pp. 793–803, 2016, doi: https://doi.org/10.1016/j.apenergy.2016.03.096.
dc.relation[6] GWEC, “Global wind energy council report 2018,” Wind Glob. Counc. Energy, no. April, pp. 1–61, 2019, [Online]. Available: www.gwec.net.
dc.relation[7] A. E. Saleh, M. S. Moustafa, K. M. Abo-Al-Ez, and A. A. Abdullah, “A hybrid neuro-fuzzy power prediction system for wind energy generation,” Int. J. Electr. Power Energy Syst., vol. 74, pp. 384–395, 2016, doi: 10.1016/j.ijepes.2015.07.039.
dc.relation[8] A. M. Foley, P. G. Leahy, A. Marvuglia, and E. J. McKeogh, “Current methods and advances in forecasting of wind power generation,” Renew. Energy, vol. 37, no. 1, pp. 1–8, 2012, doi: 10.1016/j.renene.2011.05.033.
dc.relation[9] M. Á. Vanegas Ramos, “Implementación de modelos locales en el espacio de fase para el pronóstico de variables hidrometeorológicas a partir de series de tiempo,” p. 398, 2011.
dc.relation[10] G. Riahy and M. Abedi, “Short term wind speed forecasting for wind turbine applications using linear prediction method,” Renew. Energy, vol. 33, pp. 35–41, Jan. 2008, doi: 10.1016/j.renene.2007.01.014.
dc.relation[11] Z. Lin and X. Liu, “Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network,” Energy, vol. 201, p. 117693, 2020, doi: https://doi.org/10.1016/j.energy.2020.117693.
dc.relation[12] C. Wan, Y. Song, Z. Xu, G. Yang, and A. H. Nielsen, “Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks,” Electr. Power Components Syst., vol. 44, no. 15, pp. 1656–1668, 2016, doi: 10.1080/15325008.2016.1198437.
dc.relation[13] S. Salcedo-Sanz, E. G. Ortiz-García, Á. M. Pérez-Bellido, A. Portilla-Figueras, and L. Prieto, “Short term wind speed prediction based on evolutionary support vector regression algorithms,” Expert Syst. Appl., vol. 38, no. 4, pp. 4052–4057, 2011, doi: 10.1016/j.eswa.2010.09.067.
dc.relation[14] A. Marvuglia and A. Messineo, “Monitoring of wind farms’ power curves using machine learning techniques,” Appl. Energy, vol. 98, no. May, pp. 574–583, 2012, doi: 10.1016/j.apenergy.2012.04.037.
dc.relation[15] V. Veloso de Melo and W. Banzhaf, “Automatic feature engineering for regression models with machine learning: An evolutionary computation and statistics hybrid,” Inf. Sci. (Ny)., vol. 430–431, no. November, pp. 287–313, 2018, doi: 10.1016/j.ins.2017.11.041.
dc.relation[16] C. Wan, Z. Xu, P. Pinson, Z. Y. Dong, and K. P. Wong, “Probabilistic forecasting of wind power generation using extreme learning machine,” IEEE Trans. Power Syst., vol. 29, no. 3, pp. 1033–1044, 2014, doi: 10.1109/TPWRS.2013.2287871.
dc.relation[17] Y. Zhang, K. Liu, L. Qin, and X. An, “Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods,” Energy Convers. Manag., vol. 112, pp. 208–219, 2016, doi: 10.1016/j.enconman.2016.01.023.
dc.relation[18] S. An, H. Shi, Q. Hu, X. Li, and J. Dang, “Fuzzy rough regression with application to wind speed prediction,” Inf. Sci. (Ny)., vol. 282, pp. 388–400, 2014, doi: 10.1016/j.ins.2014.03.090.
dc.relation[19] G. Santamaría-Bonfil, A. Reyes-Ballesteros, and C. Gershenson, “Wind speed forecasting for wind farms: A method based on support vector regression,” Renew. Energy, vol. 85, pp. 790–809, 2016, doi: 10.1016/j.renene.2015.07.004.
dc.relation[20] F. Andrés and A. Rodríguez, “Tendencias recientes en el pronóstico de velocidad de viento para generación eólica,” Universidad Nacional de Colombia, 2017.
dc.relation[21] C. Wan, Z. Xu, P. Pinson, Z. Y. Dong, and K. P. Wong, “Optimal prediction intervals of wind power generation,” IEEE Trans. Power Syst., vol. 29, no. 3, pp. 1166–1174, 2014, doi: 10.1109/TPWRS.2013.2288100.
dc.relation[22] H. Demolli, A. S. Dokuz, A. Ecemis, and M. Gokcek, “Wind power forecasting based on daily wind speed data using machine learning algorithms,” Energy Convers. Manag., vol. 198, no. July, p. 111823, 2019, doi: 10.1016/j.enconman.2019.111823.
dc.relation[23] T. G. Barbounis, J. B. Theocharis, M. C. Alexiadis, and P. S. Dokopoulos, “Long-term wind speed and power forecasting using local recurrent neural network models,” IEEE Trans. Energy Convers., vol. 21, no. 1, pp. 273–284, 2006, doi: 10.1109/TEC.2005.847954.
dc.relation[24] C. Potter and M. Negnevitsky, “Very short-term wind forecasting for tasmanian power generation,” 2006 IEEE Power Eng. Soc. Gen. Meet. PES, vol. 21, no. 2, pp. 965–972, 2006, doi: 10.1109/pes.2006.1709044.
dc.relation[25] G. Giebel and E. Al., “The State of the Art in Short-Term Prediction of Wind Power,” ANEMOS.plus, no. January, pp. 1–110, 2011, doi: 10.13140/RG.2.1.2581.4485.
dc.relation[26] IEEE PES, “Open Data Sets « IEEE PES Intelligent Systems Subcommittee.” [Online]. Available: https://site.ieee.org/pes-iss/data-sets/.
dc.relation[27] NASA, “NASA POWER - Prediction Of Worldwide Energy Resources.” 2019, [Online]. Available: https://power.larc.nasa.gov/data-access-viewer/.
dc.relation[28] “Solcast, 2019,” Global solar irradiance data and PV system power output data. https://solcast.com/.
dc.relation[29] “WunderMap® | Interactive Weather Map and Radar | Weather Underground.” 2019, [Online]. Available: https://www.wunderground.com.
dc.relation[30] W. B. Langdon, R. Poli, N. F. McPhee, and J. R. Koza, “Genetic programming: An introduction and tutorial, with a survey of techniques and applications,” in Studies in Computational Intelligence, vol. 115, 2008, pp. 927–1028.
dc.relation[31] S. Nguyen, M. Zhang, D. Alahakoon, and K. C. Tan, “Visualizing the evolution of computer programs for genetic programming [Research Frontier],” IEEE Comput. Intell. Mag., vol. 13, no. 4, pp. 77–94, 2018, doi: 10.1109/MCI.2018.2866731.
dc.relation[32] I. Icke and J. C. Bongard, “Improving genetic programming based symbolic regression using deterministic machine learning,” 2013 IEEE Congr. Evol. Comput. CEC 2013, no. June, pp. 1763–1770, 2013, doi: 10.1109/CEC.2013.6557774.
dc.relation[33] J. Žegklitz and P. Pošík, “Symbolic regression in dynamic scenarios with gradually changing targets,” Appl. Soft Comput. J., vol. 83, p. 105621, 2019, doi: 10.1016/j.asoc.2019.105621.
dc.relation[34] S. Chatterjee and A. S. Hadi, Regression Analysis by Example, 5th Editio. Wiley, 2013.
dc.relation[35] E. Cortés Pérez, A. Nuñez Rodríguez, R. E. Moreno De La Torre, O. Lastres Danguillecourt, and J. R. Dorrego Portela, “Forecast of Wind Speed with a Backpropagation Artificial Neural Network in the Isthmus of Tehuantepec Region in the State of Oaxaca, Mexico.,” Acta Univ., vol. 22, pp. 7–14, Dec. 2012, [Online]. Available: https://www.redalyc.org/articulo.oa?id=41623190001.
dc.relation[36] C. A. Martínez, “Problemas abiertos en la aplicación de la Regresión Simbólica en el pronóstico de series de tiempo,” Nacional de Colombia Sede Medellín, 2011.
dc.relation[37] E. Pérez, D. Bautista, F. Acevedo, and J. Pimentel, “Programación Genética Aplicada al Pronóstico de Viento en la Región del Istmo de Tehuantepec.,” 2° Congr. Int. Energías Renov., vol. 1, pp. 1–11, 2016, [Online]. Available: http://www.unistmo.edu.mx/~neto_144/papers/CIER_2016_Articulo.pdf.
dc.relation[38] J. Garcia, D. Alvarez, and S. Rivera, “Ensemble based optimization for electric demand forecast: Genetic programming and heuristic algorithms,” Rev. Int. Metod. Numer. para Calc. y Disen. en Ing., vol. 36, no. 3, pp. 1–12, 2020, doi: 10.23967/j.rimni.2020.07.001.
dc.relation[39] S. Manrique-Naranjo, M. Guzman, and S. Rodriguez, “Hybrid inference algorithm by combining genetic programming methods and nonlinear regression techniques,” no. June, 2018, doi: 10.17654/ETAI070010001. [40] A. Patelli, Genetic programming techniques for nonlinear systems identification, Rum Ed. 2011.
dc.relation[41] J. R. Koza, “Genetic programming as a means for programming computers by natural selection,” Stat. Comput., vol. 4, no. 2, pp. 87–112, 1994, doi: 10.1007/BF00175355.
dc.relation[42] J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge: Cambridge University Press, 2004.
dc.relation[43] S. Lumbreras and A. Ramos, “Offshore wind farm electrical design: a review,” Wind Energy, vol. 16, no. 3, pp. 459–473, Apr. 2013, doi: https://doi.org/10.1002/we.1498.
dc.relation[44] J. Tambke, M. Lange, U. Focken, J. O. Wolff, and J. A. T. Bye, “Forecasting offshore wind speeds above the North Sea,” Wind Energy, vol. 8, no. 1, pp. 3–16, 2005, doi: 10.1002/we.140.
dc.relation[45] J. Liu, X. Wang, and Y. Lu, “A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system,” Renew. Energy, vol. 103, pp. 620–629, 2017, doi: 10.1016/j.renene.2016.10.074.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titlePronóstico de velocidad de viento para generación eólica Offshore basado en programación genética
dc.typeTrabajo de grado - Maestría


Este ítem pertenece a la siguiente institución