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Article

Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming

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CONACYT—Instituto Nacional de Electricidad y Energías Limpias, Cuernavaca, Morelos 62490, Mexico
2
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
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Instituto Nacional de Electricidad y Energías Limpias, Cuernavaca, Morelos 62490, Mexico
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Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco, Morelos 62580, Mexico
*
Author to whom correspondence should be addressed.
Energies 2020, 13(8), 1885; https://doi.org/10.3390/en13081885
Received: 9 March 2020 / Revised: 5 April 2020 / Accepted: 8 April 2020 / Published: 13 April 2020
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
Given the imminent threats of climate change, it is urgent to boost the use of clean energy, being wind energy a potential candidate. Nowadays, deployment of wind turbines has become extremely important and long-term estimation of the produced power entails a challenge to achieve good prediction accuracy for site assessment, economic feasibility analysis, farm dispatch, and system operation. We present a method for long-term wind power forecasting using wind turbine properties, statistics, and genetic programming. First, due to the high degree of intermittency of wind speed, we characterize it with Weibull probability distributions and consider wind speed data of time intervals corresponding to prediction horizons of 30, 25, 20, 15 and 10 days ahead. Second, we perform the prediction of a wind speed distribution with genetic programming using the parameters of the Weibull distribution and other relevant meteorological variables. Third, the estimation of wind power is obtained by integrating the forecasted wind velocity distribution into the wind turbine power curve. To demonstrate the feasibility of the proposed method, we present a case study for a location in Mexico with low wind speeds. Estimation results are promising when compared against real data, as shown by MAE and MAPE forecasting metrics. View Full-Text
Keywords: Wind power forecasting; Weibull distribution; Genetic programming Wind power forecasting; Weibull distribution; Genetic programming
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    Doi: http://doi.org/10.5281/zenodo.3691886
MDPI and ACS Style

Borunda, M.; Rodríguez-Vázquez, K.; Garduno-Ramirez, R.; de la Cruz-Soto, J.; Antunez-Estrada, J.; Jaramillo, O.A. Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming. Energies 2020, 13, 1885. https://doi.org/10.3390/en13081885

AMA Style

Borunda M, Rodríguez-Vázquez K, Garduno-Ramirez R, de la Cruz-Soto J, Antunez-Estrada J, Jaramillo OA. Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming. Energies. 2020; 13(8):1885. https://doi.org/10.3390/en13081885

Chicago/Turabian Style

Borunda, Mónica, Katya Rodríguez-Vázquez, Raul Garduno-Ramirez, Javier de la Cruz-Soto, Javier Antunez-Estrada, and Oscar A. Jaramillo. 2020. "Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming" Energies 13, no. 8: 1885. https://doi.org/10.3390/en13081885

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