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

Author

Listed:
  • Mónica Borunda

    (CONACYT—Instituto Nacional de Electricidad y Energías Limpias, Cuernavaca, Morelos 62490, Mexico)

  • Katya Rodríguez-Vázquez

    (Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico)

  • Raul Garduno-Ramirez

    (Instituto Nacional de Electricidad y Energías Limpias, Cuernavaca, Morelos 62490, Mexico)

  • Javier de la Cruz-Soto

    (CONACYT—Instituto Nacional de Electricidad y Energías Limpias, Cuernavaca, Morelos 62490, Mexico)

  • Javier Antunez-Estrada

    (Instituto Nacional de Electricidad y Energías Limpias, Cuernavaca, Morelos 62490, Mexico)

  • Oscar A. Jaramillo

    (Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco, Morelos 62580, Mexico)

Abstract

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.

Suggested Citation

  • Mónica Borunda & Katya Rodríguez-Vázquez & Raul Garduno-Ramirez & Javier de la Cruz-Soto & Javier Antunez-Estrada & Oscar A. Jaramillo, 2020. "Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming," Energies, MDPI, vol. 13(8), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:1885-:d:344819
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    References listed on IDEAS

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    Cited by:

    1. Luis Lopez & Ingrid Oliveros & Luis Torres & Lacides Ripoll & Jose Soto & Giovanny Salazar & Santiago Cantillo, 2020. "Prediction of Wind Speed Using Hybrid Techniques," Energies, MDPI, vol. 13(23), pages 1-13, November.
    2. Gomez, William & Wang, Fu-Kwun & Lo, Shih-Che, 2024. "A hybrid approach based machine learning models in electricity markets," Energy, Elsevier, vol. 289(C).

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