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Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data

Author

Listed:
  • Monica Borunda

    (Centro Nacional de Investigación y Desarrollo Tecnológico, Tecnológico Nacional de México, Cuernavaca 62490, Morelos, Mexico
    Consejo Nacional de Humanidades, Ciencias y Tecnologías, Mexico City 03940, Mexico
    Faculty of Science, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico)

  • Adrián Ramírez

    (Faculty of Science, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico)

  • Raul Garduno

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

  • Carlos García-Beltrán

    (Centro Nacional de Investigación y Desarrollo Tecnológico, Tecnológico Nacional de México, Cuernavaca 62490, Morelos, Mexico)

  • Rito Mijarez

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

Abstract

Wind power is an important energy source that can be used to supply clean energy and meet current energy needs. Despite its advantages in terms of zero emissions, its main drawback is its intermittency. Deterministic approaches to forecast wind power generation based on the annual average wind speed are usually used; however, statistical treatments are more appropriate. In this paper, an intelligent statistical methodology to forecast annual wind power is proposed. The seasonality of wind is determined via a clustering analysis of monthly wind speed probabilistic distribution functions (PDFs) throughout n years. Subsequently, a methodology to build the wind resource typical year (WRTY) for the n + 1 year is introduced to characterize the resource into the so-called statistical seasons (SSs). Then, the wind energy produced at each SS is calculated using its PDFs. Finally, the forecasted annual energy for the n + 1 year is given as the sum of the produced energies in the SSs. A wind farm in Mexico is chosen as a case study. The SSs, WRTY, and seasonal and annual generated energies are estimated and validated. Additionally, the forecasted annual wind energy for the n + 1 year is calculated deterministically from the n year. The results are compared with the measured data, and the former are more accurate.

Suggested Citation

  • Monica Borunda & Adrián Ramírez & Raul Garduno & Carlos García-Beltrán & Rito Mijarez, 2023. "Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data," Energies, MDPI, vol. 16(23), pages 1-34, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7915-:d:1293934
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    References listed on IDEAS

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    Cited by:

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    2. Javier Sánchez-Soriano & Pedro Jose Paniagua-Falo & Carlos Quiterio Gómez Muñoz, 2025. "Historical Hourly Information of Four European Wind Farms for Wind Energy Forecasting and Maintenance," Data, MDPI, vol. 10(3), pages 1-14, March.

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