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<p>This study analyzed the wind speed distribution in the state of Piauí, Brazil, aiming to estimate its wind energy generation potential. Hourly wind speed data measured at 10&#xa0;m above ground level from 2010 to 2023 were analyzed. The mean wind speed ranged from 4.0 to 10.0&#xa0;m s⁻¹, with higher values observed in the coastal zone and eastern region of the state. Several probability distributions (Weibull, Gamma, Lognormal, Logistic, and Normal) were fitted to the data, with the Weibull distribution showing the best performance based on the Akaike (AIC) and Bayesian (BIC) information criteria across all stations. The AIC and BIC values for the Weibull model ranged from 500 to 2000. The Weibull shape (k) and scale (c) parameters varied between 1.5 and 5.0 and 4.0–13.0&#xa0;m s⁻¹, respectively. Estimated wind power density (WPD) values ranged from 50 to 400&#xa0;W m⁻², indicating moderate to high wind energy potential in specific areas. Spatial analysis using inverse distance weighting (IDW) interpolation revealed favorable regions for wind farm deployment. These results provide quantitative support for sustainable energy planning and offer strategic insights for renewable energy development in Piauí.</p>

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Assessment of wind speed distribution and wind power potential in Piauí, Northeastern Brazil

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月9日

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  • Kerolly Kedma Felix do Nascimento,
  • Fernando Henrique Antunes de Araujo,
  • Artur Mendes Medeiros,
  • Priscila Alves Barroso,
  • Fábio Sandro dos Santos

摘要

This study analyzed the wind speed distribution in the state of Piauí, Brazil, aiming to estimate its wind energy generation potential. Hourly wind speed data measured at 10 m above ground level from 2010 to 2023 were analyzed. The mean wind speed ranged from 4.0 to 10.0 m s⁻¹, with higher values observed in the coastal zone and eastern region of the state. Several probability distributions (Weibull, Gamma, Lognormal, Logistic, and Normal) were fitted to the data, with the Weibull distribution showing the best performance based on the Akaike (AIC) and Bayesian (BIC) information criteria across all stations. The AIC and BIC values for the Weibull model ranged from 500 to 2000. The Weibull shape (k) and scale (c) parameters varied between 1.5 and 5.0 and 4.0–13.0 m s⁻¹, respectively. Estimated wind power density (WPD) values ranged from 50 to 400 W m⁻², indicating moderate to high wind energy potential in specific areas. Spatial analysis using inverse distance weighting (IDW) interpolation revealed favorable regions for wind farm deployment. These results provide quantitative support for sustainable energy planning and offer strategic insights for renewable energy development in Piauí.