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Wind Potential Assessment of Polokwane, South Africa, Using Statistical Models for Wind Power Density Estimation.

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Title: Wind Potential Assessment of Polokwane, South Africa, Using Statistical Models for Wind Power Density Estimation.
Authors: Shambira, Ngwarai1 (AUTHOR), Mukumba, Patrick1 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2464. 23p.
Subject Terms: *Statistical models, *Distribution (Probability theory), *Renewable energy sources, *Turbine efficiency, *Wind speed measurement, *Wind power
Geographic Terms: South Africa
Abstract: This study evaluates the wind energy potential of Polokwane, South Africa, using statistical distribution models to estimate wind power density (WPD) and assess turbine performance under low-wind inland conditions. Hourly wind speed and direction data (2015–2024) measured at a 10 m height above ground level (AGL) were analysed to characterise wind behaviour and assess energy availability. Four probability distributions, namely generalised logistic (GLD), generalised extreme value (GEVD), Gumbel (GD), and Weibull (WD), were fitted using the maximum likelihood (ML) method. Model performance was evaluated using Kolmogorov–Smirnov (KS), Anderson–Darling (AD), and Chi-square (χ 2) tests, while wind power density accuracy was assessed using wind power density error (WPDE). The results showed that Polokwane is characterised by low wind speeds, with an overall mean wind speed of 2.72 m/s at 10 m AGL, reaching a low of 3.88 m/s at a hub height of 125 m. The GEVD model produced the most accurate wind power density estimate of 32.37 W/m2, classifying the site within the poor wind resource category. Wind direction analysis revealed a dominant northeast sector with seasonal shifts toward the south. Wind turbine performance analysis showed improved energy generation at higher hub heights, with the Gamesa G136-4.5 MW turbine identified as the most suitable option for the site, achieving the highest net annual energy production (AEP) of 10.82 GWh/yr and the highest net capacity factor (CF) of 27.44%. These results indicate that the Polokwane site is suitable for low-to-moderate wind energy applications and small-scale distributed wind generation rather than large-scale commercial wind farm development. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141578
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  Data: Wind Potential Assessment of Polokwane, South Africa, Using Statistical Models for Wind Power Density Estimation.
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2464. 23p.
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  Data: *<searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br />*<searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br />*<searchLink fieldCode="DE" term="%22Turbine+efficiency%22">Turbine efficiency</searchLink><br />*<searchLink fieldCode="DE" term="%22Wind+speed+measurement%22">Wind speed measurement</searchLink><br />*<searchLink fieldCode="DE" term="%22Wind+power%22">Wind power</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22South+Africa%22">South Africa</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study evaluates the wind energy potential of Polokwane, South Africa, using statistical distribution models to estimate wind power density (WPD) and assess turbine performance under low-wind inland conditions. Hourly wind speed and direction data (2015–2024) measured at a 10 m height above ground level (AGL) were analysed to characterise wind behaviour and assess energy availability. Four probability distributions, namely generalised logistic (GLD), generalised extreme value (GEVD), Gumbel (GD), and Weibull (WD), were fitted using the maximum likelihood (ML) method. Model performance was evaluated using Kolmogorov–Smirnov (KS), Anderson–Darling (AD), and Chi-square (χ 2) tests, while wind power density accuracy was assessed using wind power density error (WPDE). The results showed that Polokwane is characterised by low wind speeds, with an overall mean wind speed of 2.72 m/s at 10 m AGL, reaching a low of 3.88 m/s at a hub height of 125 m. The GEVD model produced the most accurate wind power density estimate of 32.37 W/m2, classifying the site within the poor wind resource category. Wind direction analysis revealed a dominant northeast sector with seasonal shifts toward the south. Wind turbine performance analysis showed improved energy generation at higher hub heights, with the Gamesa G136-4.5 MW turbine identified as the most suitable option for the site, achieving the highest net annual energy production (AEP) of 10.82 GWh/yr and the highest net capacity factor (CF) of 27.44%. These results indicate that the Polokwane site is suitable for low-to-moderate wind energy applications and small-scale distributed wind generation rather than large-scale commercial wind farm development. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19102464
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
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        PageCount: 23
        StartPage: 2464
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      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Distribution (Probability theory)
        Type: general
      – SubjectFull: Renewable energy sources
        Type: general
      – SubjectFull: Turbine efficiency
        Type: general
      – SubjectFull: Wind speed measurement
        Type: general
      – SubjectFull: Wind power
        Type: general
      – SubjectFull: South Africa
        Type: general
    Titles:
      – TitleFull: Wind Potential Assessment of Polokwane, South Africa, Using Statistical Models for Wind Power Density Estimation.
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            NameFull: Shambira, Ngwarai
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            NameFull: Mukumba, Patrick
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            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
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              Value: 19961073
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              Value: 19
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              Value: 10
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            – TitleFull: Energies (19961073)
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