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Wind speed pattern and the available wind power at Basotu, Tanzania

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  • Kainkwa, R.M.R.

Abstract

This paper examines the wind speed pattern and the available wind power at Basotu, Tanzania. Wind speed data for two different sessions reveals that the windy season, is from June to November, with October being the windiest month. The average monthly wind power from June to November is at least 114 W/m2. The shortfall of hydroelectricity that prevails during the dry season is normally supplemented from fossil fuel generators. It is fortunate that the windy season coincides with the dry season. It is urged that the use of wind turbines to supplement the shortfall of hydroelectricity during the dry season would offset the emission of greenhouse gases from fossil fuel generators that degrade our aerial environment.

Suggested Citation

  • Kainkwa, R.M.R., 2000. "Wind speed pattern and the available wind power at Basotu, Tanzania," Renewable Energy, Elsevier, vol. 21(2), pages 289-295.
    • Handle: RePEc:eee:renene:v:21:y:2000:i:2:p:289-295
    DOI: 10.1016/S0960-1481(00)00076-8
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    References listed on IDEAS

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    1. Njau, Ernest C., 1997. "Velocity characteristics of wind patterns in Tanzania," Renewable Energy, Elsevier, vol. 11(1), pages 37-46.
    2. Kainkwa, R.R, 1999. "Wind energy as an alternative source to alleviate the shortage of electricity that prevails during the dry season: a case study of Tanzania," Renewable Energy, Elsevier, vol. 18(2), pages 167-174.
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    Citations

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    Cited by:

    1. Dorvlo, A.S.S & Ampratwum, D.B, 2002. "Wind energy potential for Oman," Renewable Energy, Elsevier, vol. 26(3), pages 333-338.
    2. Cancino-Solórzano, Yoreley & Xiberta-Bernat, Jorge, 2009. "Statistical analysis of wind power in the region of Veracruz (Mexico)," Renewable Energy, Elsevier, vol. 34(6), pages 1628-1634.
    3. Mazzeo, Domenico & Oliveti, Giuseppe & Labonia, Ester, 2018. "Estimation of wind speed probability density function using a mixture of two truncated normal distributions," Renewable Energy, Elsevier, vol. 115(C), pages 1260-1280.
    4. Wang, Jianzhou & Qin, Shanshan & Jin, Shiqiang & Wu, Jie, 2015. "Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 26-42.
    5. Islam, M.R. & Saidur, R. & Rahim, N.A., 2011. "Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function," Energy, Elsevier, vol. 36(2), pages 985-992.
    6. Himri, Y. & Rehman, S. & Draoui, B. & Himri, S., 2008. "Wind power potential assessment for three locations in Algeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2495-2504, December.
    7. Ko, Kyungnam & Kim, Kyoungbo & Huh, Jongchul, 2010. "Variations of wind speed in time on Jeju Island, Korea," Energy, Elsevier, vol. 35(8), pages 3381-3387.
    8. Ahmed, Ahmed Shata, 2010. "Wind energy as a potential generation source at Ras Benas, Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(8), pages 2167-2173, October.
    9. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    10. Weisser, D, 2003. "A wind energy analysis of Grenada: an estimation using the ‘Weibull’ density function," Renewable Energy, Elsevier, vol. 28(11), pages 1803-1812.

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