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Method of Four Moments Mixture-A new approach for parametric estimation of Weibull Probability Distribution for wind potential estimation applications

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  • Sumair, Muhammad
  • Aized, Tauseef
  • Aslam Bhutta, Muhammad Mahmood
  • Siddiqui, Farrukh Arsalan
  • Tehreem, Layba
  • Chaudhry, Abduallah

Abstract

Cutting down the reliance on fossil fuels and utilization of wind energy as green energy source requires detailed resource exploration using some probability distribution. In contrast to literature methods which are based on first and second moment of Weibull Probability Distribution (WPD) for its parametric estimation, the new method proposed in this study (called Method of Four Moments Mixture, MFMM) combines the effect of first four moments of WPD. This model is based on the squared deviation (deviation of sample from population) of first four moments; minimization of which using Nelder-Mead algorithm estimates parameters of WPD. In order to assess its comprehensive effectiveness, this method has been validated using large dataset i.e. five years wind data measured at 50 m height for thirty-six stations (in Pakistan) for parametric estimation and it has been compared with six past methods using MAPE, RMSE and R2. To rank all seven methods, Global Performance Indicator (GPI) was evaluated and it was found that MFMM is the best method for all stations. Therefore, it can be effectively used in wind resource assessment of various geographical regions in the world.

Suggested Citation

  • Sumair, Muhammad & Aized, Tauseef & Aslam Bhutta, Muhammad Mahmood & Siddiqui, Farrukh Arsalan & Tehreem, Layba & Chaudhry, Abduallah, 2022. "Method of Four Moments Mixture-A new approach for parametric estimation of Weibull Probability Distribution for wind potential estimation applications," Renewable Energy, Elsevier, vol. 191(C), pages 291-304.
    • Handle: RePEc:eee:renene:v:191:y:2022:i:c:p:291-304
    DOI: 10.1016/j.renene.2022.04.054
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    References listed on IDEAS

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    1. Jowder, Fawzi A.L., 2009. "Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain," Applied Energy, Elsevier, vol. 86(4), pages 538-545, April.
    2. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    3. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    4. Mirhosseini, M. & Sharifi, F. & Sedaghat, A., 2011. "Assessing the wind energy potential locations in province of Semnan in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 449-459, January.
    5. Shoaib, Muhammad & Siddiqui, Imran & Amir, Yousaf Muhammad & Rehman, Saif Ur, 2017. "Evaluation of wind power potential in Baburband (Pakistan) using Weibull distribution function," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1343-1351.
    6. Chang, Tsang-Jung & Wu, Yu-Ting & Hsu, Hua-Yi & Chu, Chia-Ren & Liao, Chun-Min, 2003. "Assessment of wind characteristics and wind turbine characteristics in Taiwan," Renewable Energy, Elsevier, vol. 28(6), pages 851-871.
    7. Hasumi, Tomohiro & Akimoto, Takuma & Aizawa, Yoji, 2009. "The Weibull–log Weibull distribution for interoccurrence times of earthquakes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(4), pages 491-498.
    8. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
    9. Celik, Ali N. & Kolhe, Mohan, 2013. "Generalized feed-forward based method for wind energy prediction," Applied Energy, Elsevier, vol. 101(C), pages 582-588.
    10. Usta, Ilhan, 2016. "An innovative estimation method regarding Weibull parameters for wind energy applications," Energy, Elsevier, vol. 106(C), pages 301-314.
    11. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
    12. He, Gang & Kammen, Daniel M., 2014. "Where, when and how much wind is available? A provincial-scale wind resource assessment for China," Energy Policy, Elsevier, vol. 74(C), pages 116-122.
    13. Srivastava, S. K. & Gaur, A. & Singh, O. P. & Tiwari, R. N., 1995. "Comparison of methods for estimating daily and hourly diffuse solar radiation," Applied Energy, Elsevier, vol. 51(2), pages 119-123.
    14. Khan, Komal S. & Tariq, Muhammad, 2018. "Wind resource assessment using SODAR and meteorological mast – A case study of Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2443-2449.
    15. Khahro, Shahnawaz Farhan & Tabbassum, Kavita & Mahmood Soomro, Amir & Liao, Xiaozhong & Alvi, Muhammad Bux & Dong, Lei & Manzoor, M. Farhan, 2014. "Techno-economical evaluation of wind energy potential and analysis of power generation from wind at Gharo, Sindh Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 460-474.
    16. 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.
    17. Akdag, S.A. & Bagiorgas, H.S. & Mihalakakou, G., 2010. "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean," Applied Energy, Elsevier, vol. 87(8), pages 2566-2573, August.
    Full references (including those not matched with items on IDEAS)

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