Wind Energy Potential Approximation with Various Metaheuristic Optimization Techniques Deployment

Wadi M., Elmasry W., Shobole A., Tur M. R., BAYINDIR R., Shahinzadeh H.

7th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2021, Tehran, Iran, 29 - 30 December 2021 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/icspis54653.2021.9729389
  • City: Tehran
  • Country: Iran
  • Keywords: Cumulative Distribution Function (CDF), GWO, Inverse CDF (ICDF), MPA, MVO, Probability Distribution Function (PDF), Wind Energy Approximation
  • Gazi University Affiliated: Yes


© 2021 IEEE.This paper presents a comprehensive empirical study of five different distribution functions to analysis the wind energy potential, namely, Rayleigh, Gamma, Extreme Value, Logistic, and T Location-Scale. In addition, three metaheuristics optimization methods, Grey Wolf Optimization, Marine Predators Algorithm, and Multi-Verse Optimizer are utilized to determine the optimal parameter values of each distribution. To test the accuracy of the introduced distributions and optimization methods, five error measures are investigated and compared such as mean absolute error, root mean square error, regression coefficient, correlation coefficient, and net fitness. To conduct this analysis, the Catalca site in the Marmara region in Istanbul, Republic of Turkey is selected to be the case study. The experimental results confirm that all introduced distributions based on optimization methods are efficient to model wind speed distribution in the selected site. Rayleigh distribution achieved the best matching while Extreme Value distribution provided the worst matching. Finally, many valuable observations drawn from this study are also discussed. MATLAB 2020b and Excel 365 were used to perform this study.