Applied Science and Convergence Technology 2022; 31(4): 93-98
Published online July 30, 2022
Copyright © The Korean Vacuum Society.
aDivision of Industrial Convergence Systems Engineering, Dongeui University, Busan 47340, Republic of Korea
bDepartment of e-Business, Busan University of Foreign Studies, Busan 46234, Republic of Korea
cDepartment of Systems Management and Engineering, Pukyong National University, Busan 48513, Republic of Korea
Many countries have strived to expand the adoption of renewable power generation in the transition to a low-carbon society. Wind power is recognized as one of the most promising and scalable renewable energy sources for power generation, but the amount of wind power generation is heavily dependent on wind speed. Therefore, techniques that enable the reliable estimation of wind speed have long been under focus. In this study, statistically appropriate probability distribution functions were explored using wind speed measurement data from wind farm sites in the Republic of Korea. In particular, the problem of overfitting was investigated in depth by evaluating the fitness of distributions using different parameters. The suitability of mixed distributions was examined statistically based on the information criteria until suitable distributions were established. The results indicated that monthly wind speed data are a good fit with distinct Weibull distributions; thus, planning for wind power generation in the ROK should consider temporal variations in wind speed as revealed by the distribution analyses.
Keywords: Wind farm, Wind speed, Weibull distribution, Distribution fitting, Goodness-of-Fit