A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications.

One of the green, clean, and environment-friendly sources of energy is wind energy. For the assessment of wind energy potential, the parameters of the probability distribution function (PDF), i.e., Weibull distribution (WD), that fits well with the wind speed data must be known. In this research, we proposed a novel optimized energy pattern factor method (NOEPFM) based on the trust-region-dogleg algorithm and applied it to wind speed data of four cities of the Southern region of Punjab, Pakistan, to determine WD parameters, i.e., shape k and scale c parameters. In order to authenticate the practicability of the proposed NOEPFM, it is compared with the other existing energy pattern factor (EPF)-based methods such as the energy pattern factor method (EPFM), Sathyajith's EPFM (EPFMS), and novel EPFM (NEPFM). The performance of NOEPFM is measured in terms of five goodness-of-fit indices, namely root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), coefficient of efficiency (CoE), and maximum absolute error (MaxAE). Numerical results reveal that the NOEPFM method was the best fit compared to the other EPFMs for all the considered wind speed datasets. This justifies the workability of the proposed NOEPFM and can serve as an enhanced approach for calculating wind power potential.