Evaluation of the accuracy of mathematical models through use of multiple metrics

Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

“…Moreover, using the current WT dataset, it was discovered that the NSE and LMI values derived from the RF-based technique were superior to those of other models (Table 3). Moreover, the calculated OAS (ψ) values (6.6967 and 6.4797 for the training and testing sets, respectively) were much closer to 7 [92], suggesting that the RF-based model worked better than other soft-computing-based methods. Furthermore, the RF-based model's AIC values were the lowest across all subgroups, demonstrating its superior predictive accuracy in comparison to alternative modeling strategies.…”

“…Energies 2024, 17, x FOR PEER REVIEW 16 of 41 6.4797 for the training and testing sets, respectively) were much closer to 7 [92], suggesting that the RF-based model worked better than other soft-computing-based methods. Furthermore, the RF-based model's AIC values were the lowest across all subgroups, demonstrating its superior predictive accuracy in comparison to alternative modeling strategies.…”

“…The current computational analysis includes a number of key statistics, such as slope of the best-fit line (herein b or s), intercept (a), determination coefficient (R 2 ), adjusted coefficient of multiple determination (R 2 adj ), mean absolute error (MAE), mean bias error (MBE), mean absolute percentage error (MAPE), root mean squared error (RMSE), systematic and unsystematic RMSE (RMSE S and RMSE U , respectively), proportion of systematic error (PSE), standard error of the estimate (SEE), index of agreement (IA) (or known as Willmott's Index (WI)), fractional variance (FV), the factor of two (FA2), coefficient of variation of RMSE (CV(RMSE) (or known as scattering index (SI) or normalized root mean squared error (NRMSE)), Durbin-Watson statistic (DW), Nash-Sutcliffe efficiency (NSE), Legates and McCabe's index (LMI), mean fractional bias (MFB), mean fractional error (MFE), Akaike information criterion (AIC), t-statistic, and overall accuracy score (ψ) (with varying weighting factors of 3, 1, 1, 1, and 1 for s, R 2 , RMSE, MBE, and MAE, respectively), which were calculated to measure the degree of agreement and to make detailed comparisons between the applied soft-computing techniques for the training, testing, and overall datasets. Detailed descriptions and formulae of these measures (not presented here due to space limitations) can be found in the previous investigations [64][65][66][67][68][89][90][91][92][93][94][95].…”