(i) All are polynomial models (ii) Do not follow curvature of power curve (iii) Accuracy is poor (iv) Mostly applied for power prediction for sizing of wind based systems
(i) Accurate for the particular data set (ii) High-order polynomials may not represent the variance of data (iii) Applied for sizing of hybrid wind, PV, and biodiesel based system
(i) Considers inflection point on curve (ii) Follows shape of curve more accurately than polynomial models (iii) Simple method (iv) Applied for economic load dispatch, reliability analysis, and multiple turbines with and without correlation
4PL curve Parameter estimation: Technique: least squares and MLM Algorithm: ES
Actual data of wind farm
AE, RE
(i) Least square better than MLM method (ii) -NN outperforms other nonparametric models (iii) MLP best accuracy (iv) Residual and control chart approach for online monitoring is presented (v) 4PL least squares and -NN models proposed for detecting anomalies
Data mining: -NN, MLP, random forest, M5P tree, and boosting tree
4PL curve Parameter estimation: Technique: least squares Algorithm: ES Data mining: -NN, MLP, REP tree, M5P tree, and bagging tree
Manufacturer’s curve Actual data of wind farm
MAE, AE, RE, and mean relative error
(i) -NN outperforms other nonparametric models (ii) Outlier filtering by residual approach and control chart (iii) Application for power prediction and online monitoring is suggested
(i) ANN and NN perform best (ii) -NN worst performance (iii) Effect of wind direction, temperature consideration produces less errors (iv) Application in online monitoring