Treffer: Wind turbine energy forecasting using real wind farm's measurement data and performance of gene expression programming analytical model in comparison to traditional algorithms.

Forecasting wind power is vital to ensure steady, sustainable, and renewable energy. The complex nonlinear nature of wind flow and its interrelated factors make power prediction challenging. This study predicted wind power curves inspired by the Biologically Inspired Evolutionary Computation (BIEC) paradigm, incorporating Gene Expression Programming (GEP), Artificial Neural Networks (ANN), Least Square Support Vector Machines (LSSVM), and Random Forest (RF) models. Key input parameters include wind speed, yaw azimuth, turbulent intensity, veer, horizontal and vertical shear, ambient temperature, blade pitch, and rotor speed. The study evaluates these models' effectiveness using root mean square error (RMSE) and correlation coefficient R. Results indicate that the GEP model offers transparent modeling, emphasizing critical inputs like wind speed, rotor speed, blade pitch angle, and temperature for wind power prediction. The GEP model identified wind speed, rotor speed, blade pitch angle, and temperature as the most influential parameters, with variable importance index (Ii) values of 69.39%, 24.39%, 5.46%, and 0.64% for training, and 69.37%, 25.03%, 4.98%, and 0.54% for validation. The study demonstrates the GEP model's efficacy in accurately forecasting wind power curve, achieving a high correlation with training and validation coefficients of 0.9899 and 0.994, respectively, outperforming traditional models with minor errors. [ABSTRACT FROM AUTHOR]

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