Özet:

The need for renewable energy sources for sustainable development has been increasing every day. One of these sources is wind energy. Due to the stochastic nature of the wind, the estimation of wind speed and wind power has been a subject of great interest to researchers in recent years. In this study, wind power estimation was carried out for a wind turbine in Turkey, using the data set obtained by the SCADA system during 2018 and the meteorological data set shared by NASA for the same location. Wind speed, wind direction, and theoretical power curve were taken from the SCADA system as input variables; Meteorological parameters were taken from the NASA system and historical data of wind power were used. Unnecessary features in the model that cause computational complexity are removed from the model with the wrapper selection method to increase model performance. Genetic Algorithm (GA) was used as the wrapper selection method. In the study, the predictive power of different machine learning algorithms was compared according to different performance criteria and the effect of feature selection on the model was evaluated. In the final model proposed by GA, the number of variables was reduced from 47 to 9, unnecessary variables were removed from the model, and a strong prediction model with R2 value of 0.98 was obtained with the least number of variables.

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