## 3 Results  
In the study, average wind power was predicted using the daily average wind speed and wind power values of the Eneron E-92 turbine and the wind speed data of the Velimese region. ANN and ANFIS methods, which are frequently used as forecast models for wind energy forecasts, have been applied. While determining the ANN model, different parameters were evaluated and tested. First, a single-layer ANN model was created, and the network was trained many times using different numbers of neurons and different activation functions. In the created model, the Tansig activation function and the number of neurons in the hidden layer were given as 10, 15, and 20, respectively, and the network was trained and the prediction results were obtained. The prediction results obtained using a single-layer network structure were compared with the actual values, and error performance values were calculated using the RMSE, MAPE, and R\({}^{2}\) methods. The RMSE value of the model using 10 neurons in the hidden layer was 45.31, the MAPE value was 20.27, and the R\({}^{2}\) value was 0.9973; the RMSE value of the model using 15 neurons was 43.41, the MAPE value was 21.70, and the R\({}^{2}\) value was 0.9975; the RMSE value of  
the model using 15 and 20 neurons; the MAPE values were 24.79 for the model using 10 and 15 neurons, 32.89 for the model using 10 and 20 neurons, and 16.22 for the model using 15 and 20 neurons; and the R\({}^{2}\) values were calculated as 0.9981 for the model using 10 and 15 neurons, 0.9990 for the model using 10 and 20 neurons, and 0.9998 for the model using 15 and 20 neurons. Prediction values were produced by retraining the network with the logsig activation function using the same number of neurons. The RMSE values of the models that produced predictive values using two hidden layers were 49.51 for the model with 10 and 15 neurons, 55.59 for the model with 10 and 20 neurons, and 15.92 for the model with 15 and 20 neurons; the MAPE values were 20.42 for the model with 10 and 15 neurons, 25.43 for the model with 10 and 20 neurons, and 7.45 for the model with 15 and 20 neurons; and the R\({}^{2}\) values were calculated as 0.9967 for the model with 10 and 15 neurons, 0.9959 for the model with 10 and 20 neurons, and 0.9997 for the model with 15 and 20 neurons. When the computational results were compared among the two-layer models, the model using the Tansig activation function with 15 and 20 neurons in the hidden layer gave the best results. When the single-layer and two-layer ANN models were compared based on the above, it was seen that the ANN model, in which the Tansig activation function was used and the number of neurons was determined as 15 and 20, produced the closest prediction values to the real values. By determining the training parameters for the ANFIS model, the performance of three membership functions was evaluated, and the closeness of the prediction values to the real values was tested. Triangle, trapezoidal, and Gaussian functions were used as membership functions in the models created with the Sugeno inference system, and the number of functions was determined as 3 for each. After the network was trained, predicted power values were produced for 355 wind speed values. The error performance results for the predicted values were calculated with the RMSE, MAPE, and R\({}^{2}\) methods. The RMSE values for the models using triangular, trapezoidal, and Gaussian membership functions were 41.40, 26.39, and 12.94, respectively; the MAPE values were 19.82, 10.39, and 6.02, and the R\({}^{2}\) values were calculated as 0.9977, 0.9991, and 0.9998. When all membership functions were evaluated among themselves, it turned out that the membership function that produced the values closest to the real values was Gaussian. Considering the results mentioned above, different models belonging to both techniques will be able to predict wind power close to the actual values as long as meteorological data are available. A comparison of both models showed that the difference was not significant.