*6.1. Case Study*

Figures 5 and 6 show the hourly wind speed and solar radiation data. However, due to the instability of wind energy and the tendency of wind density to change, this brings certain challenges to research. In order to ensure that the energy generated by the system can be balanced with the load demand. In this paper, we use the Artificial Neural Networks(ANN) to forecast the wind power. Figure 7 shows the regression graph obtained using ANN training test data including wind speed and solar radiation. We see that 0.96316 in the 40th iteration, which demonstrates a high correlation between the results obtained after training and the target. In addition, most of the results generated by the training data are related to the best fit line.

**Figure 5.** Annual wind speed of the studied location.

**Figure 6.** Annual solar irradiance of the location.

**Figure 7.** Regression of wind power ANN.
