## 3 Material and Methods
### Dataset and Preprocessing  
The dataset used in this study includes data recorded at 10 min intervals from the Supervisory Control and Data Acquisition (SCADA) system of an operating wind turbine in Turkiye [31]. Features in the dataset are as follows: LV (Low Voltage) Actual Power (kW), which expresses the power produced by the turbine for a certain moment; Wind Speed (m/s), which shows the wind speed used by the turbine for electricity production; Theoretical Power Curve (KWh), which expresses the theoretical power values for that wind speed specified by the turbine manufacturer; and Wind Direction (\({}^{\circ}\)), which shows the wind direction in which the turbine automatically rotates. Sample data from the dataset are given in Table 1.  
In Table 1, each row represents values over a period of time recorded at 10 min intervals. 'Thpower (KWh)' represents the Theoretical Power Curve. How the wind speed, and therefore the production capacity, of the wind turbine change over time is given in Figure 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Date** & **Actual Power (kW)** & **Wind Speed (m/s)** & **Thpower (KWh)** & **Wind Direction (\({}^{\circ}\))** \\ \hline
0 & 1 January 2018 00:00 & 380.047791 & 5.311336 & 416.328908 & 259.994904 \\
1 & 1 January 2018 00:10 & 453.769196 & 5.672167 & 519.917511 & 268.641113 \\
2 & 1 January 2018 00:20 & 306.376587 & 5.216037 & 390.900016 & 272.564789 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample data in the dataset. As seen in Figure 1, it is possible to observe that on some dates, energy production is almost zero, while at other times it is very close to maximum capacity. These fluctuations reflect the natural variability of wind and the difficulties in predicting wind energy production. A correlation heatmap graph was obtained to show the strength and direction of the relationship between the variables in the dataset (Figure 2). Correlation is a statistical metric that quantifies the strength and direction of the association between two variables. The visualization process commences with the application of the 'df.corr()' method, which calculates the Pearson correlation coefficients within the dataset. This computation yields a correlation matrix, delineating the correlation coefficients for every possible pair of variables contained within the dataset. To visualize this matrix, the 'heatmap' function from the seaborn library, a data visualization library that extends'matplotlib', is utilized. 'Seaborn' facilitates the creation of graphs that are not only more aesthetically pleasing but also more comprehensible. Figure 1: Monthly wind energy production changes in 2018 (in kW). Figure 2: Correlation heatmap of wind power generation parameters. The graph presented in Figure 2 is a correlation heatmap generated from a dataset comprising wind energy metrics pertinent to this investigation. The heatmap essentially functions as a matrix that visually encodes the correlation coefficients between disparate data columns through a spectrum of colors. The color intensity on the heatmap indicates the strength of the correlation between pairs of variables: hues approaching dark red signify positive correlations, whereas those approaching dark blue denote negative correlations. The values of correlation coefficients range from \(-1\) t \(-\)o +1, where +1 signifies a perfect positive linear relationship, \(-1\) signifies a perfect negative linear relationship, and 0 indicates no linear relationship exists [19]. Within the context of this study, the variables 'Actualpower', 'Windspeed', Thpower', and 'Winddir' are considered. 'Actualpower' and 'Thpower' denote the wind energy production values; 'Windspeed' is indicative of wind velocity; and 'Winddir' signifies the direction of the wind. The heatmap delineates both the magnitude and direction of the linear correlations among these variables.