## 5 Results and Discussion  
### Expected Wind Energy Production  
In this part, wind rose software assumes the installation of the wind turbine rather than the meteorological mast in order to calculate the expected energy production of the wind turbine based on the measured data and its power curve. Accordingly, the expected energy production produced by the software is 4,235,202 kWh and the capacity factor is 25.9%. The results also show the annual energy production with the capacity factors based on the time distribution and Weibull distribution. Figure 3 shows that the relationship between the standard deviation and the average wind speed is proportional; the average wind speed is increasing, and the standard deviation is increasing as well. In all wind parks, the relationship between the standard deviation and the average wind speed is proportional, which means that when the average wind speed is increasing, then the standard deviation is increasing as well as shown in Figure 3. Standard deviation plays an important role in the assessment of the uncertainty in the prediction of wind farm energy production, where the relative uncertainty of wind speed is 4%. The energy production in a wind park follows a stochastic principle and requires a statistical analysis in which production estimates should be associated with occurrences probabilities. An uncertainty analysis is often performed as part of a wind farm energy yield assessment. Figure 3: Standard deviation as a function of average wind speed at 10 m above ground level. Figure 4 shows the relationship between the average wind speed and the wind direction. In the wind sectors, WSW and ENE appear the highest average wind speed as well as more frequent data which means the first best sector in energy and time distribution is WSW, while the second best sector in energy is W and the second best sector in time distribution is ENE. Based on the wind data collected, the annual energy production of each wind park is the total hourly energy production, result of the setting of the data logger, which is usually justified in small periods for avoiding and faults and achieving more accuracy. Usually, the selected location and hub height, shows the monthly average wind speed for each month of the year and lists the specific month that has the highest monthly value as well as the lowest monthly value, in the calculation of the energy production, avoided any high speed and any very small speed (within the range of the operation speeds of each wind turbine, which is between the speeds cut and the speed in). Figure 5 shows that the highest average wind speed in KTD wind mast is 6.36 m/s in August while the lowest average wind speed is 3.54 m/s in December. Figure 6 shows the evaluation of the daily mean wind speed, as well as, the maximum wind speed occurred per day, for the given time-step i.e., if 10 min-series are recorded, the maximum value is of course, not the instantaneous one second gust, but the maximum 10 mins wind speed that occurred during this day. Figure 4: Wind speed at 10 m agl vs wind direction. Figure 5: Wind speed variation per month. Figure 7 presents the Diurnal distribution; this is the distribution of the wind speed as a function of the hour of the day. In this study, there are 52704 data sets available (6 times per hour \(\times\) 24 h \(\times\) 366 days) which have been recorded during one leap year. They are represented in 10 min readings. However, the total valid data during the measurement period is lowered to 4,9098 data sets during 340.95 days of the year, while the calm wind speed, measuring less than 2 m/s, is 0%. The missing data in the examined time frame is 3605 samples representing 6.8% from all samples, where all the missing data was in June. Thus. The expected wind turbine energy production based on the available data is 4,234,202 kWh while the calculated annual energy production after correlation is 4,536,933 kWh, where the value of the annual energy production is the projection of the data distributions to 1-year.