# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 6 Results and Analysis
### Wind Turbine Power Curve Modeling and Applications  
The wind turbine power curve is an essential characteristic of the turbine, as it describes the relationship between hub height wind speed and the generated wind power, as shown in Figure 20A. Precise models of wind turbine power curves have a crucial role in enhancing the performance of the wind energy system and operating the wind energy conversion system [70; 71; 72]. During various wind speeds, the power curve reflects the power response. Therefore, precise models can be applied in several wind energy applications as follows:  
1. Wind power prediction [73; 74].
2. Wind turbines selection [75].
3. Early faults identification and [73].
4. Online capacity factor estimation [76].
5. Online power monitoring [73; 74].  
Wind turbine operators need to accurately forecast the turbine's power output to deliver the traded amount of power directly to the electricity market [77]. Moreover, the prediction of wind output power is required for cost optimization studies, as well as the design and sizing of wind energy systems [78]. Moreover, 10 min ahead of wind speed and power prediction can be used to efficiently adjust wind turbine control to maximize harvesting the wind energy [79]. Capacity factor prediction is needed to choose potential sites, optimum turbine-site selection, sizing, and cost optimization [80]. In addition, predicting the power curve can aid in selecting a suitable turbine [75]. Based on power curves, online monitoring and analysis of the detected outliers can predict power curves early to warn of various faults and anomalies of the turbine, such as pitch system faults or blade faults [81]. Moreover, VSTWPP can be utilized to deal with different sudden scenarios, such as zero wind velocity. Consequently, a system of spare generators would be powered on to compensate for the lost energy to ensure a continuous supply of electrical energy.  
Figure 19: (**A**) Average computational time for WSF, (**B**) average computational time for WPF, (**C**) average computational memory usage for WSF, and (**D**) average computational memory usage for WPF.  
Figure 20A illustrates the standard turbine wind power curve, where the cut-in speed (V\({}_{\rm c}\)) was 4 m/s, the rated wind speed (V\({}_{\rm r}\)) was 14 m/s, and the cut-out speed (V\({}_{\rm f}\)) was 25 m/s for (GAMESA G80-2.0 MW) [82]. Nonparametric methods of power curve modeling, including ANNs or ensemble models, provide superior performance than other models such as clustering, parametric, and probabilistic models [72]. This study uses the Extra Trees model for VSTWSP and the bagging model for VSTWPP. Consequently, both VSTWSP and VSTWPP can be combined to generate 10 min ahead of the wind turbine power curve as shown in Figure 20B. Figure 20B illustrates the predicted wind power curve where the two curves are approximately identical. The measured wind speed and its corresponding wind turbine power produced a reference power curve. During field operation, the expected power curve as shown in Figure 20B, and actual values as shown in Figure 20C can be collected and compared to the benchmark curve. The actual value deviations from the expected output can identify underperformance or faults in wind power systems [83].  
Figure 20: (**A**) Standard power curve, (**B**) 10 min ahead power curve for a single turbine, and (**C**) actual wind power curve.