# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 6 Results and Analysis
### Results and Analysis of WPF  
The optimal wind speed forecasting (WSF) models for each time scale were selected based on validation results (Figures 11-15) and integrated into wind power forecasting (WPF). For very short-term wind power forecasting (VSTWPF) on a 10 min scale (Figure 11), models such as Bagging, Extra Trees, Random Forest (RF), Decision Trees (DT), Deep Neural Networks (DNNs), and XGBoost performed well. RF, DT, DNNs, Stochastic Gradient Descent (SGD), and XGBoost had acceptable MAPE values starting from 11.552%. Bagging, Extra Trees, and RF achieved the highest adjusted R\({}^{2}\) scores (0.990, 0.993, and 0.969, respectively). Overall, ensemble methods and tree models outperformed linear models, AdaBoost, and SVM. Figure 10: ML results for 36 h ahead of WSF. Figure 9: ML results for 24 h ahead of WSF. Figure 11: Results of ML models for VSTWPF (10 min). Figure 12: ML results for 30 min ahead WPF. Figure 13: ML results for 6 h ahead WPF. At the 30 min forecasting interval (Figure 12), Bagging and Extra Trees models demonstrated exceptional accuracy, achieving MAPE values of 4.12% and 4.15%, respectively. This performance highlights their robustness and ability to effectively handle short-term fluctuations in wind speed. RF and DT followed closely, achieving MAPE values of 4.183% and 9.664%, respectively, confirming their utility for short-term predictions. XGBoost and SGB also delivered reasonably good results, reinforcing their versatility for various forecasting tasks, although they fell short of the top performers. On the other hand, DNNs, AdaBoost, regression, and SVM struggled to produce reliable forecasts, potentially due to their higher sensitivity to parameter tuning and overfitting on smaller datasets. These findings emphasize that ensemble methods such as Bagging and Extra Trees excel in cap  
Figure 14: ML results for 24 h ahead WPF. Figure 15: ML results for 36 h ahead WPF. turing the inherent complexities of wind speed variability over shorter periods, offering both precision and stability. For longer forecasting intervals, such as the 6 h, 24 h, and 36 h scales (Figures 13-15), the trends remained consistent, with Bagging, Extra Trees, RF, DT, XGBoost, and SGB models consistently achieving high performance. At the 6 h scale, the top models achieved MAPE values near 4%, showcasing their capability to handle extended prediction horizons while maintaining accuracy. At the 24 h scale, Bagging and Extra Trees emerged as the most reliable models, followed by RF, DT, XGBoost, and SGB, indicating their adaptability to medium-term predictions. The same ensemble-based methods retained their edge at the 36 h scale, solidifying their position as reliable tools for long-term wind speed and power forecasting. In contrast, models such as DNNs, AdaBoost, regression, and SVM consistently underperformed across all intervals. These results underscore the importance of leveraging ensemble methods and decision-tree-based models for comprehensive wind forecasting, as they effectively balance accuracy, computational efficiency, and scalability over varying time horizons. For further elaboration, Figures 16A and 17A demonstrate the performance of the Extra Trees model for VSTWSF in meters per second (m/s). These figures show that the predicted VSTWSF values closely match the actual observed wind speeds, indicating a high level of accuracy in the model's predictions. Similarly, Figures 16B and 17B highlight the effectiveness of the Bagging model for predicting VSTWPF in kilowatts per turbine (kW/turbine). As shown in Figure 16A, the regression equation: y = 0.9724x + 0.2432y = 0.9724x + 0.2432y demonstrates a strong correlation between actual and predicted values of VSTWSF, with an R\({}^{2}\) value of 0.98, indicating high accuracy. Similarly, Figure 16B shows the regression equation y = 0.9841x + 14.234y = 0.9841x + 14.234y = 0.9841x + 14.234 for VSTWPF, with an R\({}^{2}\) value of 0.97, also reflecting a strong correlation and reliable predictions.