## 6 Experimental Results and Discussion  
In this work, the dual-step WT-ACO-FFANN integrated ML method is implemented for 24-ahead WPP of a real wind generation system in Beijing, China. The actual measurement of the power generation of the system is used for the forecast model construction. The wind farm has an installed capacity of 2500 kW. A two-year (2014-2015) historical dataset of meteorological forecast, wind speed, and power real observations are employed for the forecast model training and validation; 85% of the dataset is employed for the model training, while the remaining 15% is utilized for validation. The data points are grouped either to the training or the validation dataset using a random selection mechanism. The forecast performance of the devised model is tested using a one-year (2016) testing dataset. The prediction test results are demonstrated for one representative day from each season of the testing year and for the complete testing year on a monthly basis at the end. The four testing days are selected randomly to reveal the wind power generation actual scenario and the irregular accuracy distribution at different time frames in the testing year. The forecasts are presented in one-hour resolutions. The forecasts by the proposed dual-step WT-ACO-FFANN integrated ML method are depicted in Figures 6-9 for winter, spring, summer, and fall testing days, respectively. The figures show the wind power generation actual measurements from the SCADA versus the predicted power by the devised technique. Figure 6: Real versus forecasted wind power for a winter day (4 January 2016). Table 4 presents the comparison of the MAPEs obtained by the devised dual-step WT-ACO-FFANN method and four other methods--persistence, dual-step BP-FFANN, dual-step GA-FFANN, and dual-step PSO-FFANN methods. The devised method achieves improved accuracy with a mean MAPE value of 4.67% for 24 h-ahead forecast. The devised method's mean MAPE improvements over the other four methods are 58.89%, 38.95%, 31.82%, and 22.30%, respectively. Table 5 gives the comparison of the NMAEs achieved by the devised dual-step WT-ACO-FFANN method and the other four methods. Regarding the NMAE criterion values presented in Table 5, the devised dual-step WT-ACO-FFANN WPP method achieves the lowest average error value, which equals 0.82% of the wind farm maximum capacity. This a very acceptable value in the current research scope of WPP, which verifies the effectiveness of the devised WPP method in this work. The hourly WPP absolute errors normalized by the wind farm maximum power (2500 kW) for all the evaluated methods are displayed in Figures 10-13 for winter, spring, summer, and fall forecast test days, respectively. It is illustrated in the figures that the devised dual-step WT-ACO-FFANN WPP method achieves the lowest absolute error values over the other methods in most of the day hours. \begin{table}
\begin{tabular}{c c c c c c} \hline
**WPP Method** & **Winter** & **Spring** & **Summer** & **Fall** & **Average** \\ \hline Persistence & 13.84 & 17.83 & 4.56 & 9.32 & 11.36 \\ BP-FFANN & 9.41 & 11.97 & 2.70 & 6.52 & 7.65 \\ GA-FFANN & 8.88 & 10.35 & 2.55 & 5.61 & 6.85 \\ PSO-FFANN & 8.42 & 8.64 & 2.23 & 4.76 & 6.01 \\ WT-ACO-FFANN & 7.70 & 5.80 & 1.66 & 3.50 & 4.67 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of MAPE results (%) by various wind power prediction (WPP) methods. \begin{table}
\begin{tabular}{c c c c} \hline
**Day Type** & **MAPE (\%)** & **RMSE (kW)** & **Forecast Skill (\%)** \\ \hline Winter & 7.70 & 38.84 & 45.33 \\ Spring & 5.80 & 24.87 & 63.55 \\ Summer & 1.66 & 5.41 & 61.31 \\ Fall & 3.50 & 32.80 & 54.67 \\ Average & 4.67 & 25.48 & 56.22 \\ \hline \end{tabular}
\end{table} 
Table 3: Accuracy of the wind power forecasts by the proposed dual-step WT-ACO-FFANN integrated machine learning (ML) method.