## 7 Conclusions  
In this work, a new dual-step integrated machine learning method is devised for 24 h-ahead wind power forecasting using wind power generation actual measurements and weather forecast datasets. The devised technique is based on the hybridization of WT, ACO, and FFANN. The fundamental prediction engine is the FFANN. The WT is employed to extract the healthy and the important elements of the wind power raw data for improved forecast. The ACO algorithm is employed to find the best (optimal) values of the FFANN weight parameters for better forecast accuracy. The devised prediction method comprises two successive steps. In the first step, the ACO-FFANN is developed to estimate wind speed at the generation system spot and the hub elevation. In this step, historical weather variables as inputs and wind speed actual observation as the target are utilized to train the FFANN using the ACO parameter optimization process. In the second step, the WT-ACO-FFANN is implemented to fit the wind speed actual measurement with the wind power actual measurement. Later, the future speed by the ACO-FFANN in the initial step is given to the modeled WT-ACO-FFANN in the second step to predict the future wind generation subseries. The final generation power forecast is then produced by applying the inverse WT on the obtained subseries data. Two-year (2014-2015) historical weather data and wind speed and power actual measurement data are utilized to develop the forecaster. The developed forecaster performance is tested using one-year (2016) out-of-sample data. The devised method can be retrained when new training datasets exist in a rolling way. The application of the devised technique for 24 h-ahead wind power prediction is new and effective and achieves improved accuracy values. The daily mean MAPE, NMAE, and FS values achieved by the devised method are 4.67%, 0.82%, and 56.22%, respectively. The devised method's mean MAPE improvements \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Persistence** & **BP-FFANN** & **GA-FFANN** & **PSO-FFANN** & **WT-ACO-FFANN** \\ \hline January & 11.02 & 7.49 & 7.07 & 6.70 & 6.13 \\ February & 15.24 & 10.36 & 9.78 & 9.27 & 8.48 \\ March & 18.96 & 10.01 & 9.44 & 8.9 & 8.19 \\ April & 17.12 & 9.03 & 8.53 & 8.08 & 7.39 \\ May & 19.41 & 10.24 & 9.67 & 9.17 & 8.38 \\ June & 5.60 & 3.32 & 3.13 & 2.74 & 2.04 \\ July & 4.01 & 2.37 & 2.24 & 1.96 & 1.46 \\ August & 6.32 & 3.74 & 3.53 & 3.09 & 2.30 \\ September & 10.22 & 7.15 & 6.15 & 5.22 & 3.84 \\ October & 9.11 & 6.37 & 5.48 & 4.65 & 3.42 \\ November & 11.37 & 7.95 & 6.84 & 5.81 & 4.27 \\ December & 14.92 & 10.14 & 9.57 & 9.08 & 8.30 \\
**Average** & 11.94 & 7.35 & 6.79 & 6.22 & 5.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of MAPE results (%) by various WPP methods for complete one-year (2016) testing dataset. over the other four methods (persistence, dual-step BP-FFANN, dual-step GA-FFANN, and dual-step PSO-FFANN) are 58.89%, 38.95%, 31.82%, and 22.30%, respectively. The devised method's average error variance improvements over the other four methods are 75.44%, 57.58%, 39.13%, and 30%, respectively. The devised method outperforms the other four evaluated methods, and the 24 h-ahead forecast execution time is shorter than 10 s. Therefore, the demonstrated experimental outcomes verify the effectiveness of the devised method for 24 h-ahead wind generation forecast. The obtained short-term wind power forecast results can be used as input information for energy management systems, demand response control, scheduling centers, dispatching centers, and other operational control units of a power system or a smart grid containing high penetration of wind power generation. [PERSON] and [PERSON] gathered the prediction model input dataset, examined the dataset, and proposed and implemented the prediction algorithm. This research was funded by the National Natural Science Foundation Project of China, 61473094. The School of Elect. Info. Eng.