## 7 Conclusions  
With a rapidly rising demand for clean energy obtained from renewable power resources, more attention and resources have been focused on the development of effectively utilizing these energy resources. Among those resources, wind power has the most promising future. In the area of the prediction, not only the accuracy, but also the stability, should be seen as key factors of the forecasting model. However, due to the uncertainty and intermittence of the raw wind speed data, the forecasting results obtained by conventional models cannot meet this goal. Additionally, with the poor results of forecasting, the power grids cannot make adjustments to the wind plan timely, causing low productivity and increasing the operation cost of the wind farms. Therefore, it is necessary to develop a short-term forecasting method that can achieve satisfactory accuracy and stability at the same time [37]. This paper suggests a new hybrid model that combines a parameter optimization algorithm (CSO) and a forecasting module (SVM). The key weight coefficients of the forecasting module are optimized by the proposed algorithm. To reduce the noise in the raw data, a data preprocessing technique (EEMD) is used to obtain a stationary time series. To verify the validity of the proposed model, multi-step ahead forecasting and several performance evaluation metrics are applied in this paper. Traditional forecasting methods are used for comparison. In one-step ahead forecasting, the MAPE values of the BP, ARIMA, RBF, and the proposed model are 5.46%, 5.22%, 4.83%, and 3.07%, respectively. In two-step ahead forecasting, the MAPE values of these models are 6.70%, 6.42%, 5.97%, \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Datasets** & & **BPNN** & **RBFNN** & **ARIMA** \\ \hline
**Dataset 1** & 1-step & 7.2859 & 7.4392 & 8.0205 \\  & 2-step & 6.8073 & 7.1682 & 7.5217 \\  & 3-step & 6.5270 & 6.9463 & 7.2258 \\ \hline
**Dataset 2** & 1-step & 6.9469 & 7.4245 & 6.2829 \\  & 2-step & 6.7716 & 7.2839 & 6.5691 \\  & 3-step & 6.8358 & 7.1475 & 6.2039 \\ \hline
**Dataset 3** & 1-step & 7.1325 & 6.2161 & 6.2934 \\  & 2-step & 7.0413 & 6.4755 & 6.7390 \\  & 3-step & 6.9427 & 6.3586 & 6.4803 \\ \hline
**Dataset 4** & 1-step & 6.8941 & 7.2790 & 7.1783 \\  & 2-step & 7.2564 & 7.2387 & 7.0492 \\  & 3-step & 6.4396 & 6.9257 & 6.5864 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of DM test. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & & **BPNN** & **RBFNN** & **ARIMA** & **EEMDCSOSVM** \\ \hline \multirow{2}{*}{**Dataset 1**} & First order & 0.8951 & 0.8889 & 0.8874 & **0.9146** \\  & Second order & 0.8322 & 0.8257 & 0.8283 & **0.8638** \\ \hline \multirow{2}{*}{**Dataset 2**} & First order & 0.9130 & 0.9029 & 0.9072 & **0.9367** \\  & Second order & 0.8384 & 0.8362 & 0.8436 & **0.8842** \\ \hline \multirow{2}{*}{**Dataset 3**} & First order & 0.9283 & 0.9177 & 0.9220 & **0.9409** \\  & Second order & 0.8517 & 0.8582 & 0.8625 & **0.8975** \\ \hline \multirow{2}{*}{**Dataset 4**} & First order & 0.8956 & 0.8934 & 0.9031 & **0.9269** \\  & Second order & 0.8344 & 0.8360 & 0.8492 & **0.8816** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Forecasting Effectiveness of different models for four datasets. and 4.55%, while in three-step ahead forecasting, the MAPE values of the four models are 7.83%, 7.15%, 6.92%, and 5.46%. Not only the figures of MAPE, but also other five variables and the two tests, show the excellent performance of the proposed approach. Based on the experimental results, the newly proposed approach achieved the highest performance in wind speed prediction in both one-step forecasting and multi-step forecasting, in comparison with the other three methods used in the experiment. In conclusion, based on the experimental results, the proposed approach achieved significant improvement in both forecasting accuracy and stability. The proposed a hybrid model, which effectively improved the accuracy and the stability of wind speed forecasting, can be a great tool in managing wind farms.