## 4 Experiments
### Wind Power Forecasting Results  
In order to better express the value of the proposed model in this paper, five models are selected to be compared with the proposed model. These models include CART, BPNN, RF, SVR, and a simple XGBoost model.  
Figure 5 shows the clustering results. Points with different colors represent different clusters. We can clearly see that there are seven clusters in Figure 5. These clusters have different characteristics, such as different wind directions and different air pressure. From the prediction results in Table 2, we see that the XGBoost model with weather similarity analysis and feature engineering can reduce the prediction error compared with a single XGBoost model, in which MSE, RMSE, MAE, and RMAE are reduced by 19.93%, 10.52%, 18.79%, and 9.73%, respectively. From this point of view, weather similarity analysis and feature engineering improve the accuracy of forecasting results significantly. What is more, the proposed model is the most accurate model among the six models. For RF, which obtains the best performance among the five compared models, MSE, RMSE, MAE, and RMAE are reduced by 16.56%, 8.68%, 4.58%, and 2.11%, respectively.  
Figure 6 shows the predicted wind power of RF and the proposed model from April 21, 2017, to April 28, 2017. For the five compared models, there is a RF model in Figure 6, because RF is the best model among all compared models based on results in Table 2. In Figure 6, the blue curve represents wind power of actual data. The red curve represents wind power forecasting results of the proposed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Proposed** & **RF** & **CART** & **BPNN** & **XGBoost** & **SVR** \\ \hline MSE & 434.06 & 520.20 & 948.51 & 738.97 & 542.11 & 1174.73 \\ RMSE & 20.83 & 22.81 & 31.51 & 27.18 & 23.28 & 34.27 \\ MAE & 13.74 & 14.40 & 19.39 & 20.37 & 16.92 & 23.37 \\ RMAE & 3.71 & 3.79 & 4.40 & 4.51 & 4.11 & 4.83 \\ \hline \hline \end{tabular} RF: random forests; CART: classification and regression tree; BPNN: back propagation neural network; XGBoost: extreme gradient boosting; SVR: support vector regression; MSE: mean square error; RMSE: root mean square error; MAE: mean absolute error; RMAE: root mean absolute error.  
\end{table}
Table 2: Evaluated results of different wind power forecasting models.  
Figure 5: Scatter diagram of clustering results.  
model and the black curve represents wind power forecasting results of RF. According to the messages of the red circle in Figure 6, it is easy to see that the proposed model is obviously better than RF.  
Figure 7 displays the absolute error between actual wind power and predictive wind power for different models. In Figure 7, the red curve represents the absolute error between the predictive wind power of the proposed model and actual wind power, and the black curve represents the absolute error between the predictive wind power of RF and actual wind power. For most times, the forecasting error of the RF is greater than the proposed model. The proposed model can follow the wind power's variation tendency of actual data and the stability of the proposed model is better than RF.  
Figure 6: WPF results of RF and the proposed model.  
Figure 7: Absolute error between actual wind power and predictive results of different models.  
According to the above analysis, it is significant to see that the XGBoost model with weather similarity analysis and feature engineering is better than a simple XGBoost model. This also confirms the value of weather similarity analysis and feature engineering. In short, the proposed model in this paper is better than the traditional WPF models (BPNN, CART, RF, XGBoost, and SVR) and has wide applicability.