# Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource
## 4 Numerical Case of Deterministic Prediction  
The establishment of the wind speed prediction model is mainly divided into four parts: data processing; establishing a hybrid model; establishing a comparison model; and verifying the effectiveness of the hybrid model. In this section, we introduce a numeral case of deterministic prediction using the RBF network, considering the characteristics of randomness, fluctuation and nonlinearity of wind speed, and because the RBF network can approximate arbitrary nonlinear functions and has good generalization ability. The RBF neural network has strong nonlinear fitting ability and can map any complex nonlinear relationship. RBF has a three-layer structure, including the input layer, hidden layer and output layer. This paper will not go into detail due to limited space. In this section, we will compare the prediction results of the RBF network and BP neural network. In order to verify the applicability of the model, the sampling time interval is 10 minutes and the monitoring period is 2018.01.01-2018.08.30. The sampling variables include wind speed, as well as six factors: sinusoidal wind direction (\(\sin D\)); cosine wind direction (\(\cos D\)); temperature (T); air pressure (P); humidity (H); and motor speed (M). The data of the first three quarters of the month will be used as a training set, and the remaining data as a testing set after preprocessing. Here, the statistical analysis of data samples excludes extreme weather such as typhoons during the monitoring period. One day is divided into four continuous periods, with each period containing 400 minutes, and one day having 144 measured values of [0-40], [0-40], [0-40], [0-40], [80-10], [80-120] and [120-144], with the corresponding times being [0:00-6:40], [6:40-13:20], [13:20-20:00] and [20:00-24:00]. The forecasting day is 7 February 2019. Firstly, according to the above continuous time similarity clustering method (CTSC), the simultaneous data similar to the test samples are selected and used as the training samples to conduct the training network. The prediction results are shown in Figure 3.  
Figure 2: The process of partitioning continuous periods based on the wind characteristics. In this section, an RBF network model based on continuous time similarity clustering (CTSC) is proposed, and the results of RBF are better than that of the BP neural network, as shown in Figure 3. In Figure 3a,c, compared with primitive model and the RBF-CTSC model, all have strong fluctuation. The predicted results of the primitive model are far from the measured values. In Figure 3b, the prediction results of the RBF-CTSC model are significantly closer to the measured wind power values than the primitive model. In Figure 3d, compared with the measured values, the prediction results of the primitive model without CTSC lack fluctuation characteristics, and do not reflect the trend of the original series. As we can see from Figure 3, the RBF model is mainly applied to reflect the nonlinear characteristic of wind speed, while the time series model is used to reflect the linear characteristic of wind speed. As can be seen in Figure 3, both models can reflect the fluctuation trends of some actual values, but the prediction results of the RBF network are more approximate to actual values, and the validity of the model is proven. Table 1 is the accuracy analysis of the prediction model, in which MAE and RMSE are the error indexes. The less MAE or RMSE there are, the greater the prediction accuracy is. Comparing the average absolute error (MAE) and root mean square error (RMSE) of these two models, these error indexes of the BP model and RBF-CTSC model show a decreasing trend in order. We also find that even though the wind speed and direction present a randomness and volatility fluctuation range in the training data, the results of the new method still have a good performance.