# Assessment of Offshore Wind Resources, Based on Improved Particle Swarm Optimization
## 4 Prediction Results and Precision Analysis of the Wind Speed
### Training and Simulation  
According to the IPSO-BP model, taking the wind speed data of Luchao Port in 2004 as the training object, one can predict the three time periods in 2005, as shown in Figure 7. It is necessary to explain that the BP in Figures 7-9 actually represents the BPNN, to simplify the description. The wind speed changes slowly in Figure 6(a), while in Figure 6(b), the wind speed increases significantly and fluctuates sharply within a short period of time, and the prediction curve appears as a small deviation, at this time. In Figure 6(c), the wind speed also rises sharply, but it is relatively stable in the range of 5-6 m/s, and the IPSO-BP model maintains an accurate prediction performance at this moment. It can be observed from Figure 6(d),e that the error is relatively large on 27 October, but the overall AE of the IPSO-BP model is small and its maximum does not exceed 1.4 m/s. It is clear that the IPSO-BP model is very accurate in predicting the wind speed  
Figure 6: Time series distribution of the wind speed in different periods. with little fluctuations, in a short period of time. However, if the wind speed fluctuates sharply or suddenly in a very short time range (such as 1-2 h), the IPSO-BP model will have a prediction deviation, but the change trend of the corresponding wind speed is more accurate. In order to further verify the accuracy of the model, the data from 2004 is still used as the training object to predict the three time periods in 2006, and the corresponding results are shown in Figure 8.  
Figure 7: Prediction results for 2005 based on the training set in 2004. The wind speed in Figure 8a varies greatly, the difference between the maximum wind speed and minimum one is about 6 m/s, and the prediction curve will have a small deviation when the wind speed sharply increases or decreases. The wind speed in Figure 8b ascends evidently and fluctuates violently in a short time, and at this time, there is a certain deviation in the prediction curve, especially near the 37 th and 90 th time series point. The sharp change of wind speed in a short period indicates that the measured value may be wrong or extreme weather may occur. Here are the 33 rd to 38 th values and the 85 th to 95 th values, where the wind speed fluctuates violently, rather than some value, and this is abnormal. Therefore, it can be ruled out that it is a measurement problem. The wind speed  
Figure 8: Prediction results for 2006 based on training set in 2004.  
rises slowly in Figure 8c, and the IPSO-BP model presents a relatively accurate prediction performance. It can be seen from Figure 8d,e that the error of the other time periods is controlled within 5%, except that of the prediction value is relatively large, due to the sudden change of wind speed on 8 April. Finally, the wind speed of the three time periods in 2006 is predicted by taking the data of 2005 as the training object, as shown in Figure 9.  
The results reflect the fact that the prediction errors of the 46 th and 47 th time points in Figure 9a, the 36 th, 37 th and 90 th time points in Figure 9b, and the 46 th and 47 th time  
Figure 9: Prediction results for 2006, based on training set in 2005.  
points in Figure 9c, are visibly reduced. The maximum AE of the IPSO-BP model drops by about 35 m/s, in comparison with Figures 8d and 9d. Nevertheless, since the wind speed rose sharply from the 32 nd to the 35 th points on 8 April, and dropped sharply from the 85 th to the 90 th points and then rose rapidly, these two places have large errors. Therefore, the selection of the training data has a certain impact on the precision of the IPSO-BP model, multiple groups of data should be trained and tested before determining the model parameters, and a group of data with the smallest MAPE are ultimately selected as the benchmark training data applied to the prediction model.4.3. The above nine groups of prediction results, based on the IPSO-BP model, are listed in Tables 3-5.