## 3 Results
### Results Comparison for Different Algorithms
However, since the method can make full use of the correlation between adjacent load sequences when the load is abruptly changed at other periods such as points 26-27 and 32-33, a certain interval prediction margin is retained, thereby reducing the number of points of failed prediction. From the prediction results of the four methods, when using the method of this paper to predict region 1, only the upper bounds of the two prediction intervals do not meet the requirements, while more than three appear in (b), (c), (d), which indicates that the PICP obtained by this method is higher. At the same time, the PIAW of the prediction results obtained by this method is the lowest among all methods, which shows that the interval prediction algorithm provided in this paper has the highest accuracy. Figure 4: _Cont._  
In Figure 5a, only two points (34, 74) in the predicted segment do not meet the prediction requirements. In Figure 5b, only three points (27, 34, 71) in the predicted segment do not meet the prediction requirements. In Figure 5c, only four points (2, 34, 35, 71) in the predicted segment do not meet the prediction requirements. In Figure 5d, only five points (34, 35, 39, 71, 74) in the predicted segment do not meet the prediction requirements. It can be observed that the method of this paper is still the most reliable. Similarly, the main reason for the failure of the 34 th point prediction may be a sudden load change during this period. In Figure 5, we find that at the 90-96 point, all the lower bounds of the interval predictions given by the four methods are low, which makes the actual power values close to the lower bound of the interval prediction, so the prediction of this period is relatively conservative. This result might be due to the consecutive decrease in load prior to this period (86-90 points), such that the prediction model expects that this downward trend in load will continue to exist, thereby reducing the predicted lower bound and increasing the success rate. However, by comparing the prediction results of the four methods, we can also see that the method proposed in this paper has the largest PICP and the smallest PIAW, and a better interval prediction effect. Figure 4: Prediction results of applying four methods in region 1: (**a**) Prediction results based on the optimal parameter \(K\) and \(t\); (**b**) prediction results of artificial neural network (ANN); (**c**) prediction results based on extreme learning machine (ELM); (**d**) prediction results of Gaussian process regression (GPR) algorithm. Figure 5: _Cont._  
In region 3, the accuracy of the method proposed in this paper is still the highest, and three points (32, 34, 89) in Figure 6a do not meet the requirements. Four points (32, 34, 35, 89) in Figure 6b do not meet the requirements. Four points (31, 32, 34, 35) in Figure 6c do not meet the requirements. Five points (31, 32, 34, 35, 36) in Figure 6d do not meet the requirements. It can be seen that the overall prediction effect of each method in region 3 is inferior to region 1 and region 2, which may be caused by the relatively bigger fluctuation of load in the forecast period of region 3, and the overall prediction effect of each method in points 30 to 40 is poor; we speculate that this may be related to the fact that the load before the 30 th point is relatively stable, so that each model believes that this \"smooth\" process will continue, but in fact, the load has increased by about 500 MW in one hour. The excessive increase of the load makes all the upper bounds of the interval prediction given by various prediction models low, and the actual value of the load exceeds the upper prediction bound, causing prediction failure. However, the method in this paper can make full use of the correlations between loads and finds the \"trend\" of such a continuous decline is actually weakened. The prediction contains a certain margin for the prediction interval at the next moment, and that makes the prediction successful at points 35 and 36.