## 1 Introduction
At present, all the traditional load prediction methods can only obtain the determined point prediction result, that is, only a certain value is given; the disadvantage is that the possible fluctuation range of the prediction result cannot be determined. In fact, the power system contains various uncertain factors, which makes decision-making risky. Therefore, the uncertainty of the power system must be fully considered in the decision-making, and the interval prediction is more in line with the objective requirements [25]. In addition, as the power system load characteristics tend to be complex, the difficulty of accurate and reliable load point prediction is increasing, and it is more difficult to only use the point prediction results to ensure the safe and stable operation of the power system [26]. Compared to deterministic point prediction, the result of probabilistic interval prediction is not a simple deterministic value, but an interval. Based on this result, power system decision-makers can better understand the fluctuation range of future load changes, so that they can clearly recognize the potential uncertainties and risk factors of the future load when performing real-time scheduling and system safety analysis, and then make more reasonable, safe, and economical scheduling decisions [27], and also be more in line with the future development needs of the power market [28]. At present, many scholars have explored and studied the load interval prediction and formed a variety of prediction methods [26; 27; 28; 29; 30; 31]. They can be summarized as follows: the upper and lower bound estimation method based on ANN [29], the mixed construction interval method [26; 27; 28; 29; 30], and the probabilistic prediction method [28; 29; 30; 31]. The upper and lower bound estimation method based on ANN directly adopts the multi-input and double-output structure of feedforward neural network and makes the objective function optimal to obtain the prediction interval. In the literature [29], this method is applied to load interval prediction, and the particle swarm optimization algorithm is used to optimize the parameters in the ANN to improve the quality of the prediction interval. The hybrid construction interval method is usually based on a certain point prediction method, and then the interval results are constructed by error analysis. The authors of Reference [26] proposed a method based on the point prediction method of the extreme learning machine to construct the proportional coefficient so as to predict the short-term load interval. It has a wide application range and strong robustness; the literature [30] gives a prediction method based on multi-layer wavelet ANN, which constructs a load prediction interval by adding a hybrid Kalman filter. However, this kind of method is to perform interval prediction based on the traditional point prediction result, and the effect of point prediction will directly affect the effect of interval prediction. The probabilistic prediction method is mostly based on Bayesian theory [31]. This method mainly proceeds by constructing a distribution model of the predictive quantity, which can directly obtain the expected value of the predicted quantity, and its distribution, so that interval prediction results can be more conveniently obtained under any confidence level. The literature [28] also describes a brand-new machine learning algorithm based on Bayesian theory and statistical learning theory. Compared with the traditional interval prediction method, it has the advantages of strong generalization ability of machine learning algorithms. However, in practical applications, using different kernel functions would obtain different prediction results; when conducting an accuracy evaluation on the prediction results with different kernel functions, the evaluation results of the evaluation indicators often prove to be contradictory, resulting in an inability to obtain the optimal kernel function and prediction results.