## 1 Introduction
The copula function can describe the correlation relationship between multiple variables more comprehensively and accurately; due to this excellent characteristic, some scholars have tried to apply it in the field of wind power prediction in recent years [32; 33]. This method has a fast calculation speed and relatively good prediction effect, but there are also some problems, which are manifested in the following aspects: (1) In the research, relevant scholars did not consider the value boundary problem of conditional copula function prediction model parameters (interval division number \(K\), condition number \(t\)); (2) different parameter values (\(K\), \(t\)) will result in interval prediction outcome with different precision, and the values of inappropriate parameters will reduce the prediction accuracy; (3) interval accuracy evaluation index containing the PICP and the PIAW generally contradict each other (when the effect of PICP is preferable, PIAW has poorer evaluation results and vice versa), which means selecting the parameter is difficult in the application of the current method. Aiming at the above problems, this paper proposes a conditional copula multi-objective prediction algorithm based on discrete form. This method takes advantage of the copula function and makes full use of the correlation existing in the adjacent load sequences to obtain the prediction interval to be predicted. A rolling prediction method is adopted to obtain prediction results for the entire period. At the same time, we further improve the prediction method of the conditional copula function in the process of research, and the value boundary problem of the model parameters was found and fully considered. Based on this, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II)multi-objective optimization algorithm was used to obtain the optimal solution of \(K\) and \(t\). After that, the optimal parameters and corresponding prediction results are obtained by evaluating the solution set, which improves the prediction accuracy and practicability of the method. Compared with the current common load prediction methods, the conditional copula multi-target load interval prediction method proposed in this paper has the following advantages:  
1. The conditional copula multi-objective interval prediction algorithm does not need to process a large amount of original data, and the calculation takes a short time. 2. Compared with the traditional machine learning algorithm represented by ANN, the prediction algorithm proposed in this paper has fewer calculation model parameters, does not require long-term machine learning and is convenient to apply. 3. The prediction algorithm proposed in this paper does not depend on the accuracy of point prediction and can directly obtain the probability distribution of the load to be predicted, thus, on the whole, improving the prediction accuracy of the load. 4. Compared with the probabilistic prediction method represented by the literature [28], there is no problem of selecting the kernel function, so that it can be applied better in practice and has the advantages of wide application range and strong expansibility. 5. Due to the excellent characteristics of the copula function, the prediction method proposed in this paper can make full use of the correlation between load sequences in adjacent time periods, and, compared with the current commonly used load prediction methods, it has better prediction accuracy and effect.