Abstract：Compared with the point prediction, the load interval prediction can provide the upper and lower bounds of the prediction value, which is more conducive to the stable operation of the power system. Aiming at the problem that the correlation between adjacent load sequences is not fully utilized, thereby reducing the prediction accuracy, a load interval prediction method based on Copula function and the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed. This method makes full use of the correlation between adjacent load sequences by establishing a Copula function. The MOEA/D multi-objective optimization algorithm is used to find the Pareto optimal solution set, and through entropy weight method and technique for order preference by similarity to ideal solution (TOPSIS), the optimal prediction model parameters and the prediction results are obtained. Finally, this method is applied to load forecasting for a certain area, and compared with commonly used interval forecasting methods. The results show that this method has better forecasting effect.
李智轩, 李嘉丰, 叶晓华, 熊显智, 李天泽. 基于Copula函数与多目标进化算法的负荷区间预测[J]. 电气技术, 2024, 25(6): 24-30.
LI Zhixuan, LI Jiafeng, YE Xiaohua, XIONG Xianzhi, LI Tianze. Interval prediction of load based on Copula function and multi-objective evolutionary algorithm. Electrical Engineering, 2024, 25(6): 24-30.
2024, Vol. 25 
