近日，我院国际商务教研室专职教师罗晓双在《Journal of Mathematics》以第一作者身份发表了题目为“Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization”的期刊论文。《Journal of Mathematics》现如今已被收录到科学引文索引扩展板（SCIE）中，为SCI四区期刊。自2013年创刊以来，该期刊共出版了300多篇论文，涉及理论和应用数学的多个分支。

Abstract：The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly,considering the modeling mechanism and process,the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly,as different orders of accumulation generation correspond to different parameter matrixes,the background value coefficient of the grey prediction model is optimized correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%,which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%,respectively). The forecast shows that China’s to the other three grey prediction models (2.82%,1.68%,and 2.60%, respectively). The forecast shows that China' s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

This work was supported by the Chongqing Natural Science Foundation of China and Key Project of Scientific and Technological Research of Chongqing Education Commission .  

文章链接为：

https://schlr.cnki.net/zn/Detail/index/WWMERGEJLAST/SJHDA5294182361961963740BB4930F3C5F4;