摘要
为了增强灰色预测模型对各种特征数据的适应性,在现有反向累加灰色模型研究的基础上,提出了一种含线性时变参数的反向累加离散灰色预测模型,并给出了可用于预测的模型时间响应式和还原式。通过数值模拟和对广东省电力消费量的预测分析,结果显示:新模型比传统GM(1,1)模型、反向累加GOM(1,1)模型和反向累加离散DGOM(1,1)模型具有更高的建模精度。
In order to enhance the adaptability of grey prediction model to various characteristic data sequences,based on the existing opposite-direction accumulative grey model, a discrete grey prediction model with linear time-varying parameters is proposed,and the time response and restoring formulas of the model which can be used for forecasting are given respectively. Finally,through numerical simulation and forecasting analysis of electricity consumption in Guangdong Province,the results show that the new model exhibits higher modeling accuracy than the traditional GM( 1,1)model,opposite-direction accumulative GOM( 1,1) model and opposite-direction accumulative discrete DGOM( 1,1) model.
作者
曾亮
李亚男
ZENG Liang;LI Yanan(Department of Basic Courses,Guangdong Polytechnic College,Zhaoqing 526100,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2020年第7期238-244,共7页
Journal of Chongqing University of Technology：Natural Science
基金
广东省教育厅科研项目(2018KTSCX276,2019WTSCX130)
广东理工学院科研资助项目(GKJ2018013)。
关键词
灰色系统
反向累加
GOM(1
1)模型
时变参数
grey system
opposite-direction accumulation
GOM(1
1)model
time-varying parameters
