摘要
为了提高灰色模型在实际应用中的预测精度,文章对经典GM(1,1)模型进行了改进优化。首先从初始值、背景值和灰色微分方程三个方面优化经典GM(1,1)模型,然后运用诱导有序加权平均(IOWA)算子对三个优化模型进行组合赋权,建立基于IOWA算子的优化灰色组合模型,最后将该组合模型应用到江西省农村电力中长期负荷预测中。结果表明,所提出的组合模型比经典模型和单项优化模型具有更高的预测精度。
In order to increase the accuracy of gray forecasting model in the practical application, this paper firstly meliorates and optimizes the classical GM（1,1） model from three aspects concerning initial value, background value and improved grey differential equation, and then employs induced ordered weighted averaging （IOWA） operator to conduct combination weighting on the three optimization models, and establishes combined optimized gray model based on the IOWA operator. Finally, the paper applies this combined model to the medium and long term rural power load forecasting of Jiangxi Province. The study results show that the proposed combined model has higher prediction accuracy than the classical model and the individually optimized model.
出处
《统计与决策》
CSSCI
北大核心
2017年第19期73-77,共5页
Statistics & Decision
基金
南昌大学研究生创新专项资金(cx2016141)
