基于自适应调节的灰色滚动预测模型及对碳排放趋势预测  

Grey rolling model based on self-adaptive adjustment and forecasting tendency of carbon emission

作　　者：徐宁 秦邱皓 王天宇 丁松 XU Ning;QIN Qiu-hao;WANG Tian-yu;DING Song(Business School,Nanjing Audit University,Nanjing 211815,China;College of Economics,Zhejiang University of Finance and Economics,Hangzhou 310018,China)

机构地区：[1]南京审计大学商学院,南京211815 [2]浙江财经大学经济学院,杭州310018

出　　处：《控制与决策》2023年第12期3409-3417,共9页Control and Decision

基　　金：国家自然科学基金项目(71701101,71901191);江苏省研究生科研与实践创新计划项目(SJCX22_0988,KYCX22_2193);浙江省软科学项目(2021C35068);江苏高校“青蓝工程”项目。

摘　　要：利用有限数据预测发展趋势是数据建模领域广泛存在的问题,采用灰预测模型处理此类问题时会面临适应数据不规则波动特征的挑战性.在灰预测模型基础上提出适应数据特征的滚动建模方法,结合双参数的变权缓冲算子建立一种AGRM(1,1)模型,该模型在数据的切片基础上使用优化的灰预测模型实现对不同增长系数的准确模拟,利用缓冲算子链对数据进行调整处理,最后设计以差分进化算法为基础的算子参数优化方法.所提出的模型改变了传统灰预测模型响应式形式单调的结构特点,实现对带有波动和振荡的序列的准确预测.在算例检验中,利用不同增长系数的检验数据验证了AGRM(1,1)模型同样具备无偏性,同时将模型应用于我国宏观范围碳排放数据的发展趋势预测,建模结果印证了该模型相对同类模型具有明显的精确度提升.Using limited data to predict the development trend is a widespread problem in the field of data modeling.When dealing with such problems,a grey prediction model faces the challenge of adapting to the irregular fluctuation characteristics of data.Based on the grey prediction model,this paper proposes a rolling modeling method that dynamically adapts to the data characteristics,and establishes the AGRM(1,1) model combined with the full information variable weight buffer operator that contains two parameters.The model uses the unbiased optimized grey prediction model to accurately simulate different growth coefficients on the basis of data slicing,and uses the buffer operator chain to adjust the data.Finally,an operator parameter optimization method based on the differential evolution algorithm is designed.The model changes the monotonous structure of the form of the traditional grey prediction model's time response function,and realizes the accurate prediction of the sequence with fluctuation and oscillation.In the case study,the test data of different growth coefficients are used to verify that the AGRM(1,1) model also has high unbiasedness.At the same time,the model is applied to the development trend prediction of China's macro carbon emission data.The modeling results confirm that the accuracy of the model is significantly improved compared with similar models.

关 键 词：灰色预测 GM(1 1) 缓冲算子 差分进化算法 滚动建模 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]