GM(1,1)-MEA-BP组合模型电能消耗预测及应用  

Power consumption prediction and application of GM(1,1)-MEA-BP combined model

作　　者：钞寅康 龚立雄[1] 黄霄 陈佳霖 CHAO Yinkang;GONG Lixiong;HUANG Xiao;CHEN Jialin(School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北工业大学机械工程学院,武汉430068

出　　处：《重庆理工大学学报（自然科学）》2023年第7期306-314,共9页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(51907055)。

摘　　要：为解决传统单一模型泛化能力弱、预测精度低等问题,提出一种GM(1,1)灰色模型和MEA-BP神经网络的组合预测模型,解决了GM(1,1)预测模型对能耗的预测受时间因素影响随机波动大及预测精度较低等问题,融合MEA-BP神经网络并行计算、强容错力以及分布式信息存储等优势,减少了因数据波动而影响预测结果精度的情况,解决了误差无法反馈调整等问题。选取1985—2020年全国电能消耗总量为建模数据,与线性回归、三指数平滑、GM(1,1)、BP神经网络、MEA-BP神经网络等模型的预测结果进行分析比较。结果表明:GM(1,1)-MEA-BP组合模型相较于其他模型,预测精度最高,误差值最小,MAPE值达到0.0065,RMSE值达到977.9961。通过实例证明了GM(1,1)-MEA-BP组合模型对电能消耗量预测具备较高的精度,可为国家在能源方面宏观智能调度提供依据。Aiming at the problems of the weak generalization ability and low prediction accuracy of the traditional single model,this paper proposes a combined prediction model of GM(1,1)grey model and MEA-BP neural network.The model solves the problems of the influence of large random fluctuation and low prediction accuracy of the GM(1,1)prediction model on energy consumption prediction caused by time factors.With the advantages of MEA-BP neural network parallel computation,strong fault-tolerant force and distributed information storage,it also reduces the situation where the accuracy of the prediction results is affected by data fluctuations,and solves the problem of error feedback adjustment.The total amount of national electric energy consumption from 1985 to 2020 is selected as the modeling data,and the prediction results of linear regression,triple exponential smoothing,GM(1,1),BP neural network,MEA-BP neural network and other models are analyzed and compared.The results show that,compared with other models,GM(1,1)-MEA-BP combination model has the highest prediction accuracy and the smallest error,with MAPE value reaching 0.0065 and RMSE value reaching 977.9961.It is proved by an example that GM(1,1)-MEA-BP combination model has a high prediction accuracy of the electric consumption in China,which provides a basis for national macro intelligent scheduling in energy.

关 键 词：灰色模型 MEA-BP神经网络 电能消耗预测 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]