基于改进GM(1,1)和SVM的长期电量优化组合预测模型

针对中长期电量预测可使用的相关历史数据较少、影响因素较为复杂等特点,提出一种基于改进GM(1,1)和支持向量机的优化组合预测模型.该模型将改进灰色预测模型和支持向量机模型进行组合,采用蛙跳寻优算法求取组合预测模型中各单一模型的权重,构建基于蛙跳优化的组合预测模型.将优化后的组合预测模型应用于我国中长期电量预测,选择我国1991-2005年电量进行分析,对2006-2010年的电量进行预测,并与一般组合预测模型及各单一模型进行比较.研究结果表明:本文方法得到的电量平均相对误差为2.06％,比等权组合预测模型、方差－协方差优选组合预测模型以及各单一预测模型的预测精度都有所提高.

An optimally combined forecast model for long-term power demand based on improved grey and SVM model

According to the characteristics of long-term power demand forecasting,an optimally combined forecast model based on improved GM(1,1) and support vector machine was proposed.In this model,the shuffled frog leaping algorithm was used to optimize the weight of each model.The optimized combination forecast model was used to forecast the long-term power demand of our country,based on the training exampling power demand from 1991 to 2005,the testing exampling power demand from 2006 to 2010,and it was compared with common combination forecast model and each single model.The results show that the average relative error is 2.06% using the combined forecast model.Compared with equal weight combined forecast model,variance-covariance optimally combined forecast model and the single model,the prediction accuracy is improved.