| 动态自适应粒子群优化灰色模型的碳排放预测 |
| |
| 引用本文： | 黄飞.动态自适应粒子群优化灰色模型的碳排放预测[J].湖南文理学院学报(自然科学版),2012(4):21-25. |
| |
| 作者姓名： | 黄飞 |
| |
| 作者单位： | 丽水学院理学院 |
| |
| 基金项目： | 浙江省教育厅项目(Y200805484) |
| |
| 摘 要： | 碳排放量预测对于发展低碳经济十分重要，利用GM（1，1）灰色模型对碳排放预测存在一些不足．本文引入动态自适应粒子群算法对其进行改进，并结合新模型（DAPSOGM）来预测碳排放，以浙江丽水市近5年的碳排放量，编辑matlab程序实证分析，结果证实新模型具有较高的预测精度和推广价值．
|
| 关 键 词： | 碳排放 灰色模型GM（1 1） 标准粒子群算法 动态自适应粒子群算法 |
Carbon emissions forcasting based on dynamic adaptive particle swarm optimization grey model |
| |
| HUANG Fei.Carbon emissions forcasting based on dynamic adaptive particle swarm optimization grey model[J].Journal of Hunan University of Arts and Science:Natural Science Edition,2012(4):21-25. |
| |
| Authors: | HUANG Fei |
| |
| Affiliation: | HUANG Fei(College of Science,Lishui University,Lishui 323000,China) |
| |
| Abstract: | Lots of facts show： economic development is closely related to carbon emissions, in order to plan ＂the twelve five economy＂ and develop the low-carbon economy, predicting carbon emissions seeming meaningful. Because of the shortcoming of the model GM （1, 1）, this paper introduces a dynamic adaptive particle swarm optimization algorithm and improves the GM （1, 1） model to obtain a new model, called DAPSOGM, to forecast carbon emissions. Finally, using the carbon emissions of 2005 to 2009 from Lishui city in Zhejiang province to verify the DAPSOGM model shows that it has a higher precision of predict. |
| |
| Keywords: | carbon emissions gray model GM（1 1） particle swarm optimization （PSO） dynamic adaptive particleswarm optimization algorithm （DAPSO） |
| 本文献已被 CNKI 维普 等数据库收录！ |
