中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 数学与系统科学研究院
  3. 中国科学院数学与系统科学研究院
  4. 系统科学研究所
A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China

文献类型：期刊论文

作者Wang, Shuai2,4; Yu, Lean1,3; Tang, Ling2,4; Wang, Shouyang1,3
刊名ENERGY
出版日期2011-11-01
卷号36期号:11页码:6542-6554
ISSN号0360-5442
关键词Hydropower consumption forecasting LSSVR ensemble Learning Seasonal decomposition
DOI10.1016/j.energy.2011.09.010
英文摘要Due to the distinct seasonal characteristics of hydropower, this study tries to propose a seasonal decomposition (SD) based least squares support vector regression (LSSVR) ensemble learning model for Chinese hydropower consumption forecasting. In the formulation of ensemble learning model, the original hydropower consumption series are first decomposed into trend cycle, seasonal factor and irregular component. Then the LSSVR with the radial basis function (RBF) kernel is used to predict the three different components independently. Finally, these prediction results of the three components are combined with another LSSVR to formulate an ensemble result for the original hydropower consumption series. In terms of error measurements and statistic test on the forecasting performance, the proposed approach outperforms all the other benchmark methods listed in this study in both level accuracy and directional accuracy. Experimental results reveal that the proposed SD-based LSSVR ensemble learning paradigm is a very promising approach for complex time series forecasting with seasonality. (C) 2011 Elsevier Ltd. All rights reserved.
资助项目National Science Fund for Distinguished Young Scholars[71025005] ; National Natural Science Foundation of China (NSFC)[90924024] ; National Natural Science Foundation of China (NSFC)[70601029] ; Chinese Academy of Sciences ; K.C. Wong Education Foundation, Hong Kong
WOS研究方向Thermodynamics ; Energy & Fuels
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:000297894500028
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/11362]  
专题系统科学研究所
通讯作者Yu, Lean
作者单位1.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Policy & Management, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, MADIS, Inst Syst Sci, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Grad Univ, Beijing 100080, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Shuai,Yu, Lean,Tang, Ling,et al. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China[J]. ENERGY,2011,36(11):6542-6554.
APA Wang, Shuai,Yu, Lean,Tang, Ling,&Wang, Shouyang.(2011).A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China.ENERGY,36(11),6542-6554.
MLA Wang, Shuai,et al."A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China".ENERGY 36.11(2011):6542-6554.

入库方式： OAI收割

来源：数学与系统科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。