期刊论文、会议论文

Xu, Yuebing

英文

2018 13TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA)

2018

534-539

13th World Congress on Intelligent Control and Automation (WCICA)

JUL 04-08, 2018

Changsha, PEOPLES R CHINA

[Xu, Yuebing;Zhang, Jing;Chen, Yan] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China.^[Xu, Yuebing] Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & Ap, Coll Phys & Elect Engn, Hengyang 421002, Peoples R China.^[Long, Zuqiang] Hengyang Normal Univ, Coll Phys & Elect Engn, Hengyang 421002, Peoples R China.

IEEE, RA, Hunan Univ, Natl Univ Defense Technol, Harbin Inst Technol, State Key Lab Robot & Syst, China State Construct Int Investment China Ltd, Chinese Univ Hong Kong, Hunan Xiangjiang New Area, Econ Dev Bur, Hunan Assoc Automat, Yuelu Dist Govt

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-5386-7345-4

10.1109/WCICA.2018.8630722

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61573299]; Science and Technology Plan Project of Hunan Province [2016TP1020]; Open fund project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University [201711PAYB04]; Natural Science Foundation of Hunan ProvinceNatural Science Foundation of Hunan Province [2017.1.12011]; Research Project of the Education Department of Hunan Province [17A031]

本校为通讯机构

This study proposed a new hybrid approach for short-term water demand prediction. Raw water demand data were decomposed into a set of intrinsic mode functions (IMFs) component and a residue by using ensemble empirical mode decomposition (EEMD) method. IMF1 is the main random component of the raw water demand data, the continuous deep belief neural network (CDBNN) model is used to predict IMF1. Other IMFs and residue can be recombined employing autoregressive integrated moving average (ARIMA) model for forecasting, this method reduces forecasting steps without increasing the forecasting error. ...