期刊论文、会议论文

Liu, Libin

英文

Communications in Computer and Information Science

1865-0929

2014

472

165-170

9th International Conference on Bio-Inspired Computing - Theories and Applications (BIC-TA)

Communications in Computer and Information Science

OCT 16-19, 2014

Wuhan, PEOPLES R CHINA

[Liu, Libin] Chizhou Univ, Dept Math & Comp Sci, Chizhou 247000, Anhui, Peoples R China.^[Hao, Libo] Hunan Mech & Elect Polytech, Dept Informat Engn, Changsha 410151, Hunan, Peoples R China.^[Ouyang, Aijia] Hunan Sci & Technol Econ Trade Vocat Coll, Coll Comp, Hengyang 421001, Peoples R China.^[Ouyang, Aijia] Hunan City Univ, Sch Informat Sci & Engn, Hunan 413000, Peoples R China.

Natl Nat Sci Fdn China, Huazhong Univ Sci &Technol, Zhengzhou Univ Light Ind

Pan, L Paun, G PerezJimenez, MJ Song, T

HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY

SPRINGER-VERLAG BERLIN

978-3-662-45048-2

10.1007/978-3-662-45049-9_26

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [11301044, 61202109]; Scientific Research Fund of Hunan Provincial Education DepartmentHunan Provincial Education Department [13C333]; Science and Technology Research Foundation of Hunan Province [2014GK3043]; Hunan Education Science Twelfth- Five Year PlanChina [XJK013CXX003]

本校为其他机构

To address the shortcomings of traditional grey forecasting model GM (1, 1) in terms of poor forecasting on fast-growing power load, this paper proposes a chaotic co-evolutionary PSO algorithm which has better efficiency than the particle swarm optimization algorithm. Combined with the GM (1, 1) model, a chaotic co-evolutionary particle swarm optimization algorithm has been used to solve the values of two parameters in GM (1, 1) model. In this way, we have designed a CCPSO algorithm-based grey model. Results of case simulation on the power consumption in 3 regions show that the CCPSO-GM model ...