A Fast and Stable Forecasting Model to Forecast Power Load-Reference-Cited by-同舟云学术

A Fast and Stable Forecasting Model to Forecast Power Load

Published:2015-04-27 Issue:03 Volume:29 Page:1559005
ISSN:0218-0014
Container-title:International Journal of Pattern Recognition and Artificial Intelligence
language:en
Short-container-title:Int. J. Patt. Recogn. Artif. Intell.

Author:

Tan Li-Zhi¹²,Ouyang A.-J.¹³,Peng Xu-Yu³,Li Er-Xi²,Truong Tung Khac⁴⁵,Hao Libo⁶

Affiliation:

1. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan 410082, P. R. China

2. Department of Science & Research, Hunan Automotive Engineering Vocational College, Zhuzhou, Hunan 412001, P. R. China

3. Department of Electrical and Information Engineering, Hunan Institute of Traffic Engineering, Hengyang 421001, Hunan, P. R. China

4. Institute for Computational Science and Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

5. Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh, Vietnam

6. Information Engineering Collegem, Hunan Mechanical & Electrical Polytechnic, Changsha 410151, P. R. China

Abstract

As the traditional gray forecasting model GM(1, 1) has poor performance in forecasting the fast-growing power load, we present a chaotic co-evolutionary particle swarm optimization (CCPSO) algorithm, one with better efficiency than the PSO algorithm. Based on the GM(1, 1) model, the CCPSO algorithm is adopted to solve the values of parameters a and b in GM(1, 1) model. This is how the way we come up with the CCPSO algorithm-based GM. As can be seen from experimental results of case simulation on the power consumption in three regions, the CCPSO-GM model is better than the other four forecasting models, which attests its wide applicability and high forecasting accuracy.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218001415590053

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