Short-term load forecasting based on complexity science theory

Author

Ma, Lixin ; Ren, Youming ; Qu, Nana ; Jiang, Ni

Author_Institution

Dept. of Electr. Eng., Univ. of Shanghai for Sci. & Tech., Shanghai, China

fYear

2011

fDate

16-18 Sept. 2011

Firstpage

1262

Lastpage

1262

Abstract

As a typical and special complexity gigantic system, the power system is facing the challenge from complexity science in the aspects of load forecasting and its management. Therefore, on the basis of complex system theory, a new method used for predicting the short-term load is proposed by means of a series of subsystems divided according to the different areas and types of regional electricity. Support vector machine forecasting model is applied to each subsystem and the results show this model is better than one of neural network in forecasting accuracy.

Keywords

load forecasting; neural nets; power engineering computing; support vector machines; complex system theory; complexity science theory; forecasting accuracy; load management; neural network; power system; short-term load forecasting; special complexity gigantic system; support vector machine forecasting model; Artificial neural networks; Complexity theory; Electricity; Kernel; Load forecasting; Support vector machines; complexity science; short-term load forecasting; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Control Engineering (ICECE), 2011 International Conference on

Conference_Location

Yichang

Print_ISBN

978-1-4244-8162-0

Type

conf

DOI

10.1109/ICECENG.2011.6057246

Filename

6057246