The Design of Short-Term Load Forecast Systems Based on the Theory of Complex Systems

Author

Da-shuai, Sun ; Li-xin, Ma ; Shou-zheng, Wang

Author_Institution

Sch. of Opt.-Electr. & Comput. Eng., Univ. of ShangHai for Sci. & Technol., ShangHai, China

Volume

2

fYear

2010

fDate

13-14 Oct. 2010

Firstpage

622

Lastpage

625

Abstract

This paper presents a new load forecasting system based on the complex system theory. The power network is a complex nonlinear system. The complex system analysis theory is applied to divide a power network into some sub-system according to its different area position and load type, and then uses the BP neural network incorporated with weather influence factors forecast the load value respectively. Experimental results verify that this method can help to improve the forecasting accuracy.

Keywords

backpropagation; load forecasting; power engineering computing; BP neural network; area position; complex nonlinear system; complex system analysis theory; load forecasting system; load type; load value; power network; short-term load forecast systems; weather influence factors; Accuracy; Artificial neural networks; Electricity; Forecasting; Load forecasting; Neurons; complex system; neural network; short-term load forecast; weather factor;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent System Design and Engineering Application (ISDEA), 2010 International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-8333-4

Type

conf

DOI

10.1109/ISDEA.2010.210

Filename

5743492