Title :
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
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;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2011 International Conference on
Conference_Location :
Yichang
Print_ISBN :
978-1-4244-8162-0
DOI :
10.1109/ICECENG.2011.6057246
