DocumentCode :
2100090
Title :
The Application of Two New Integrated Models in Short-Term Load Forecast
Author :
Xu Jian ; Qiu Xiaoyan ; Zhang Zijian
Author_Institution :
Sch. of Electr. Eng. & Inf., Sichuan Univ., Chengdu, China
fYear :
2010
fDate :
28-31 March 2010
Firstpage :
1
Lastpage :
4
Abstract :
Based on four general forecasting models (SVM model, BP neural network model, wavelet regression model and similar date model), two new models (integrated model I and integrated model II) are proposed in this paper. In the process of determining models and parameters, the virtual forecast conception is adopted. And a series of improvements on the aspects of historical data, temperature factor, holiday factor, economic growth, etc are made. Finally a global forecast competition which was held by EUNITE Network on August 1st, 2001 is taken as an example, showing the average daily error and maximum daily error in the forecasting of this two integrated models have been improved obviously with respect to the four simple models. So the two integrated models are proved to have has an important practical value.
Keywords :
backpropagation; load forecasting; neural nets; power engineering computing; support vector machines; wavelet transforms; BP neural network model; EUNITE Network; average daily error; maximum daily error; short-term load forecast; virtual forecast conception; wavelet regression model; Economic forecasting; Electronic mail; Error correction; Load forecasting; Load modeling; Neural networks; Power generation economics; Predictive models; Support vector machines; Temperature;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4812-8
Electronic_ISBN :
978-1-4244-4813-5
Type :
conf
DOI :
10.1109/APPEEC.2010.5448693
Filename :
5448693
Link To Document :
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