DocumentCode
2061412
Title
Short-term load forecasting techniques using ANN
Author
Xu, Leyan ; Chen, Wei Ji
Author_Institution
Fac. of Sci. & Technol., Univ. of Macau, Macao, China
fYear
2001
fDate
2001
Firstpage
157
Lastpage
160
Abstract
This paper presents some practical techniques of using artificial neural networks for the short-term load forecasting problem. The model described in this paper is a backpropagation based multilayer perceptron including the temperature factor. In order to expedite the training process, the quasi-Newton method is employed. An intelligent treatment to holiday factors in order to improve the forecasting accuracy is discussed. The average forecasting error of this system is 3.2%
Keywords
backpropagation; electricity supply industry; load forecasting; multilayer perceptrons; backpropagation; holiday factor; load forecasting; multilayer perceptron; neural networks; quasi-Newton algorithm; short-term forecasting; temperature factor; Artificial neural networks; Convergence; Cost function; Economic forecasting; Load forecasting; Multilayer perceptrons; Newton method; Predictive models; Temperature; Weather forecasting;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Applications, 2001. (CCA '01). Proceedings of the 2001 IEEE International Conference on
Conference_Location
Mexico City
Print_ISBN
0-7803-6733-2
Type
conf
DOI
10.1109/CCA.2001.973856
Filename
973856
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