DocumentCode :
2627699
Title :
Mid-long term Algerian electric load forecasting using regression approach
Author :
Nezzar, Mohamed Reda ; Farah, Nadir ; Khadir, Tarek
Author_Institution :
LabGED Lab., Badji Mokhtar Univ., Annaba, Algeria
fYear :
2013
fDate :
9-11 May 2013
Firstpage :
121
Lastpage :
126
Abstract :
Electrical load is a major input factor in economic development. To support economic growth and meet the demands in the future, the load forecasting has become a very important task for electric power stations. Therefore, several techniques have been used to accomplish this task. In this study, our interest is focused on the multiple regression approach, especially, linear and exponential regression for medium-long term load forecasting. The choice of this approach is due to the lack of data does not allow us to use artificial intelligence approaches such as neural networks. In addition to the regression approach, we used a system of electric load profile that allows us to obtain the power has a smaller scale (hour, day, week) to get the peaks. Data that has been used in this work represent electric load consumption and were taken from the Algerian national electricity company.
Keywords :
artificial intelligence; load forecasting; neural nets; regression analysis; Algeria; artificial intelligence; economic development; electric load profile; electric power stations; exponential regression; linear regression; mid-long term electric load forecasting; neural networks; Artificial neural networks; Biological system modeling; Econometrics; Load modeling; medium-long term forecasting; multiple exponential regressions; multiple linear regressions; regression approaches;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference on
Conference_Location :
Konya
Print_ISBN :
978-1-4673-5612-1
Type :
conf
DOI :
10.1109/TAEECE.2013.6557207
Filename :
6557207
Link To Document :
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