DocumentCode :
1876875
Title :
Forecasting of electricity consumption: a comparative analysis of regression and artificial neural network models
Author :
Fung, Y.H. ; Tummala, V. M Rao
Author_Institution :
Dept. of Electr. & Electron. Eng., Hong Kong Univ., Hong Kong
fYear :
1993
fDate :
7-10 Dec 1993
Firstpage :
782
Abstract :
Several authors have formulated regression models to forecast electricity consumption. Also, more recently, several authors have attempted to formulate artificial neural network models to forecast electricity consumption. The authors have attempted in this paper to formulate and estimate both regression and artificial neural network models to forecast the electricity consumption for Hong Kong. They found that artificial neural network model forecasts are generally at least as good as those generated by the multiple linear regression model
Keywords :
load forecasting; neural nets; power consumption; power system analysis computing; statistical analysis; Hong Kong; artificial neural network models; electricity consumption; load forecasting; multiple linear regression model;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Advances in Power System Control, Operation and Management, 1993. APSCOM-93., 2nd International Conference on
Conference_Location :
IET
Print_ISBN :
0-85296-569-9
Type :
conf
Filename :
292624
Link To Document :
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