Artificial neural networks applied to long-term electricity demand forecasting

Proceedings - HIS'04: 4th International Conference on Hybrid Intelligent Systems

Volumn , Issue , 2005, Pages 204-209

  Mamun, Mostafa Al ^a   Nagasaka, Ken ^a  

^a TOKYO UNIVERSITY OF AGRICULTURE AND TECHNOLOGY   (Japan)

Author keywords

Economic Factors; Electric Load Demand; Long term Load Fore casting; Radial Basis Function Net works

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS; ELECTRIC LOADS; ELECTRIC POWER DISTRIBUTION; RADIAL BASIS FUNCTION NETWORKS;

ECONOMIC SITUATION; ELECTRIC POWER DEMAND;

NEURAL NETWORKS;

EID: 28344435792     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

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2. B. Kermanshahi, et al.,"quot;Artificial Neural Network for Forecasting Daily Load of a Canadian Electric Utility"quot;, IEEE 2nd International Forum on Application of Neural Networks to Power Systems (ANNPS' 93), Yokohama, Japan, (April 1993) 302-307.
3. Bahman S. Kermanshahi and Hiroshi Iwamiya, "quot;Up to year 2020 load forecasting using Neural Nets"quot;,Dept. of Electronics & Information Engineering, Tokyo University of Agriculture & Technology.
4. D.K.Ranaweera, N.E. Hubele, A.D. Papalexopou los, "quot;Application of radial basis function neural network model for short-term load forecasting,"quot;, IEEE Proc.-Gener. Transm Distrib., Vol. 142, No. 1, January 1995.
5. Neural Connection 2.0 User's Guide, Marketing Dept., SPSS Inc. / Recognition Systems Inc.