Title :
Short Term Load Forecasting Using Artificial Neural Network
Author :
Banda, E. ; Folly, K.A.
Author_Institution :
Dept. of Electr. Eng., Univ. of Cape Town, Cape Town
Abstract :
This paper presents a method of short term load forecasting using artificial neural network (ANN). A three layered feed-forward neural network, trained by scaled conjugate back-propagation, is used. Two models of ANN were tested and compared. The models are applied to real data from the Cape Town Control Centre.
Keywords :
feedforward neural nets; load forecasting; power engineering computing; Cape Town Control Centre; artificial neural network; scaled conjugate back-propagation; short term load forecasting; three layered feed-forward neural network; Artificial intelligence; Artificial neural networks; Cities and towns; Demand forecasting; Fuzzy logic; Load forecasting; Load modeling; Predictive models; Testing; Weather forecasting; Artificial Intelligence; Artificial Neural Networks; Short-term load forecasting;
Conference_Titel :
Power Tech, 2007 IEEE Lausanne
Conference_Location :
Lausanne
Print_ISBN :
978-1-4244-2189-3
Electronic_ISBN :
978-1-4244-2190-9
DOI :
10.1109/PCT.2007.4538301
