DocumentCode :
1847807
Title :
Load forecasting using artificial neural networks
Author :
Pham, Khanh D.
Author_Institution :
Elcon Associates Inc., Portland, OR
fYear :
1995
fDate :
30 Apr-2 May 1995
Abstract :
Artificial neural networks, modeled after their biological counterpart, have been successfully applied in many diverse areas including speech and pattern recognition, remote sensing, electrical power engineering, robotics and stock market forecasting. The most commonly used neural networks are those that gain knowledge from experience. Experience is presented to the network in the form of training data. Once trained, the neural network can recognize data that it has not seen before. This paper presents a fundamental introduction to the manner in which neural networks work and how to use them in load forecasting
Keywords :
learning (artificial intelligence); load forecasting; neural nets; power system analysis computing; artificial neural networks; backpropagation; data recognition; fault tolerance; generalisation; load forecasting; neural network architecture; parallel processing; trained neural network; training; transfer function; Artificial neural networks; Biological system modeling; Load forecasting; Neural networks; Pattern recognition; Power engineering; Predictive models; Remote sensing; Robot sensing systems; Speech;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Rural Electric Power Conference, 1995. Papers Presented at the 39th Annual Conference
Conference_Location :
Nashville, TN
Print_ISBN :
0-7803-2043-3
Type :
conf
DOI :
10.1109/REPCON.1995.470937
Filename :
470937
Link To Document :
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