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