DocumentCode
1805782
Title
Artificial neural networks in short term load forecasting
Author
Reinschmidt, K.F. ; Ling, B.
Author_Institution
Stone & Webster Eng. Corp., Boston, MA, USA
fYear
1995
fDate
28-29 Sep 1995
Firstpage
209
Lastpage
214
Abstract
Discusses the use of artificial neural networks to the short term forecasting of loads. In this system there are two types of neural networks. Nonlinear and linear neural networks. The nonlinear neural network is used to capture the highly nonlinear relation between the load and various input parameters. A neural network-based ARMA model is mainly used to capture the load variation over a very short time period. The authors´ system can achieve a good accuracy in short term load forecasting
Keywords
load forecasting; artificial neural networks; linear neural networks; load variation; neural network-based ARMA model; nonlinear neural network; short term load forecasting; Artificial neural networks; Humidity; Intelligent networks; Load forecasting; Load management; Neural networks; Power system modeling; Predictive models; Temperature; Wind speed;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Applications, 1995., Proceedings of the 4th IEEE Conference on
Conference_Location
Albany, NY
Print_ISBN
0-7803-2550-8
Type
conf
DOI
10.1109/CCA.1995.555704
Filename
555704
Link To Document