Title :
Artificial neural networks in short term load forecasting
Author :
Reinschmidt, K.F. ; Ling, B.
Author_Institution :
Stone & Webster Eng. Corp., Boston, MA, USA
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;
Conference_Titel :
Control Applications, 1995., Proceedings of the 4th IEEE Conference on
Conference_Location :
Albany, NY
Print_ISBN :
0-7803-2550-8
DOI :
10.1109/CCA.1995.555704
