Title :
Recurrent neural networks and load forecasting
Author :
Connor, Jerome T. ; Atlas, Les E. ; Martin, Doug
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
Abstract :
The ability of a recurrent network to model load forecasting is investigated. Its performance in a competition is then contrasted with that of feedforward networks and linear models. Its weaknesses and strengths are then analyzed to give guidelines to the design of neural net predictors with the hope of designing better predictors in the future
Keywords :
feedforward neural nets; load forecasting; power engineering computing; feedforward networks; linear models; load forecasting; neural net predictors; recurrent neural networks; Interactive systems; Load forecasting; Neural networks; Neurofeedback; Neurons; Predictive models; Recurrent neural networks; State-space methods; Statistics; Technological innovation;
Conference_Titel :
Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0065-3
DOI :
10.1109/ANN.1991.213491
