Title :
Short term electric load forecasting using an adaptively trained layered perceptron
Author :
El-Sharkawi, M.A. ; Oh, S. ; Marks, R.J. ; Damborg, M.J. ; Brace, C.M.
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
Abstract :
The authors address electric load forecasting using artificial neural network (NN) technology. They summarize research for Puget Sound Power and Light Company. In this study, several structures for NNs are proposed and tested. Features extraction is implemented to capture strongly correlated variables to electric loads. The NN is compared to several forecasting models. Most of them are commercial codes. The NN performed as well as the best and most sophisticated commercial forecasting systems
Keywords :
load forecasting; neural nets; power engineering computing; Puget Sound Power and Light Company; adaptively trained layered perceptron; artificial neural network; electric load forecasting; short-term forecasting; Artificial neural networks; Economic forecasting; Feature extraction; Load forecasting; Load modeling; Neural networks; Power system modeling; Predictive models; Testing; Weather forecasting;
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.1991.213487
