Title :
Wavelet neural network based short term load forecasting of electric power system commercial load
Author :
Oonsivilai, Anant ; El-Hawary, M.E.
Author_Institution :
Dept. of Electr. & Comput. Eng., Dalhousie Univ., Halifax, NS, Canada
Abstract :
In an electric power system, the system load consists of domestic, commercial, industrial and municipal load sectors. This paper presents an approach for predicting electric power system commercial load using a wavelet neural network. Morlet and Mexican hat wavelets are used to generate the transfer functions of hidden layer nodes of the neural network. A wavelet neural network is trained for a particular power system load. Results show that wavelet neural networks may outperform traditional architectures in approximation and forecasting problems related to electric power system.
Keywords :
learning (artificial intelligence); load forecasting; neural nets; power system analysis computing; transfer functions; wavelet transforms; Mexican hat wavelets; Morlet wavelets; commercial load sector; domestic load sector; electric power system commercial load; hidden layer nodes; industrial load sector; municipal load sector; neural network; neural network training; short term load forecasting; transfer functions; wavelet neural network; Artificial intelligence; Artificial neural networks; Expert systems; Load forecasting; Neural networks; Neurons; Power system modeling; Power system security; Predictive models; Transfer functions;
Conference_Titel :
Electrical and Computer Engineering, 1999 IEEE Canadian Conference on
Conference_Location :
Edmonton, Alberta, Canada
Print_ISBN :
0-7803-5579-2
DOI :
10.1109/CCECE.1999.804865
