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Proceedings Paper

Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm
Author(s): Pituk Bunnoon; Kusumal Chalermyanont; Chusak Limsakul
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Paper Abstract

This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

Paper Details

Date Published: 26 February 2010
PDF: 9 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75460B (26 February 2010); doi: 10.1117/12.853203
Show Author Affiliations
Pituk Bunnoon, Prince of Songkla Univ. (Thailand)
Kusumal Chalermyanont, Prince of Songkla Univ. (Thailand)
Chusak Limsakul, Prince of Songkla Univ. (Thailand)

Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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