Wavelet and Neural Network Approach to Demand Forecasting Based on Whole and Electric Sub-Control Center Area
Pituk Bunnoon1, Kusumal Chalermyanont2, Chusak Limsakul3
1Pituk Bunnoon, Electrical engineering department, Engineering faculty, Prince of Songkla University, Hadyai, Songkhla, Thailand.
2Kusumal Chalermyanont, Electrical engineering department, Engineering faculty, Prince of Songkla University, Hadyai, Songkhla, Thailand.
3Chusak Limsakul, Electrical engineering department, Engineering faculty, Prince of Songkla University, Hadyai, Songkhla, Thailand.
Manuscript received on November 25, 2011. | Revised Manuscript received on December 14, 2011. | Manuscript published on January 05, 2012. | PP: 81-86 | Volume-1 Issue-6, January 2012. | Retrieval Number: F0273111511/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Whole and electric sub-control area load demand forecasting based on a wavelet transform and a neural network method that are very significant technique for a load prediction. The research used wavelet transform method in preprocessing stage; furthermore, a neural network is used to predict in forecasting stage for whole and sub-control areas prediction. The comparison results show that sub-control area forecasting has a good prediction than that the whole area forecasting based on two levels of wavelet transform. An accuracy of forecast is an essential activity for fuel reserve planning in a power system.
Keywords: Whole area, electric sub-control area, wavelet transform, neural network, forecasting.