26 February 2010 Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm
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Proceedings Volume 7546, Second International Conference on Digital Image Processing; 75460B (2010) https://doi.org/10.1117/12.853203
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore
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.
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Pituk Bunnoon, Pituk Bunnoon, Kusumal Chalermyanont, Kusumal Chalermyanont, Chusak Limsakul, Chusak Limsakul, } "Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75460B (26 February 2010); doi: 10.1117/12.853203; https://doi.org/10.1117/12.853203
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