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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