This paper proposes a novel wavelet transform based technique for prediction of the system marginal price (SMP) of electricity. Daubechies D1(Haar), D2 and D4 wavelet transforms are adopted, and the numerical results reveal that certain wavelet components can effectively be used to identify the SMP characteristics with relation to the system demand in electric power systems. The wavelet coefficients associated with certain frequency and time localization are adjusted using the conventional multiple regression method and then reconstructed in order to predict the SMP for the next scheduling day through a five scale synthesis technique. The outcome of the study clearly indicates that the proposed wavelet transform approach can be used as an attractive and effective means for SMP forecasting.