Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression
Electric load forecasting is an important issue for power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non linear learning capability of support vector regression (SVR) this paper presents a SVR model hybridized with the differential empirical mode decomposition (DEMD) method and auto regression (AR) for electric load forecasting. The differential EMD method is used to decompose the electric load into several detail parts associated with high frequencies (intrinsic mode function (IMF) and an approximate part associated with low frequencies. The electric load data from the New South Wales (NSW, Australia) market and the New York Independent System Operator (NYISO, USA) are employed for comparing the forecasting performances of different alternative models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Neurocomputing