Short term load forecasting using non-linear template matching

Abstract

Accurate short term load forecasting plays a very important role in power system management. As electrical load data is highly non-linear in nature, in the proposed approach, we first use a Reproducing Kernel Hilbert Space (RKHS) method to fit the data. Afterwards a template is constructed based on the input-output data and the results from the RKHS method. To predict the load, only the template is used with no additional RKHS calculations. The proposed method is compared to a Support Vector Machine (SVM) prediction. Results show that the proposed method predicts much more accurate than the SVM.