Knowledge mining collaborative DESVM correction method in short-term load forecasting

Abstract

Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events. A novel short-term forecasting method named TIK was proposed, in which ARMA forecasting model was used to consider the load time series trend forecasting, intelligence forecasting DESVR model was applied to estimate the non-linear influence, and knowledge mining methods were applied to correct the errors caused by irregular events. In order to prove the effectiveness of the proposed model, an application of the daily maximum load forecasting was evaluated. The experimental results show that the DESVR model improves the mean absolute percentage error (MAPE) from 2.82% to 2.55%, and the knowledge rules can improve the MAPE from 2.55% to 2.30%. Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method, it can be proved that TIK method gains the best performance in short-term load forecasting.

Keywords

load forecasting / support vector regression / knowledge mining / ARMA / differential evolution