A hybrid forecasting method of electricity consumption based on trend extrapolation theory and LSSVM

摘要

By mining electricity data characteristics and its relationship with environmental variables, this paper defines the electricity month-season ratio (EMSR) based on trend extrapolation and proposes a hybrid prediction method combining LSSVM and EMSR. The actual electricity consumption data of A Province is used for the case study. The prediction error of three methods are compared which are BP neural network, Elman neural network and EMSR-LSSVM hybrid method. It shows that the proposed algorithm can significantly improve the prediction accuracy.