Application of a hybrid model on short‐term load forecasting based on support vector machines (SVM)

摘要

Abstract To overcome the shortcoming of single train set of support vector machines (SVM), a novel hybrid model based on dual support vector machines (DSVM) is presented in this paper. The first SVM takes the recent samples in the vicinity of the demand day as its train samples. It can capture the most recent dynamic changing load. The second one takes the same season's load samples in historic years that have the similar attributes with the demand days as its train samples to reflect the season‐period rule. Final results can be archived by converging both SVM. The raw dataset related to experiments was obtained from the EUNITE network. The experiments have proved that the accuracy of proposed model is better than traditional one in general and showed this model's feasibility in practical application.