Study on Intelligent Optimization Model Based on Grey Relational Grade in Long–Medium Term Power Load Rolling Forecasting

Abstract

According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed that the method in this paper is superior to conventional method, so it is worth to be extended and applied.