Multivariate statistic methods for predicting electricity consumption of Beijing

Abstract

In this paper, the influence of economic variables on the annual electricity consumption in Beijing in 2000-2013 has been investigated. The purpose is to find out their relationship with single data sets and multivariate statistic methods. The multivariate methods used in this paper include ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR). In comparison of these methods, the PLSR model is shown to perform better than the others. The effectiveness of the electricity consumption prediction models is illustrated and verified based on the practical data sets of electricity consumption in Beijing.