A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors

Abstract

To accurately predict the seasonal fluctuations of the electricity consumption of the primary economic sectors, we propose a seasonal grey model (SGM(1,1) model) based on the accumulation operators generated by seasonal factors. We use the proposed model to carry out an empirical analysis based on the seasonal electricity consumption data of the primary industries in China from 2010 to 2016. The results from the SGM (1,1) model are compared with those obtained using the grey model (GM(1,1)), the particle swarm optimization algorithm combines with the grey model (PSO-GM(1,1) model), and the adaptive parameter learning mechanism based seasonal fluctuation GM (1,1) model (APL-SFGM(1,1) model). The results of the comparison show that the SGM(1,1) model can effectively identify seasonal fluctuations in the electricity consumption of the primary industries and its prediction accuracy is significantly higher than those of the GM(1,1), PSO-GM(1,1) and APL-SFGM(1,1) models. The forecast results for China from 2017 to 2020 obtained using the SGM(1,1) model suggest that the electricity consumption of the primary industries is expected to increase slightly, but obvious seasonal fluctuations will still be present. It is forecasted that the annual electricity consumption in 2020 will be 107.645 TWh with an annual growth rate of 2.83%. This prediction can provide the basis for power-supply planning to ensure supply and demand balance in the electricity markets.