Forecasting Chinese Electricity Consumption Based on Grey Seasonal Model with New Information Priority

Abstract

The total electricity consumption in China includes almost all the electricity consumption from all fields, which can reflect the overall situation of China’s electricity consumption, and it is of great significance to forecast it. This paper develops a novel grey Holt-Winters model based on the new information priority cycle accumulation operator, known as the NCGHW model for short, in order to effectively forecast the total electricity consumption in China. First of all, this paper proposes the new information priority cycle accumulation operator to mine the internal law of data while maintaining periodicity in the accumulated data. Then, based on the one-order accumulation sequence generated by the new operator, the framework of the Holt-Winters model is used to build a new model. Finally, according to the characteristics of the data itself, the LBFGS algorithm is used to find the most suitable parameters for the model. In order to model and analyze the fine-grained measurement of the total electricity consumption in China, we study the monthly and quarterly data, respectively. The new model and the contrast models are applied to the two sequences for simulation and prediction. The performance of the model is discussed through relevant evaluation criteria. The results show that the new model has sufficient capacity to forecast the monthly and quarterly total electricity consumption. It is the best choice for the total electricity consumption in China.