Mid-Long Term Electricity Consumption Forecasting Analysis Based on Cyber-Physical-Social System Architecture

Considering that the economic indicators have a great impact on electricity consumption, social sensors are used to capture massive social signals and intelligent sensors are used to collect electricity data based on the cyber-physical-social system(CPSS) theoretical framework. In this paper, an economic-power cyber-physical-social system is built to integrate the data from different spaces. In cyberspace, the data will be fused, normalized before training, then a deep belief network(DBN) model is established to perform data mining and realize mid-long term electricity consumption forecasting. In the DBN training process, economic-power data from 31 provinces are used. DBN can achieve feature extraction automatically without variable selection steps and can achieve higher forecasting accuracy than traditional methods. The application of CPSS in electricity consumption forecasting has expanded the data border of physical power system researches and can provide a reference for subsequent multi-space data modeling.