Prediction and Analysis of Saturated Electricity Consumption Based on Logistic - BP Neural Network

In this paper, the grey relational degree method is used to analyze the various macroeconomic and social factors that affect electricity consumption. The results show that social labor productivity and resident population have the greatest impact on electricity consumption. Logistic - BP neural network is used to predict saturated electricity consumption of Beijing, and the traditional method of using influencing factors to predict is abandoned. The prediction results show that the growth rate of electricity consumption begins to be less than 2% in 2023, and the growth rate is decreasing year by year, which can be considered that the power consumption of Beijing will enter the low-speed saturation stage in 2023. The corresponding development suggestions are put forward at the same time.