A Study of China's Electricity Supply Based on Correlation and Time Series Modeling

Abstract

In this study, the relationship between electricity supply and various indicators is deeply explored through multi-factor correlation analysis, quantitative analysis and prediction modeling. First, the Pearson correlation coefficient analysis was used, and it was revealed that electricity supply is significantly and positively correlated with gross domestic product (GDP), per capita GDP, urban population ratio, and consumption level of residents. Subsequently, linear regression methods were used for quantitative analysis, and forecasting models for electricity supply were established, including ARIMA model and Holt-Winters seasonal exponential smoothing model. Finally, the forecast value of China's electricity supply for the next 37 years was derived by synthesizing the forecasting models. The results of this study provide an important reference for optimizing the structure of electric power supply and improving the supply efficiency, and are of guiding significance for the formulation of relevant policies and planning.