Industry Economic Monitoring and Forecasting Model Based on Electrical Energy Use Data

This article investigates the relationship between electrical energy use data and industrial economic data over the period from 2013 to 2023. To quantify this relationship, an economic growth sentiment index calculation model based on electrical energy use data is developed. The article then applies this model to an eastern province of China, using empirical data to assess the economic prosperity of the electricity industry. The analysis applies the time difference correlation coefficient method to explore the relationship between electrical energy use and various industrial economic indicators, including those specific to the manufacturing sector. Based on this analysis, the study identifies leading, consistent, and lagging indicators within industrial economic data. Subsequently, based on the composite index method, a calculation model for the industrial economic growth sentiment index is constructed to analyze and study the industrial development trend of the province. The research results indicate a significant correlation between electricity data and industrial economic data, and the calculation model of the industrial economic growth sentiment index based on electrical energy use data can effectively reflect the changing trend of industrial economic indicators. The leading group indicators selected based on the time difference correlation coefficient method can predict economic growth or decline in advance, providing decision-makers with forward-looking warning signals and better supporting policy formulation and economic forecasting.