Industry Electricity Demand Forecast by Integrating Multiple Correlation Analysis Methods

With the high quality development of China, power grid is expected to be more reliable and stable. Industry electricity demand forecasting is extremely important as it takes a high proportion in electricity demand. Taking the influence factor of industry electricity demand into consideration, this paper proposes an industry electricity correlation analysis method based on the combination of association rules mining and principal component analysis, and explores the leading influence factors of industry electricity growth by establishing historical electricity-related analysis data sets. Based on the dominant influence factors obtained by the above two methods, the traditional ARIMA model and multiple regression model are improved respectively, and the industry electricity demand model integrating multiple correlation analysis methods is obtained. Finally, an empirical analysis is carried out by taking the nonferrous metals industry in a certain region as an example. The results show that the improved model has a higher prediction accuracy than the traditional prediction model.