Research on steam load forecasting based on optimized BP neural network
XUE Deshi
JIA Lichao
ZHANG Yifan
CAO Qi
Abstract：The total steam sales volume of the thermal power plant in the next 24 hours and the steam demand volume of heat users are predicted.Two optimization models,namely MEA-BP neural network and PSO-BP neural network,are adopted to collect the historical data of the thermal power plant for one year,including the total daily steam consumption of heat users,the total steam sales volume of the thermal power plant,and weather parameters.And through data preprocessing,correlation analysis and normalization processing,a steam load forecasting model for heat users is constructed.The results show that the optimized BP neural network model has higher prediction accuracy and stability,can effectively reflect the heat consumption characteristics of heat users and the changing trend of steam load,and can provide a reference for energy management and energy conservation and emission reduction in thermal power plants.
Keywords：big data analyticssteam load forecastthermal characteristicsprediction modelBP neural network
Publication Date：2025-05-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 74-79 )