节能2025，Vol.44Issue(5)：74-79,6.DOI:10.3969/j.issn.1004-7948.2025.05.020
基于优化BP神经网络的蒸汽负荷预测研究
Research on steam load forecasting based on optimized BP neural network
薛德师 1贾立超 2张逸凡 3曹琦2
作者信息
- 1. 河北省特种设备技术检查中心,河北 石家庄 050000
- 2. 大连理工大学能源与动力学院,辽宁 大连 116024
- 3. 大连理工大学白俄罗斯国立大学联合学院,辽宁 大连 116024
- 折叠
摘要
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.关键词
大数据分析/蒸汽负荷预测/用热特性/预测模型/BP神经网络Key words
big data analytics/steam load forecast/thermal characteristics/prediction model/BP neural network分类
信息技术与安全科学引用本文复制引用
薛德师,贾立超,张逸凡,曹琦..基于优化BP神经网络的蒸汽负荷预测研究[J].节能,2025,44(5):74-79,6.
