Heating Network Regulation and Intelligent Management Based on Nuclear Heating Technology
With its remark able low-carbon and high-efficiency characteristics,nuclear power heating technology has become one of the important means to achieve the goal of"dual carbon".However,the current nuclear heating load prediction is inaccurate and the control means are single,which cannot meet the needs of this technology development.Therefore,a refined regulation and heat load prediction method based on convolutional cycle attention mechanism model(CGAM)was proposed for nuclear power heating system.Firstly,multi-level data storage technology and data synchronization and optimization strategy were adopted to ensure the real-time and accuracy of production data.Secondly,a deep network was introduced to accurately predict the short-term heat load of the nuclear power heat exchange station and implement strategy adjustment to ensure the prediction accuracy.Finally,multi-level precise control means were set according to the prediction results to complete the intelligent adjustment of the heating system.The method was applied to Dalian Hongyanhe Nuclear Power Heating Project.The experimental results show that the heat load of the actual working conditions is accurately predicted,and the role of precise regulation of the heating system is achieved,which shows the effectiveness of the algorithm.
nuclear power heatingfine regulationheat load forecastingintelligent management
NETL
NSTL
万方数据
维普
