The Central Heating System Dynamic Model Of The Heat Storage And Heat Load Forecasting Research

Central heating has become the most important way of heating in the northern region,but the amount of coal and electricity consumption is too large,so in order to achieve the consumption of wind power cogeneration optimization.In this paper,according to the central heating system between a unit of hot users and the district's heat transfer in Changchun City,setting up various parts of the mathematical model and calculating the simulation.And according to a load temperature of the heat transfer station at the water supply in Changchun city,using the neural network technology to predict,and providing a data support for the adjustment of the central heating system.The main research content and work as follows.(1)In order to research and discuss the dynamic characteristics of the central heating system,three physical links which include of heat exchanger,central heating system,two pipe network and heat users are analyzed theoretically,and according to the principle of heat transfer to determine the heat balance equation of each link,based on the research results not being affected,ignore some secondary factors,the study on simplifying assumptions,then establish mathematical model of each physical link.(2)When a pipe network is closed,the heat of the two pipe network and building envelope is released to maintain the indoor temperature of the heat users.However,the heat storage is certain,the indoor temperature will be reduced with the passage of time.Therefore,based on the analysis of the temperature field of the lumped parameter method,the function relation expression between temperature and time is determined.(3)According to the theoretical study of the central heating system,in the case of no change in outdoor temperature and ignoring the effect of solar radiation on indoor temperature,build the corresponding simulation model by using Simulink toolbox in MATLAB,and the parameters of the design conditions are simulated;And when the pipe network is closed,respectively,to discuss whether the second pipe network outage,the indoor temperature of the user to reduce the situation,and to be concluded,the second pipe network to continue to run,the user can slow down the rate of decrease in indoor temperature,to maintain a longer period of comfort.(4)Based on the historical data of primary water inlet temperature of a district heat transfer station in Changchun City,BP neural network tool is used to train the heat load temperature.The training results show that the method can predict the heat load temperature well,and the prediction result can provide an evidence for the adjustment of the central heating system.