Research on data-driven optimization and regulation of heating systems
In view of the situation that the simple regulation method in the operation of traditional heating system causes energy waste,a machine learning regulation model aiming at minimizing the energy consumption of transmission and distribution is constructed based on the system operation data,and a data driven optimization regulation method for heating system is proposed.A unit valve control model,short-term heat load prediction model,and heat exchange station flow control model were established through machine learning.The optimal model parameter combination was obtained through research and analysis of each model.Through experimental comparison of operational data before and after optimiza-tion,the results show that:under the condition of constant heat supply,the average circulating flow rate and pump consumption after optimization are significantly reduced,and the energy-saving rate of pump consumption reaches 38%,which has a good energy-saving effect.