Research On Quantitative Heating Of Heat Exchange Station Based On Thermal Load Forecast

With the rapid development of the economy,people increase requirements on the quality of life for heating.To a certain extent,the appearance of unit heat metering and measuring instruments meet the desire of people for regulating heating independently.But with constraints of process conditions and cost performance of instrument itself,the current quantitative heating is mainly regulated from heat exchange station.Obviously,some problems exist in the operation of heat exchange station,such as relying on manual experience,unreasonable parameter setting,mismatch of control algorithms,and unsuitable function of the heating platform.The emergence of these problems lead to the failure to meet the requirements of people for heating comfort.Therefore,this paper studies on the heating system of heat exchange station on demand heating.It is not only makes a beneficial exploration in thermal energy theory,but also gets effective verification in practice.This paper focuses on analysis and improvement of the unreasonable parameter setting of heat exchange station from two aspects.Firstly,the heat load forecasting model of the heat exchange station is designed.The support vector machine(SVM)and the random forest are used as the load forecasting algorithm.The heat load forecasting model iscarried out respectively.The R2 is used as the performance test index to analyze and compare the load forecast of the two algorithms.Precisely,the latter algorithms is more suitable for heat load station heat load prediction.Secondly,in order to accurately identify the temperature feedback parameters of the secondary network,the characteristic equation is determined in this research.Based on the actual working conditions,the future parameters and historical parameters are introduced in the construction of the characteristic equation.This is to make the secondary network supply and return water temperature given parameter group setting not only contains the incremental information for the future heating demand,but also considers large hysteresis and large inertia characteristics of the thermal comfort in the building..That is,the amount of influence of stock information on it.Aiming at the problem of system control algorithm mismatch in heat exchange station,this paper use the five-state Bang-Bang control algorithm as the core part.And the adaptive Bang-Bang algorithm is designed based on the gain adaptive control theory.Firstly,it is proved on the MATLAB/SIMULINK simulation platform that the adaptive Bang-Bang has better performance and wider control domain than the former.Then,the feasibility and security of the adaptive Bang-Bang algorithm are verified on the hardware simulation platform through the integrated control test box,and the characteristics that this algorithm can be widely applied in many control fields are also proved.Finally,the effectiveness and accuracy of the adaptive Bang-Bang algorithm are confirmed through the heating field verification.Due to the inapplicability of existing heating platforms,this research sets heating load forecasting tasks on the original basis,and forms a multi-level heating monitoring service system with heating load forecasting module as heating guidance,client system as monitoring,and field control end as executing unit.Primarily,the research regards the heating monitoring service system as the core part.The adaptive Bang-Bang algorithm is used to carry out the relevant temperature control experiments in the field heat exchange station A.As the main factor,the thermal comfort index is evaluated by evaluating the indoor temperature of the hot user.This study compares and analyses two control experiments.One is the optimal parameters based on thermal load prediction.The other is based on user statistical heat demand and thermal load prediction compensation.The results show that the latter experimental effect is better,which can ensure that the indoor temperature of the hot user is close to the comfortable temperature of the hot user at 22.g° C in a certain period of time.Therefore,the purpose of quantifying heating and on-demand heating of the heat exchange station system is realized.