基于数据驱动与机理模型融合的热网水力平衡分析方法

Hydraulic Balance Analysis Method of Heating Grid Based on Data-driven and Mechanism Model Fusion

随着环保要求的不断提高,城市集中供暖小锅炉被逐步关停,并被接入城市主干网,热网不断扩张。与此同时,热量的生产也运用地热、太阳能、工业余热、电热等多种热源,使得集中供热系统变得更加复杂。靠传统手工运算方式、或者理想机理建模方式较难对热网的结构设计及运行进行科学优化,需要通过计算机仿真建模的手段,并结合实际热网运行的数据对热网进行阻力特性辨识,才能真正起到有效的作用。本文研究了基于数据驱动与机理模型融合的集中供热网水力平衡分析模型,并利用来自热网SCADA运行数据通过多种机器学习算法对先验知识模型的参数进行学习优化,最终建立与真实热网相匹配的水力分析模型,此种方法可为热力企业的热网结构优化改造、经济运行提供技术参考。

With the continuous improvement of environmental protection requirements, small boilers for urban central heating are gradually shut down and their heating network are connected to the urban backbone network,therefore,the urban central heating network continues to expand. At the same time, heat production also uses multiple heat sources such as geothermal, solar, industrial waste heat, and electric heating which make the central heating system more complicated. It is difficult to scientifically optimize the structure design and operation of heat supply network by traditional manual calculation methods or ideal mechanism modeling methods. In order to really play an effective role, it is necessary to identify the resistance characteristics of the heat supply networks by means of computer simulation modeling and combined with the actual heating grid operation data. This paper studies the hydraulic balance analysis model of the central heating network based on the fusion of data-driven and mechanism model, uses the operation data from the SCADA of heating grid to learn and optimize the parameters of the prior knowledge model through a variety of machine learning algorithms, and finally establishes the hydraulic analysis model matching with the real heating grid. This method can be used for the structural optimization, transformation and economy of the heating network of thermal enterprises Provide technical reference for operation.