系统仿真学报 ›› 2021, Vol. 33 ›› Issue (1): 180-188.doi: 10.16182/j.issn1004731x.joss.19-0148

• 国民经济仿真 • 上一篇    下一篇

基于MPC方法的供热系统一次侧流量实时预测

李仲博1, 贾萌1, 康焱1, 王海鸿1, 李淼2, 吕青2, 谢晶晶3, 方大俊3,*   

  1. 1.北京市热力集团有限责任公司,北京 100028;
    2.北京华热科技发展有限公司,北京 100028;
    3.常州英集动力科技有限公司,江苏 常州 213022
  • 收稿日期:2019-04-10 修回日期:2019-08-31 发布日期:2021-01-18
  • 通讯作者: 方大俊(1988-),男,硕士,研究方向为智慧能源系统优化。E-mail:2746813098@qq.com
  • 作者简介:李仲博(1982-),男,博士,高工,研究方向为智慧能源系统优化。E-mail:digilee@126.com

Real-Time Prediction of Primary Flow by MPC Method in Heating System

Li Zhongbo1, Jia Meng1, Kang Yan1, Wang Haihong1, Li Miao2, Lü Qing2, Xie Jingjing3, Fang Dajun3,*   

  1. 1. Beijing District Heating Group, Beijing 100028, China;
    2. Beijing HuaRe Technology Limited Company, Beijing 100028, China;
    3. Changzhou Engi Power Technology Limited Company, Changzhou 213022, China
  • Received:2019-04-10 Revised:2019-08-31 Published:2021-01-18

摘要: 基于模型预测控制方法,使用离散的受控自回归模型建立二级网动态热传输滞后模型与热力站模型,结合机器学习算法中的多项式拟合方法对二级网模型和热力站模型中的参数进行辨识校准,并基于模型结果对未来工况条件下的热力站一次侧流量进行预测,为供热系统质量调节提供依据。使用实测数据对模型进行了验证,实际偏差在5%以下,为供热系统流量调节的工程实践提供了良好的指导。

关键词: 供热系统, 热惯性, 模型预测控制, 动态模型, 流量预测

Abstract: Based on the model predictive control method, this paper uses the discrete controlled autoregressive model to establish the dynamic heat transfer delay model of the secondary network and the thermal station model. The polynomial fitting method of machine learning algorithm is applied to identify and calibrate the parameters of the secondary network model and the thermal station model. The primary flow rate of the heating station under future operating conditions is predicted based on the model results, which provides a basis for the quality-based regulation of heating system. The model is verified by measured data, and the actual deviation is less than 5%, which provides a good guide for the engineering practice of heating system flow regulation.

Key words: heating system, thermal inertia, Model Predictive Control(MPC), dynamic model, flow rate prediction

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