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Research on temperature response of secondary loop of district heating system with XGBoost
编号：374 访问权限：仅限参会人 更新：2022-05-20 17:02:02 浏览：212次 张贴报告

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摘要
District heating system is an important part of the urban energy system. But its large scale and numerous coupling variables bring many difficulties to its regulation and control. At present, rough regulation carried out by manual experience is hard to guarantee the quality of regulation, thus caused problems about heating comfort and efficiency. This paper proposes a data-driven temperature response prediction model to predict secondary supply temperature considering the historical operating status of heating substation, valve opening degree, and weather conditions, etc. XGBoost model was established under different input step and prediction step respectively are proposed and tested in this article. Results show that the prediction performance of the XGBoost model of 72 input steps and 24 prediction steps is best, with a mean square error of 0.117°C, then it is applied to an urban central heating system for an application example. Based on this data-driven model, different operation strategies on primary loop valve opening are compared for temperature response analysis. Operators can check the actual temperature response of the valve control strategy before they are about to apply.
 
关键词
data-driven model, machine learning, secondary supply temperature prediction
报告人
Encheng Feng
浙江大学

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重要日期

  • 会议日期

    05月27日

    2022

    至

    05月29日

    2022

  • 02月28日 2022

    初稿截稿日期

  • 05月29日 2022

    注册截止日期

  • 06月22日 2022

    报告提交截止日期

主办单位
IEEE Beijing Section
China Electrotechnical Society
Southeast University
协办单位
IEEE Industry Applications Society
IEEE Nanjing Section
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