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Neural Network Based Central Heating System Load Prediction and Constrained Control

机译：基于神经网络的集中供热系统负荷预测与约束控制

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摘要

A neural network (NN) based heating system load prediction and control scheme are proposed. Different from traditional physical principle based load calculation method, a multilayer NN is incorporated with selected input features and trained to predict the heating load as well as the desired supply water temperature in heating supply loop. In this manner, a complicated load calculation model can be replaced by simple but efficient data-driven scheme and the response time to outdoor temperature variation can be enhanced. Moreover, in order to handle the input and output constraints in valve opening degree control task to achieve desired supply water temperature, Barrier Lyapunov candidate function and axillary system technique are involved. An additional NN is employed to approximate the system transfer function with reliable accuracy. The stability of the system is guaranteed through rigorous mathematical analysis. The excellent performance of the novelly proposed control over traditional PID is demonstrated via extensive simulation study. A quantitative case study is also conducted to verify the flexibility and validity of proposed load prediction strategy.
机译：提出了一种基于神经网络的供热系统负荷预测与控制方案。与传统的基于物理原理的负荷计算方法不同，多层神经网络结合了选定的输入特征，并经过训练以预测供热回路中的热负荷以及所需的供水温度。以此方式，可以用简单但有效的数据驱动方案代替复杂的负荷计算模型，并可以提高对室外温度变化的响应时间。此外，为了处理阀门开度控制任务中的输入和输出约束以实现所需的供水温度，还涉及了屏障Lyapunov候选函数和辅助系统技术。采用附加的NN来以可靠的精度近似系统传递函数。通过严格的数学分析可以保证系统的稳定性。通过广泛的仿真研究证明了新颖提出的对传统PID的控制的出色性能。还进行了定量案例研究，以验证所提出的负荷预测策略的灵活性和有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第2期|2908608.1-2908608.14|共14页
  • 作者

    Wang Hongwei; Tu Fangwen; Tu Baofeng; Feng Guohui; Yuan Guangming; Ren Hao; Dong Jiarong;

  • 作者单位

    Shenyang Jianzhu Univ, Sch Municipal & Environm Engn, Shenyang 110168, Liaoning, Peoples R China;

    Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore;

    China Northeast Architectural Design & Res Inst C, Design Div 3, Shenyang 110003, Liaoning, Peoples R China;

    Shenyang Jianzhu Univ, Sch Municipal & Environm Engn, Shenyang 110168, Liaoning, Peoples R China;

    Shenyang Jianzhu Univ, Sch Municipal & Environm Engn, Shenyang 110168, Liaoning, Peoples R China;

    Shenyang Jianzhu Univ, Sch Municipal & Environm Engn, Shenyang 110168, Liaoning, Peoples R China;

    Shenyang Jianzhu Univ, Sch Municipal & Environm Engn, Shenyang 110168, Liaoning, Peoples R China;

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