Model predictive control of non-domestic heating using genetic programming dynamic models
Model predictive control of non-domestic heating using genetic programming dynamic models
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DOI：
10.1016/j.asoc.2020.106695
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发表时间：
2020-12-01
影响因子：
8.7
通讯作者：
Saber, Esmail
中科院分区：
文献类型：
--
作者：
Dou, Tiantian;Lopes, Yuri Kaszubowski;Saber, Esmail
提出了一种新的方法，获得动态非线性模型，使用遗传规划（GP）的模型预测控制（MPC）的室内温度的建筑物。目前，由于专家控制工程师设计和调整预测模型所涉及的时间和成本，在建筑物中大规模采用MPC在经济上不可行。我们表明，GP是能够自动化这个过程中，并进行开环系统识别的数据产生的工业级建筑模拟器。模拟的建筑物受到幅度调制伪随机二进制序列（APRBS），它允许收集的数据是足够的信息，以捕捉相关的操作条件下的底层系统动态。在这份初步报告中，我们详细介绍了我们如何采用GP构建MPC的预测模型，用于在模拟中加热单区建筑物，并报告使用该模型控制模拟的单区域建筑物的内部环境条件的结果。我们的结论是，GP显示出很大的希望，生产模型，使MPC的建设，以实现所需的温度带在一个单一的区域空间。(C)2020爱思唯尔B.V.保留所有权利。
We present a novel approach to obtaining dynamic nonlinear models using genetic programming (GP) for the model predictive control (MPC) of the indoor temperatures of buildings. Currently, the largescale adoption of MPC in buildings is economically unviable due to the time and cost involved in the design and tuning of predictive models by expert control engineers. We show that GP is able to automate this process, and have performed open-loop system identification over the data produced by an industry grade building simulator. The simulated building was subject to an amplitude modulated pseudo-random binary sequence (APRBS), which allows the collected data to be sufficiently informative to capture the underlying system dynamics under relevant operating conditions.In this initial report, we detail how we employed GP to construct the predictive model for MPC for heating a single-zone building in simulation, and report results of using this model for controlling the internal environmental conditions of the simulated single-zone building. We conclude that GP shows great promise for producing models that allow the MPC of building to achieve the desired temperature band in a single zone space. (C) 2020 Elsevier B.V. All rights reserved.