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A data driven method for optimal sensor placement in multi-zone buildings

机译：多区建筑中最优传感器放置的数据驱动方法

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摘要

In this paper, we propose a data-driven methodology to identify the optimal placement of sensors in a multi-zone building. The proposed methodology is based on statistical tests that study the (in) dependence of measurements from various available sensors. The tests advice on a set of most dissimilar sensors to be retained, as they would convey the maximum information. The method starts with an initial setup that can provide measurements of every building zone to carry out this study; any of these sensors can be removed eventually to decrease costs in normal operation. The method has the advantages of being purely data driven and computationally efficient, as against several methods proposed in the scientific literature, that operate under the premise that detailed building models are available, to evaluate the number/position of the required sensors. This property makes the method scale to different buildings, in an expert free manner. The methodology can help towards better characterization of a building for optimal control and monitoring applications. It is validated against a widely used method & ndash; Kalman filtering with Grey-box models, using two different case studies. In both cases, the proposed approach agrees with the results using grey box models, suggesting that the method is reliable, while being quick and efficient.(c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
机译：在本文中，我们提出了一种数据驱动方法，以识别多区建筑中传感器的最佳放置。所提出的方法基于统计测试，研究了从各种可用传感器的测量值的依赖性。测试关于一组最具不同传感器的建议，因为它们会传达最大信息。该方法从初始设置开始，可以提供每个建筑区域的测量来执行这项研究;最终可以删除任何这些传感器以降低正常操作的成本。该方法具有纯粹的数据驱动和计算效率的优点，因为在科学文献中提出的几种方法，该方法在提供详细的建筑模型可用的前提下，可以评估所需传感器的数量/位置。此属性以专家的自由方式使方法缩放到不同的建筑物。该方法可以帮助更好地表征建筑物以获得最佳控制和监测应用。它针对广泛使用的方法和ndash验证;卡尔曼用灰度盒式型号，使用两个不同的案例研究。在这两种情况下，所提出的方法同意使用灰色盒式模型的结果，表明该方法可靠，同时快速有效。（c）2021作者。由elsevier b.v发布。这是CC下的开放式访问文章，由许可证（http：// creativecommons.org/licenses/by/4.0/）。

著录项

  • 来源
    《Energy and Buildings》 |2021年第7期|110956.1-110956.10|共10页
  • 作者

    Suryanarayana Gowri; Arroyo Javier; Helsen Lieve; Lago Jesus;

  • 作者单位

    VITO NV Algorithms Modeling & Optimizat Boerentang 200 Mol Belgium|EnergyVille Thor Pk Genk Belgium;

    VITO NV Algorithms Modeling & Optimizat Boerentang 200 Mol Belgium|EnergyVille Thor Pk Genk Belgium|Katholieke Univ Leuven Dept Mech Engn Leuven Belgium;

    EnergyVille Thor Pk Genk Belgium|Katholieke Univ Leuven Dept Mech Engn Leuven Belgium;

    VITO NV Algorithms Modeling & Optimizat Boerentang 200 Mol Belgium|EnergyVille Thor Pk Genk Belgium|Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Optimal sensor placement; Building sensing; Building modeling; Grey-box modeling; Data driven; BOPTEST;

    机译：最佳传感器放置;建筑感应;建筑建模;灰度盒建模;数据驱动;BEPTEST;

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