Accurate Disaggregation of Chiller Plant Loads Using Noisy Magnetic Field Measurements

C. Hau, Binbin Chen, Ziling Zhou, W. G. Temple
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引用次数: 1

Abstract

Chiller plants consume a significant amount of energy around the world. While there have been well established systems for monitoring their performance, those existing systems are expensive and difficult to install. In this work, we propose a low-cost and non-intrusive approach to achieve accurate monitoring of individual loads in a chiller plant. Our proposed system complements the in-situ aggregated power measurement with additional magnetic field measurements near individual branches using non-intrusive Anisotropic Magnetoresistive (AMR) sensors. AMR sensor measurements help disaggregate smaller pump loads in the system. However, they are subject to various noise from the environment, which can significantly lower the estimation accuracy. To overcome this, we design a novel hybrid solution that combines the AMR-based method with a multi-layer neural network (MLNN)-based method. Specifically, we use filtered outputs (under some quality check) from the AMR-based method to help train the MLNN, and then we combine the outputs from both the AMR-based method and the MLNN-based method to achieve better estimation results. We tested our proposed solution using real world power consumption traces collected from the chiller plant of a six-story commercial building, and under varying level of environmental noise. Our approach can robustly achieve low mean-absolute error (1% for the chiller component and 3% for individual water pump loads).
利用噪声磁场测量对冷水机组负荷进行精确分解
世界各地的冷水机组消耗了大量的能源。虽然已经建立了完善的系统来监测它们的性能,但这些现有的系统既昂贵又难以安装。在这项工作中,我们提出了一种低成本和非侵入性的方法来实现对冷水机组单个负荷的准确监测。我们提出的系统使用非侵入式各向异性磁阻(AMR)传感器在单个支路附近进行额外的磁场测量,补充了原位汇总功率测量。AMR传感器测量有助于分解系统中较小的泵负载。然而,它们受到来自环境的各种噪声的影响,这会大大降低估计的精度。为了克服这个问题,我们设计了一种新的混合解决方案,将基于amr的方法与基于多层神经网络(MLNN)的方法相结合。具体来说,我们使用基于amr的方法的过滤输出(在一些质量检查下)来帮助训练MLNN,然后我们将基于amr的方法和基于MLNN的方法的输出结合起来,以获得更好的估计结果。我们使用从一栋六层商业建筑的冷水机组收集的真实世界的电力消耗轨迹,并在不同程度的环境噪声下测试了我们提出的解决方案。我们的方法可以实现较低的平均绝对误差(冷水机组组件为1%,单个水泵负荷为3%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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