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Machine learning for BMS analysis and optimisation
Engineering Research Express Pub Date : 2020-10-07 , DOI: 10.1088/2631-8695/abbb85
J J Mesa-Jimnez 1, 2, 3 , L Stokes 3 , Q Yang 1 , V N Livina 2
Affiliation  

In large buildings, linking heating, cooling or ventilation systems between themselves and to physical spaces is a very time-consuming task that requires highly skilled engineering knowledge, as all these systems are interconnected and they have a certain influence to each other (ventilation systems are often connected to heating and cooling), which often makes task of locating the sources of error or anomalies very time consuming and difficult as they are performed manually. A different approach would be to work out relationships and equipment linkage from time series data provided by the sensors, thus inferring equipment links from which anomalies can be traced back to the source more easily. This paper proposes a data-based solution to obtain equipment relationships based on cross-correlations to relate Air Handling Units (AHUs) to their respective areas of operation. We also propose a methodology, in particular for AHUs, to identify whether or not to trust correlations based on the difference between supply and return temperature. A case study is presented based a large building with 16 AHU systems.



中文翻译:

用于 BMS 分析和优化的机器学习

在大型建筑中,将供暖、制冷或通风系统与物理空间连接起来是一项非常耗时的任务,需要高度熟练的工程知识,因为所有这些系统都是相互关联的,并且彼此之间具有一定的影响(通风系统是通常连接到加热和冷却),这通常使得定位错误或异常源的任务非常耗时且困难,因为它们是手动执行的。一种不同的方法是从传感器提供的时间序列数据中计算出关系和设备链接,从而推断出设备链接,从中可以更轻松地将异常追溯到源头。本文提出了一种基于数据的解决方案,以基于互相关获取设备关系,从而将空气处理机组 (AHU) 与其各自的操作区域联系起来。我们还提出了一种方法,特别是对于 AHU,可以根据供应和返回温度之间的差异来确定是否信任相关性。案例研究基于具有 16 个 AHU 系统的大型建筑。

更新日期:2020-10-07
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