A Case Study Based Approach for Remote Fault Detection Using Multi-Level Machine Learning in A Smart Building
A Case Study Based Approach for Remote Fault Detection Using Multi-Level Machine Learning in A Smart Building
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DOI：
10.3390/smartcities3020021
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发表时间：
2020-06-01
期刊：
影响因子：
6.4
通讯作者：
Dudley, Sandra
中科院分区：
文献类型：
--
作者：
Dey, Maitreyee;Rana, Soumya Prakash;Dudley, Sandra
由于人们对绿色倡议、可持续性和居住者福祉等问题的认识不断提高，建筑物正变得越来越智能，但随着智能需求的增加，复杂性和监控也越来越高，最终由人类来完成。建筑物加热通风和空调（HVAC）单元是消耗建筑物能量的大百分比的主要单元之一，例如通过它们参与空间加热和冷却，这是建筑物中最大的能量消耗。通过有效地监控这些组件，可以大大降低建筑物的整体能源需求。由于建筑物管理系统（BMS）的复杂性，许多同时发生的异常行为警告无法及时管理;因此，许多与能源相关的问题无法得到管理，这导致不必要的能源浪费并降低了设备的寿命。提出了一种基于机器学习的多层次自动故障检测系统（MLe-AFD），该系统主要用于远程HVAC风机盘管（FCU）行为分析。该方法采用顺序两阶段聚类来识别FCU的异常行为。该模型的性能通过实施众所周知的统计措施进行验证，并通过专家建筑工程知识进一步交叉验证。该方法在英国伦敦市中心的商业建筑上进行了实验，作为一个案例研究，并允许远程识别三种类型的FCU故障，并在发生故障时主动通知建筑物管理人员;这样，能源消耗可以进一步优化。
Due to the increased awareness of issues ranging from green initiatives, sustainability, and occupant well-being, buildings are becoming smarter, but with smart requirements come increasing complexity and monitoring, ultimately carried out by humans. Building heating ventilation and air-conditioning (HVAC) units are one of the major units that consume large percentages of a building's energy, for example through their involvement in space heating and cooling, the greatest energy consumption in buildings. By monitoring such components effectively, the entire energy demand in buildings can be substantially decreased. Due to the complex nature of building management systems (BMS), many simultaneous anomalous behaviour warnings are not manageable in a timely manner; thus, many energy related problems are left unmanaged, which causes unnecessary energy wastage and deteriorates equipment's lifespan. This study proposes a machine learning based multi-level automatic fault detection system (MLe-AFD) focusing on remote HVAC fan coil unit (FCU) behaviour analysis. The proposed method employs sequential two-stage clustering to identify the abnormal behaviour of FCU. The model's performance is validated by implementing well-known statistical measures and further cross-validated via expert building engineering knowledge. The method was experimented on a commercial building based in central London, U.K., as a case study and allows remotely identifying three types of FCU faults appropriately and informing building management staff proactively when they occur; this way, the energy expenditure can be further optimized.