Abstract
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.
Highlights
With the increasing demands for smart building infrastructure and plant maintenance, automatic fault detection has gained attention in both academic and industrial fields [1]
The proposed multi-level automatic fault detection (MLe-automatic fault detection (AFD)) was performed in two stages using three different methods and the results compared to identify the best fitted clusters with the case study the fan coil unit (FCU) dataset
The number of faulty and non-faulty FCUs found from each clustering model are shown in Table 1, which displays the numbers in each category obtained from the models
Summary
With the increasing demands for smart building infrastructure and plant maintenance, automatic fault detection has gained attention in both academic and industrial fields [1]. One of the feasible and well-accepted moves is to extract the information from historic electricity consumption data of different units and identify the causes that widely effect the demand/supply scale. This efficient functioning of such systems is expected to improve the economy and deliver sustainable solutions for energy production in smart buildings [4,5]. Communicating the fault to the owner or maintenance personnel with an agreed simple language describing the fault, if it is severe enough, is highly desirable This system pipeline would eliminate the scheduled maintenance costs, reduce diagnostic labour, reduce wasted energy, reduce peak electricity demand, and minimize downtime.
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