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A Case Study Based Approach for Remote Fault Detection Using Multi-Level Machine Learning in A Smart Building
- Source :
- Smart Cities, Volume 3, Issue 2, Pages 21-419
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
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&rsquo<br />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<br />thus, many energy related problems are left unmanaged, which causes unnecessary energy wastage and deteriorates equipment&rsquo<br />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&rsquo<br />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<br />this way, the energy expenditure can be further optimized.
- Subjects :
- Building management system
Computer science
business.industry
020209 energy
0211 other engineering and technologies
smart building
02 engineering and technology
Energy consumption
Machine learning
computer.software_genre
Fan coil unit
Fault detection and isolation
fault detection
statistical validation
021105 building & construction
HVAC
0202 electrical engineering, electronic engineering, information engineering
Artificial intelligence
business
Cluster analysis
Building management
computer
fan coil unit
multi-level clustering
Building automation
Subjects
Details
- Language :
- English
- ISSN :
- 26246511
- Database :
- OpenAIRE
- Journal :
- Smart Cities
- Accession number :
- edsair.doi.dedup.....a5000c3c9ade12ebbf402ffeb020f349
- Full Text :
- https://doi.org/10.3390/smartcities3020021