Smart Building Creation in Large Scale HVAC Environments through Automated Fault Detection and Diagnosis
Journal article
Dudley, S, Dey, M and Rana, S. (2018). Smart Building Creation in Large Scale HVAC Environments through Automated Fault Detection and Diagnosis. Future Generation Computer Systems. 108, pp. 950-966. https://doi.org/10.1016/j.future.2018.02.019
Authors | Dudley, S, Dey, M and Rana, S. |
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Abstract | Modernisation and retrofitting of older buildings has created a drive to install Building Energy Management Systems (BEMS) that can assist building managers in paving the way for smarter energy use and indirectly, using appropriate methods, occupant comfort understanding. BEMS may discover problems that can inform managers of building maintenance and energy wastage issues and in-directly, via repetitive data patterns appreciate user comfort requirements. The main focus of this paper is to describe a method to detect faulty Heating, Ventilation and Air-Conditioning (HVAC) Terminal Unit (TU) and diagnose them in an automatic and remote manner. For this purpose, a typical big-data framework has been constructed to process the very large volume of data. A novel feature extraction method encouraged by Proportional Integral Derivative (PID) controller has been proposed to describe events from multidimensional TU data streams. These features are further used to categorise different TU behaviours using unsupervised data-driven strategy and supervised learning is applied to diagnose faults. X-means clustering has been performed to group diverse TU behaviours which are experimented on daily, weekly, monthly and randomly selected dataset. Subsequently, Multi-Class Support Vector Machine (MC-SVM) has been employed based on categorical information to generate an automated fault detection and diagnosis system towards making the building smarter. The clustering and classification results further compared with wellknown and established algorithms and validated through statistical measurements. |
Keywords | Heating, Ventilation and Air-Conditioning, Terminal Unit, Big Data, Feature Extraction, Machine Learning, Automatic Fault Detection and Diagnosis; 0805 Distributed Computing; 0806 Information Systems; Distributed Computing |
Year | 2018 |
Journal | Future Generation Computer Systems |
Journal citation | 108, pp. 950-966 |
Publisher | Elsevier |
ISSN | 0167-739X |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.future.2018.02.019 |
Publication dates | |
07 Mar 2018 | |
Publication process dates | |
Deposited | 17 Feb 2018 |
Accepted | 13 Feb 2018 |
Accepted author manuscript | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/86vvq
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Accepted author manuscript
Smart Building Creation in Large Scale HVAC Environments throughAutomated Fault Detection and Diagnosis.pdf | ||
License: CC BY-NC-ND 4.0 | ||
File access level: Open |
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