Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault Detection
Optimizing the performance of heating, ventilation, and air-conditioning (HVAC) systems is critical in today’s energy-conscious world. Fan coil units (FCUs) play a critical role in providing comfort in various environments as an important component of HVAC systems. However, FCUs often experience fai...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/15/6717 |
| _version_ | 1797586056937209856 |
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| author | Iva Matetić Ivan Štajduhar Igor Wolf Sandi Ljubic |
| author_facet | Iva Matetić Ivan Štajduhar Igor Wolf Sandi Ljubic |
| author_sort | Iva Matetić |
| collection | DOAJ |
| description | Optimizing the performance of heating, ventilation, and air-conditioning (HVAC) systems is critical in today’s energy-conscious world. Fan coil units (FCUs) play a critical role in providing comfort in various environments as an important component of HVAC systems. However, FCUs often experience failures that affect their efficiency and increase their energy consumption. In this context, deep learning (DL)-based fault detection offers a promising solution. By detecting faults early and preventing system failures, the efficiency of FCUs can be improved. This paper explores DL models as fault detectors for FCUs to enable smarter and more energy-efficient hotel buildings. We tested three contemporary DL modeling approaches: convolutional neural network (CNN), long short-term memory network (LSTM), and a combination of CNN and gated recurrent unit (GRU). The random forest model (RF) was additionally developed as a baseline benchmark. The fault detectors were tested on a real-world dataset obtained from the sensory measurement system installed in a hotel and additionally supplemented with simulated data via a physical model developed in TRNSYS. Three representative FCU faults, namely, a stuck valve, a reduction in airflow, and an FCU outage, were simulated with a much larger dataset than is typically utilized in similar studies. The results showed that the hybrid model, integrating CNN and GRU, performed best for all three observed faults. DL-based fault detectors outperformed the baseline RF model, confirming these solutions as viable components for energy-efficient hotels. |
| first_indexed | 2024-03-11T00:17:52Z |
| format | Article |
| id | doaj.art-deb387cafc354d1bb43ed1fa204aba66 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:17:52Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-deb387cafc354d1bb43ed1fa204aba662023-11-18T23:33:31ZengMDPI AGSensors1424-82202023-07-012315671710.3390/s23156717Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault DetectionIva Matetić0Ivan Štajduhar1Igor Wolf2Sandi Ljubic3Faculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, CroatiaOptimizing the performance of heating, ventilation, and air-conditioning (HVAC) systems is critical in today’s energy-conscious world. Fan coil units (FCUs) play a critical role in providing comfort in various environments as an important component of HVAC systems. However, FCUs often experience failures that affect their efficiency and increase their energy consumption. In this context, deep learning (DL)-based fault detection offers a promising solution. By detecting faults early and preventing system failures, the efficiency of FCUs can be improved. This paper explores DL models as fault detectors for FCUs to enable smarter and more energy-efficient hotel buildings. We tested three contemporary DL modeling approaches: convolutional neural network (CNN), long short-term memory network (LSTM), and a combination of CNN and gated recurrent unit (GRU). The random forest model (RF) was additionally developed as a baseline benchmark. The fault detectors were tested on a real-world dataset obtained from the sensory measurement system installed in a hotel and additionally supplemented with simulated data via a physical model developed in TRNSYS. Three representative FCU faults, namely, a stuck valve, a reduction in airflow, and an FCU outage, were simulated with a much larger dataset than is typically utilized in similar studies. The results showed that the hybrid model, integrating CNN and GRU, performed best for all three observed faults. DL-based fault detectors outperformed the baseline RF model, confirming these solutions as viable components for energy-efficient hotels.https://www.mdpi.com/1424-8220/23/15/6717fan coil unitsHVAC systemsfault detectiondeep learningCNNLSTM |
| spellingShingle | Iva Matetić Ivan Štajduhar Igor Wolf Sandi Ljubic Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault Detection Sensors fan coil units HVAC systems fault detection deep learning CNN LSTM |
| title | Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault Detection |
| title_full | Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault Detection |
| title_fullStr | Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault Detection |
| title_full_unstemmed | Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault Detection |
| title_short | Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault Detection |
| title_sort | improving the efficiency of fan coil units in hotel buildings through deep learning based fault detection |
| topic | fan coil units HVAC systems fault detection deep learning CNN LSTM |
| url | https://www.mdpi.com/1424-8220/23/15/6717 |
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