Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network
Data play an essential role in the optimal control of smart buildings’ operation, especially in building energy-management for the target of nearly zero buildings. The building monitoring system is in charge of collecting and managing building data. However, device imperfections and failures of the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/23/9190 |
| _version_ | 1797463281191878656 |
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| author | Anh Tuan Phan Thi Tuyet Hong Vu Dinh Quang Nguyen Eleonora Riva Sanseverino Hang Thi-Thuy Le Van Cong Bui |
| author_facet | Anh Tuan Phan Thi Tuyet Hong Vu Dinh Quang Nguyen Eleonora Riva Sanseverino Hang Thi-Thuy Le Van Cong Bui |
| author_sort | Anh Tuan Phan |
| collection | DOAJ |
| description | Data play an essential role in the optimal control of smart buildings’ operation, especially in building energy-management for the target of nearly zero buildings. The building monitoring system is in charge of collecting and managing building data. However, device imperfections and failures of the monitoring system are likely to produce low-quality data, such as data loss and inconsistent data, which then seriously affect the control quality of the buildings. This paper proposes a new approach based on Gaussian process regression for data-quality monitoring and sensor network data compensation in smart buildings. The proposed method is proven to effectively detect and compensate for low-quality data thanks to the application of data analysis to the energy management monitoring system of a building model in Viet Nam. The research results provide a good opportunity to improve the efficiency of building energy-management systems and support the development of low-cost smart buildings. |
| first_indexed | 2024-03-09T17:48:29Z |
| format | Article |
| id | doaj.art-752f95f989384b6193c42195d0ef4556 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T17:48:29Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-752f95f989384b6193c42195d0ef45562023-11-24T10:56:48ZengMDPI AGEnergies1996-10732022-12-011523919010.3390/en15239190Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor NetworkAnh Tuan Phan0Thi Tuyet Hong Vu1Dinh Quang Nguyen2Eleonora Riva Sanseverino3Hang Thi-Thuy Le4Van Cong Bui5Energy Department, University of Science and Technology of Hanoi, VAST, Hanoi 11355, VietnamEnergy Department, University of Science and Technology of Hanoi, VAST, Hanoi 11355, VietnamInstitute of Energy and Science, Vietnam Academy Science and Technology, Hanoi 11355, VietnamDepartment of Engineering, University of Palermo, 90128 Palermo, ItalyInstitute of Energy and Science, Vietnam Academy Science and Technology, Hanoi 11355, VietnamElectronics Faculty, Vietnam-Korea Vocational College of Hanoi City, Hanoi 12312, VietnamData play an essential role in the optimal control of smart buildings’ operation, especially in building energy-management for the target of nearly zero buildings. The building monitoring system is in charge of collecting and managing building data. However, device imperfections and failures of the monitoring system are likely to produce low-quality data, such as data loss and inconsistent data, which then seriously affect the control quality of the buildings. This paper proposes a new approach based on Gaussian process regression for data-quality monitoring and sensor network data compensation in smart buildings. The proposed method is proven to effectively detect and compensate for low-quality data thanks to the application of data analysis to the energy management monitoring system of a building model in Viet Nam. The research results provide a good opportunity to improve the efficiency of building energy-management systems and support the development of low-cost smart buildings.https://www.mdpi.com/1996-1073/15/23/9190smart buildingsensor maintenancedata compensationGaussian process regression |
| spellingShingle | Anh Tuan Phan Thi Tuyet Hong Vu Dinh Quang Nguyen Eleonora Riva Sanseverino Hang Thi-Thuy Le Van Cong Bui Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network Energies smart building sensor maintenance data compensation Gaussian process regression |
| title | Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network |
| title_full | Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network |
| title_fullStr | Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network |
| title_full_unstemmed | Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network |
| title_short | Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network |
| title_sort | data compensation with gaussian processes regression application in smart building s sensor network |
| topic | smart building sensor maintenance data compensation Gaussian process regression |
| url | https://www.mdpi.com/1996-1073/15/23/9190 |
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