Virtual In Situ Calibration for Operational Backup Virtual Sensors in Building Energy Systems

Jabeom Koo, Sungmin Yoon and Joowook Kim
Additional contact information
Jabeom Koo: Division of Architecture and Urban Design, Incheon National University, Incheon 22012, Korea
Sungmin Yoon: Division of Architecture and Urban Design, Incheon National University, Incheon 22012, Korea
Joowook Kim: Department of Architectural Engineering, Chosun University, Gwangju 61452, Korea

Energies, 2022, vol. 15, issue 4, 1-12

Abstract: Intelligent building systems require a data-rich environment. Virtual sensors can provide informative and reliable sensing environments for operational datasets in building systems. In particular, backup virtual sensors that are in situ are beneficial for developing the counterparts of target physical sensors in the field, thus providing additional information about residuals between both types of sensors for use in data-driven modeling, analytics, and diagnostics. Therefore, to obtain virtual sensor potentials continuously during operation, we proposed an in situ calibration method for in situ backup virtual sensors (IBVS) in operational building energy systems, based on virtual in situ calibration (VIC). The proposed method was applied using operational datasets measured by a building automation system built into a target system. In a case study, the in situ virtual sensor showed large errors (the root mean squared error (RMSE) was 0.97 °C) on certain days. After conducting the proposed VIC, the RMSE of virtual sensor errors decreased by 22.7% and 18.7% from the perspective of sensor error types such as bias and random error, respectively, in the validation month. The subsequent virtual measurements could be considerably and effectively improved without retraining the specific in situ backup virtual sensor.

Keywords: virtual sensors; soft sensors; virtual in-situ calibration (VIC); sensor calibration; building energy systems; data-driven modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/4/1394/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/4/1394/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:4:p:1394-:d:749331

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().