IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1394-d749331.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    Institute of Urban Science, Incheon National University, Incheon 22012, Korea)

  • Joowook Kim

    (Department of Architectural Engineering, Chosun University, Gwangju 61452, Korea)

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.

Suggested Citation

  • Jabeom Koo & Sungmin Yoon & Joowook Kim, 2022. "Virtual In Situ Calibration for Operational Backup Virtual Sensors in Building Energy Systems," Energies, MDPI, vol. 15(4), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1394-:d:749331
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1394/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1394/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yoon, Sungmin & Yu, Yuebin, 2018. "Hidden factors and handling strategies on virtual in-situ sensor calibration in building energy systems: Prior information and cancellation effect," Applied Energy, Elsevier, vol. 212(C), pages 1069-1082.
    2. Kim, Ryunhee & Hong, Yejin & Choi, Youngwoong & Yoon, Sungmin, 2021. "System-level fouling detection of district heating substations using virtual-sensor-assisted building automation system," Energy, Elsevier, vol. 227(C).
    3. Sungmin Yoon & Youngwoong Choi & Jabeom Koo & Yejin Hong & Ryunhee Kim & Joowook Kim, 2020. "Virtual Sensors for Estimating District Heating Energy Consumption under Sensor Absences in a Residential Building," Energies, MDPI, vol. 13(22), pages 1-13, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).
    2. Hyo-Jun Kim & Young-Hum Cho, 2021. "Optimal Control Method of Variable Air Volume Terminal Unit System," Energies, MDPI, vol. 14(22), pages 1-15, November.
    3. Hong, Yejin & Yoon, Sungmin & Kim, Yong-Shik & Jang, Hyangin, 2021. "System-level virtual sensing method in building energy systems using autoencoder: Under the limited sensors and operational datasets," Applied Energy, Elsevier, vol. 301(C).
    4. Hong, Yejin & Yoon, Sungmin & Choi, Sebin, 2023. "Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality," Energy, Elsevier, vol. 265(C).
    5. Guelpa, E. & Capone, M. & Sciacovelli, A. & Vasset, N. & Baviere, R. & Verda, V., 2023. "Reduction of supply temperature in existing district heating: A review of strategies and implementations," Energy, Elsevier, vol. 262(PB).
    6. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    7. Lu, Xing & O'Neill, Zheng & Li, Yanfei & Niu, Fuxin, 2020. "A novel simulation-based framework for sensor error impact analysis in smart building systems: A case study for a demand-controlled ventilation system," Applied Energy, Elsevier, vol. 263(C).
    8. Kim, Ryunhee & Hong, Yejin & Choi, Youngwoong & Yoon, Sungmin, 2021. "System-level fouling detection of district heating substations using virtual-sensor-assisted building automation system," Energy, Elsevier, vol. 227(C).
    9. Dino, Giuseppe Edoardo & Catrini, Pietro & Buscemi, Alessandro & Piacentino, Antonio & Palomba, Valeria & Frazzica, Andrea, 2023. "Modeling of a bidirectional substation in a district heating network: Validation, dynamic analysis, and application to a solar prosumer," Energy, Elsevier, vol. 284(C).
    10. Sun, Shaobo & Shan, Kui & Wang, Shengwei, 2022. "An online robust sequencing control strategy for identical chillers using a probabilistic approach concerning flow measurement uncertainties," Applied Energy, Elsevier, vol. 317(C).
    11. Neumayer, Martin & Stecher, Dominik & Grimm, Sebastian & Maier, Andreas & Bücker, Dominikus & Schmidt, Jochen, 2023. "Fault and anomaly detection in district heating substations: A survey on methodology and data sets," Energy, Elsevier, vol. 276(C).
    12. Lim, Hyunwoo & Zhai, Zhiqiang (John), 2018. "Influences of energy data on Bayesian calibration of building energy model," Applied Energy, Elsevier, vol. 231(C), pages 686-698.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1394-:d:749331. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.