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Virtual Sensors for Estimating District Heating Energy Consumption under Sensor Absences in a Residential Building

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  • Sungmin Yoon

    (Division of Architecture and Urban Design, Incheon National University, Incheon 22012, Korea
    Institute of Urban Science, Incheon national University, Incheon 22012, Korea)

  • Youngwoong Choi

    (Division of Architecture and Urban Design, Incheon National University, Incheon 22012, Korea)

  • Jabeom Koo

    (Division of Architecture and Urban Design, Incheon National University, Incheon 22012, Korea)

  • Yejin Hong

    (Division of Architecture and Urban Design, Incheon National University, Incheon 22012, Korea)

  • Ryunhee Kim

    (Division of Architecture and Urban Design, Incheon National University, Incheon 22012, Korea)

  • Joowook Kim

    (Center for Building Environment, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea)

Abstract

District heating (DH) is an energy efficient building heating system that entails low primary energy consumption and reduced environmental impact. The estimation of the required heating load provides information for operators to control district heating systems (DHSs) efficiently. It also yields historical datasets for intelligent management applications. Based on the existing virtual sensor capabilities to estimate physical variables, performance, etc., and to detect the anomaly detection in building energy systems, this paper proposes a virtual sensor-based method for the estimation of DH energy consumption in a residential building. Practical issues, including sensor absences and limited datasets corresponding to actual buildings, were also analyzed to improve the applicability of virtual sensors in a building. According to certain virtual sensor development processes, model-driven, data-driven, and grey-box virtual sensors were developed and compared in a case study. The grey-box virtual sensor surpassed the capabilities of the other virtual sensors, particularly for operation patterns corresponding to low heating, which were different from those in the training dataset; notably, a 16% improvement was observed in the accuracy exhibited by the grey-box virtual sensor, as compared to that of the data-driven virtual sensor. The former sensor accounted for a significantly wider DHS operation range by overcoming training data dependency when estimating the actual DH energy consumption. Finally, the proposed virtual sensors can be applied for continuous commissioning, monitoring, and fault detection in the building, since they are developed based on the DH variables at the demand side.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6013-:d:446863
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    References listed on IDEAS

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    1. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    2. Jian Sun & Jin Dong & Bo Shen & Wenhua Li, 2020. "Virtual Pressure Sensor for Electronic Expansion Valve Control in a Vapor Compression Refrigeration System," Energies, MDPI, vol. 13(18), pages 1-13, September.
    3. Zhang, Qiang & Tian, Zhe & Ma, Zhijun & Li, Genyan & Lu, Yakai & Niu, Jide, 2020. "Development of the heating load prediction model for the residential building of district heating based on model calibration," Energy, Elsevier, vol. 205(C).
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    Cited by:

    1. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    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. 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.
    4. Frafjord, Aksel Johan & Radicke, Jan-Philip & Keprate, Arvind & Komulainen, Tiina M., 2024. "Data-driven approaches for deriving a soft sensor in a district heating network," Energy, Elsevier, vol. 292(C).

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