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Modeling of HVAC operational faults in building performance simulation

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  • Zhang, Rongpeng
  • Hong, Tianzhen

Abstract

Operational faults are common in the heating, ventilating, and air conditioning (HVAC) systems of existing buildings, leading to a decrease in energy efficiency and occupant comfort. Various fault detection and diagnostic methods have been developed to identify and analyze HVAC operational faults at the component or subsystem level. However, current methods lack a holistic approach to predicting the overall impacts of faults at the building level—an approach that adequately addresses the coupling between various operational components, the synchronized effect between simultaneous faults, and the dynamic nature of fault severity. This study introduces the novel development of a fault-modeling feature in EnergyPlus which fills in the knowledge gap left by previous studies. This paper presents the design and implementation of the new feature in EnergyPlus and discusses in detail the fault-modeling challenges faced. The new fault-modeling feature enables EnergyPlus to quantify the impacts of faults on building energy use and occupant comfort, thus supporting the decision making of timely fault corrections. Including actual building operational faults in energy models also improves the accuracy of the baseline model, which is critical in the measurement and verification of retrofit or commissioning projects. As an example, EnergyPlus version 8.6 was used to investigate the impacts of a number of typical operational faults in an office building across several U.S. climate zones. The results demonstrate that the faults have significant impacts on building energy performance as well as on occupant thermal comfort. Finally, the paper introduces future development plans for EnergyPlus fault-modeling capability.

Suggested Citation

  • Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
    • Handle: RePEc:eee:appene:v:202:y:2017:i:c:p:178-188
    DOI: 10.1016/j.apenergy.2017.05.153
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    6. Yoon, Y. & Jung, S. & Im, P. & Salonvaara, M. & Bhandari, M. & Kunwar, N., 2023. "Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    7. 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.
    8. Georgios Martinopoulos & Anna Serasidou & Panagiota Antoniadou & Agis M. Papadopoulos, 2018. "Building Integrated Shading and Building Applied Photovoltaic System Assessment in the Energy Performance and Thermal Comfort of Office Buildings," Sustainability, MDPI, vol. 10(12), pages 1-24, December.
    9. Fan, Cheng & Sun, Yongjun & Shan, Kui & Xiao, Fu & Wang, Jiayuan, 2018. "Discovering gradual patterns in building operations for improving building energy efficiency," Applied Energy, Elsevier, vol. 224(C), pages 116-123.
    10. Yimin Chen & Guanjing Lin & Eliot Crowe & Jessica Granderson, 2021. "Development of a Unified Taxonomy for HVAC System Faults," Energies, MDPI, vol. 14(17), pages 1-25, September.
    11. Winkler, Jon & Das, Saptarshi & Earle, Lieko & Burkett, Lena & Robertson, Joseph & Roberts, David & Booten, Charles, 2020. "Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S. energy usage," Applied Energy, Elsevier, vol. 278(C).
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    13. Im, Piljae & Joe, Jaewan & Bae, Yeonjin & New, Joshua R., 2020. "Empirical validation of building energy modeling for multi-zones commercial buildings in cooling season," Applied Energy, Elsevier, vol. 261(C).
    14. Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.
    15. Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).
    16. Younghoon Kwak & Jeonga Kang & Sun-Hye Mun & Young-Sun Jeong & Jung-Ho Huh, 2020. "Development and Application of a Flexible Modeling Approach to Reference Buildings for Energy Analysis," Energies, MDPI, vol. 13(21), pages 1-22, November.
    17. Dayoung Jung & Youngtae Choe & Jihun Shin & Eunche Kim & Gihong Min & Dongjun Kim & Mansu Cho & Chaekwan Lee & Kilyong Choi & Byung Lyul Woo & Wonho Yang, 2022. "Risk Assessment of Indoor Air Quality and Its Association with Subjective Symptoms among Office Workers in Korea," IJERPH, MDPI, vol. 19(4), pages 1-12, February.
    18. Max Emil S. Trothe & Hamid Reza Shaker & Muhyiddine Jradi & Krzysztof Arendt, 2019. "Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings," Energies, MDPI, vol. 12(9), pages 1-12, April.
    19. 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).
    20. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    21. Antonio Rosato & Francesco Guarino & Sergio Sibilio & Evgueniy Entchev & Massimiliano Masullo & Luigi Maffei, 2021. "Healthy and Faulty Experimental Performance of a Typical HVAC System under Italian Climatic Conditions: Artificial Neural Network-Based Model and Fault Impact Assessment," Energies, MDPI, vol. 14(17), pages 1-41, August.
    22. 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.
    23. Baldi, Simone & Zhang, Fan & Le Quang, Thuan & Endel, Petr & Holub, Ondrej, 2019. "Passive versus active learning in operation and adaptive maintenance of Heating, Ventilation, and Air Conditioning," Applied Energy, Elsevier, vol. 252(C), pages 1-1.

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