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Monitoring-based HVAC commissioning of an existing office building for energy efficiency

Author

Listed:
  • Wang, Liping
  • Greenberg, Steve
  • Fiegel, John
  • Rubalcava, Alma
  • Earni, Shankar
  • Pang, Xiufeng
  • Yin, Rongxin
  • Woodworth, Spencer
  • Hernandez-Maldonado, Jorge

Abstract

The performance of Heating, Ventilation and Air Conditioning (HVAC) systems may fail to satisfy design expectations due to improper equipment installation, equipment degradation, sensor failures, or incorrect control sequences. Commissioning identifies and implements cost-effective operational and maintenance measures in buildings to bring them up to the design intent or optimum operation. An existing office building is used as a case study to demonstrate the process of commissioning. Building energy benchmarking tools are applied to evaluate the energy performance for screening opportunities at the whole building level. A large natural gas saving potential was indicated by the building benchmarking results. Faulty operations in the HVAC systems, such as improper operations of air-side economizers, simultaneous heating and cooling, and ineffective optimal start, were identified through trend data analyses and functional testing. The energy saving potential for each commissioning measure is quantified with a calibrated building simulation model. An actual energy saving of 10% was realized after the implementations of cost-effective measures.

Suggested Citation

  • Wang, Liping & Greenberg, Steve & Fiegel, John & Rubalcava, Alma & Earni, Shankar & Pang, Xiufeng & Yin, Rongxin & Woodworth, Spencer & Hernandez-Maldonado, Jorge, 2013. "Monitoring-based HVAC commissioning of an existing office building for energy efficiency," Applied Energy, Elsevier, vol. 102(C), pages 1382-1390.
    • Handle: RePEc:eee:appene:v:102:y:2013:i:c:p:1382-1390
    DOI: 10.1016/j.apenergy.2012.09.005
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    Cited by:

    1. Abdel-Salam, Mohamed R.H. & Ge, Gaoming & Fauchoux, Melanie & Besant, Robert W. & Simonson, Carey J., 2014. "State-of-the-art in liquid-to-air membrane energy exchangers (LAMEEs): A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 700-728.
    2. Abdel-Salam, Mohamed R.H. & Fauchoux, Melanie & Ge, Gaoming & Besant, Robert W. & Simonson, Carey J., 2014. "Expected energy and economic benefits, and environmental impacts for liquid-to-air membrane energy exchangers (LAMEEs) in HVAC systems: A review," Applied Energy, Elsevier, vol. 127(C), pages 202-218.
    3. 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.
    4. Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
    5. Azar, Elie & Nikolopoulou, Christina & Papadopoulos, Sokratis, 2016. "Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling," Applied Energy, Elsevier, vol. 183(C), pages 926-937.
    6. Henze, Gregor P. & Pavlak, Gregory S. & Florita, Anthony R. & Dodier, Robert H. & Hirsch, Adam I., 2015. "An energy signal tool for decision support in building energy systems," Applied Energy, Elsevier, vol. 138(C), pages 51-70.
    7. U. G. D. Madushika & Thanuja Ramachandra & Gayani Karunasena & P. A. D. S. Udakara, 2023. "Energy Retrofitting Technologies of Buildings: A Review-Based Assessment," Energies, MDPI, vol. 16(13), pages 1-16, June.
    8. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    9. Balvís, Eduardo & Sampedro, Óscar & Zaragoza, Sonia & Paredes, Angel & Michinel, Humberto, 2016. "A simple model for automatic analysis and diagnosis of environmental thermal comfort in energy efficient buildings," Applied Energy, Elsevier, vol. 177(C), pages 60-70.
    10. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    11. Ahn, Jonghoon & Cho, Soolyeon, 2017. "Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments," Applied Energy, Elsevier, vol. 204(C), pages 117-130.
    12. Yulong Xie & Mark Halverson & Rosemarie Bartlett & Yan Chen & Michael Rosenberg & Todd Taylor & Jeremiah Williams & Michael Reiner, 2020. "Evaluating Building Energy Code Compliance and Savings Potential through Large-Scale Simulation with Models Inferred by Field Data," Energies, MDPI, vol. 13(9), pages 1-19, May.
    13. Zhou, Zhihua & Feng, Lei & Zhang, Shuzhen & Wang, Chendong & Chen, Guanyi & Du, Tao & Li, Yasong & Zuo, Jian, 2016. "The operational performance of “net zero energy building”: A study in China," Applied Energy, Elsevier, vol. 177(C), pages 716-728.
    14. Li, Wenzhuo & Koo, Choongwan & Hong, Taehoon & Oh, Jeongyoon & Cha, Seung Hyun & Wang, Shengwei, 2020. "A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    15. 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.
    16. 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.
    17. Wang, Huilong & Xu, Peng & Lu, Xing & Yuan, Dengkuo, 2016. "Methodology of comprehensive building energy performance diagnosis for large commercial buildings at multiple levels," Applied Energy, Elsevier, vol. 169(C), pages 14-27.
    18. 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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