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Empirical validation of building energy modeling for multi-zones commercial buildings in cooling season

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  • Im, Piljae
  • Joe, Jaewan
  • Bae, Yeonjin
  • New, Joshua R.

Abstract

Recent nationwide efforts have provided reliable empirical data for ASHRAE standard 140, “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs,” to enable improved accuracy of building energy modeling (BEM) engines and improved characterization of their accuracy. Use of reliable empirical validation datasets in the evaluation of BEM tools will lead to more consistent and validated simulation engines across all software vendors. This will expedite the use of BEM in designing new buildings and retrofitting existing buildings, which delivers more energy-efficient buildings.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s0306261919320616
    DOI: 10.1016/j.apenergy.2019.114374
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    References listed on IDEAS

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    1. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2018. "Development of a lightweight building simulation tool using simplified zone thermal coupling for fast parametric study," Applied Energy, Elsevier, vol. 223(C), pages 188-214.
    2. Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
    3. Kamal, Rajeev & Moloney, Francesca & Wickramaratne, Chatura & Narasimhan, Arunkumar & Goswami, D.Y., 2019. "Strategic control and cost optimization of thermal energy storage in buildings using EnergyPlus," Applied Energy, Elsevier, vol. 246(C), pages 77-90.
    4. Gholamibozanjani, Gohar & Tarragona, Joan & Gracia, Alvaro de & Fernández, Cèsar & Cabeza, Luisa F. & Farid, Mohammed M., 2018. "Model predictive control strategy applied to different types of building for space heating," Applied Energy, Elsevier, vol. 231(C), pages 959-971.
    5. O' Donovan, Adam & O' Sullivan, Paul D. & Murphy, Michael D., 2019. "Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches," Applied Energy, Elsevier, vol. 250(C), pages 991-1010.
    6. Edwards, Richard E. & New, Joshua & Parker, Lynne E. & Cui, Borui & Dong, Jin, 2017. "Constructing large scale surrogate models from big data and artificial intelligence," Applied Energy, Elsevier, vol. 202(C), pages 685-699.
    7. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2019. "The feasibility and importance of considering climate change impacts in building retrofit analysis," Applied Energy, Elsevier, vol. 233, pages 254-270.
    8. Glasgo, Brock & Hendrickson, Chris & Azevedo, Inês Lima, 2017. "Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit level," Applied Energy, Elsevier, vol. 203(C), pages 348-363.
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    Citations

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    Cited by:

    1. Kyoungcheol Oh & Eui-Jong Kim & Chang-Young Park, 2022. "A Physical Model-Based Data-Driven Approach to Overcome Data Scarcity and Predict Building Energy Consumption," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    2. 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).
    3. Joe, Jaewan & Im, Piljae & Cui, Borui & Dong, Jin, 2023. "Model-based predictive control of multi-zone commercial building with a lumped building modelling approach," Energy, Elsevier, vol. 263(PA).
    4. Jaewan Joe & Piljae Im & Jin Dong, 2020. "Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    5. Jinhyung Park & Kwangwon Choi & Abu Talib & Jaewan Joe, 2024. "Investigation of Energy Consumption of Office Spaces with Active Workstations," Energies, MDPI, vol. 17(3), pages 1-12, January.
    6. Shazia Noor & Hadeed Ashraf & Muhammad Sultan & Zahid Mahmood Khan, 2020. "Evaporative Cooling Options for Building Air-Conditioning: A Comprehensive Study for Climatic Conditions of Multan (Pakistan)," Energies, MDPI, vol. 13(12), pages 1-23, June.
    7. Hye-Jin Kim & Do-Young Choi & Donghyun Seo, 2021. "Development and Verification of Prototypical Office Buildings Models Using the National Building Energy Consumption Survey in Korea," Sustainability, MDPI, vol. 13(7), pages 1-15, March.
    8. Stella Tsoka & Kondylia Velikou & Konstantia Tolika & Aikaterini Tsikaloudaki, 2021. "Evaluating the Combined Effect of Climate Change and Urban Microclimate on Buildings’ Heating and Cooling Energy Demand in a Mediterranean City," Energies, MDPI, vol. 14(18), pages 1-23, September.

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