IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i13p3398-d379377.html
   My bibliography  Save this article

Error Analysis of QUB Method in Non-Ideal Conditions during the Experiment

Author

Listed:
  • Naveed Ahmad

    (Univ Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL, UMR5008, F-69621 Villeurbanne, France)

  • Christian Ghiaus

    (Univ Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL, UMR5008, F-69621 Villeurbanne, France)

  • Moomal Qureshi

    (Univ Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL, UMR5008, F-69621 Villeurbanne, France)

Abstract

Overall heat transfer coefficient, also known as the intrinsic performance measurement of the building, determines the amount of heat lost by a building due to temperature difference between indoor and outdoor. QUB (Quick U-value of Buildings) is a short-term method for measuring the overall heat transfer coefficient of buildings. The test involves heating and cooling the house with a power step and measuring the indoor temperature response in a single night. Ideally, the outdoor temperature during QUB experiment should remain constant. To compare the influence of variable outdoor temperature, the QUB experiments are simulated on a well-calibrated model with real weather conditions. The experiments at varying outdoor temperature and constant outdoor temperature during the night show that the results in both conditions are nearly similar. A ±2 °C increase or decrease in the outdoor temperature during the QUB experiment can change the results in the measured overall heat transfer coefficient by ±5%. QUB experiments simulated during the months of winter show that the majority of results are ±15% of the steady-state overall heat transfer coefficient. The QUB results during the months of summer show relatively large variation. The large errors coincide with the small temperature difference between indoor and outdoor temperatures before the start of QUB experiment. The median error of multiple QUB experiments during summer can be reduced by increasing the setpoint temperature before the start of QUB experiment.

Suggested Citation

  • Naveed Ahmad & Christian Ghiaus & Moomal Qureshi, 2020. "Error Analysis of QUB Method in Non-Ideal Conditions during the Experiment," Energies, MDPI, vol. 13(13), pages 1-17, July.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3398-:d:379377
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/13/3398/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/13/3398/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Naveed Ahmad & Christian Ghiaus & Thimothée Thiery, 2020. "Influence of Initial and Boundary Conditions on the Accuracy of the QUB Method to Determine the Overall Heat Loss Coefficient of a Building," Energies, MDPI, vol. 13(1), pages 1-24, January.
    2. Ghiaus, Christian & Ahmad, Naveed, 2020. "Thermal circuits assembling and state-space extraction for modelling heat transfer in buildings," Energy, Elsevier, vol. 195(C).
    3. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    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. José Miguel Márquez-Martinón & Norena Martín-Dorta & Eduardo González-Díaz & Benjamín González-Díaz, 2021. "Influence of Thermal Enclosures on Energy Saving Simulations of Residential Building Typologies in European Climatic Zones," Sustainability, MDPI, vol. 13(15), pages 1-19, August.

    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. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
    2. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    3. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    4. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    5. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    6. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    7. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    8. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    9. Nweye, Kingsley & Nagy, Zoltan, 2022. "MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering," Applied Energy, Elsevier, vol. 316(C).
    10. Papineau, Maya & Yassin, Kareman & Newsham, Guy & Brice, Sarah, 2021. "Conditional demand analysis as a tool to evaluate energy policy options on the path to grid decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    11. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    12. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    13. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    14. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    15. Sun, Xiaoqin & Medina, Mario A. & Lee, Kyoung Ok & Jin, Xing, 2018. "Laboratory assessment of residential building walls containing pipe-encapsulated phase change materials for thermal management," Energy, Elsevier, vol. 163(C), pages 383-391.
    16. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2021. "Feature assessment frameworks to evaluate reduced-order grey-box building energy models," Applied Energy, Elsevier, vol. 298(C).
    17. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    18. De Boeck, L. & Verbeke, S. & Audenaert, A. & De Mesmaeker, L., 2015. "Improving the energy performance of residential buildings: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 960-975.
    19. Hillary, Jason & Walsh, Ed & Shah, Amip & Zhou, Rongliang & Walsh, Pat, 2017. "Guidelines for developing efficient thermal conduction and storage models within building energy simulations," Energy, Elsevier, vol. 125(C), pages 211-222.
    20. Gourlis, Georgios & Kovacic, Iva, 2017. "Building Information Modelling for analysis of energy efficient industrial buildings – A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 953-963.

    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:13:y:2020:i:13:p:3398-:d:379377. 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.