IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i15p6673-d1707068.html

Calibration of Building Performance Simulations for Zero Carbon Ready Homes: Two Open Access Case Studies Under Controlled Conditions

Author

Listed:
  • Christopher Tsang

    (Energy House Labs, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

  • Richard Fitton

    (Energy House Labs, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

  • Xinyi Zhang

    (Energy House Labs, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

  • Grant Henshaw

    (Energy House Labs, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

  • Heidi Paola Díaz-Hernández

    (Energy House Labs, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

  • David Farmer

    (Energy House Labs, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

  • David Allinson

    (Building Energy Research Group (BERG), School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK)

  • Anestis Sitmalidis

    (Energy House Labs, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

  • Mohamed Dgali

    (Energy House Labs, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

  • Ljubomir Jankovic

    (Energy House Labs, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

  • William Swan

    (Energy House Labs, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK)

Abstract

This study provides a detailed dataset from two modern homes constructed inside an environmentally controlled chamber. These data are used to carefully calibrate a dynamic thermal simulation model of these homes. The calibrated models show good agreement with measurements taken under controlled conditions. The two case study homes, “The Future Home” and “eHome2”, were constructed within the University of Salford’s Energy House 2.0, and high-quality data were collected over eight days. The calibration process involved updating U-values, air permeability rates, and modelling refinements, such as roof ventilation, ground temperatures, and sub-floor void exchange rates, set as boundary conditions. Results demonstrated a high level of accuracy, with performance gaps in whole-house heat transfer coefficient reduced to 0.5% for “The Future Home” and 0.6% for “eHome2”, falling within aggregate heat loss test uncertainty ranges by a significant amount. The study highlights the improved accuracy of calibrated dynamic thermal simulation models, compared to results from the steady-state Standard Assessment Procedure model. By providing openly accessible calibrated models and a clearly defined methodology, this research presents valuable resources for future building performance modelling studies. The findings support the UK’s transition to dynamic modelling approaches proposed in the recently introduced Home Energy Model approach, contributing to improved prediction of energy efficiency and aligning with goals for zero carbon ready and sustainable housing development.

Suggested Citation

  • Christopher Tsang & Richard Fitton & Xinyi Zhang & Grant Henshaw & Heidi Paola Díaz-Hernández & David Farmer & David Allinson & Anestis Sitmalidis & Mohamed Dgali & Ljubomir Jankovic & William Swan, 2025. "Calibration of Building Performance Simulations for Zero Carbon Ready Homes: Two Open Access Case Studies Under Controlled Conditions," Sustainability, MDPI, vol. 17(15), pages 1-22, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:6673-:d:1707068
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/15/6673/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/15/6673/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gillich, Aaron & Saber, Esmail Mahmoudi & Mohareb, Eugene, 2019. "Limits and uncertainty for energy efficiency in the UK housing stock," Energy Policy, Elsevier, vol. 133(C).
    2. Ljubomir Jankovic & Grant Henshaw & Christopher Tsang & Xinyi Zhang & Richard Fitton & William Swan, 2025. "Heat Transfer Coefficient of a Building: A Constant with Limited Variability or Dynamically Variable?," Energies, MDPI, vol. 18(9), pages 1-26, April.
    3. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    4. Mustafaraj, Giorgio & Marini, Dashamir & Costa, Andrea & Keane, Marcus, 2014. "Model calibration for building energy efficiency simulation," Applied Energy, Elsevier, vol. 130(C), pages 72-85.
    Full references (including those not matched with items on IDEAS)

    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. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    2. Xue, Qingwen & Gu, Mei & Yang, Yingxia & Bai, Pengyun & Wang, Zhichao & Jiang, Sihang & Duan, Pengfei, 2025. "Calibration study of uncertainty parameters for nearly-zero energy buildings based on a novel approximate Bayesian approach," Energy, Elsevier, vol. 322(C).
    3. Abokersh, Mohamed Hany & Spiekman, Marleen & Vijlbrief, Olav & van Goch, T.A.J. & Vallès, Manel & Boer, Dieter, 2021. "A real-time diagnostic tool for evaluating the thermal performance of nearly zero energy buildings," Applied Energy, Elsevier, vol. 281(C).
    4. Zhou, Yuekuan & Zheng, Siqian, 2020. "Uncertainty study on thermal and energy performances of a deterministic parameters based optimal aerogel glazing system using machine-learning method," Energy, Elsevier, vol. 193(C).
    5. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    6. Sun, Kaiyu & Hong, Tianzhen & Taylor-Lange, Sarah C. & Piette, Mary Ann, 2016. "A pattern-based automated approach to building energy model calibration," Applied Energy, Elsevier, vol. 165(C), pages 214-224.
    7. Deb, C. & Gelder, L.V. & Spiekman, M. & Pandraud, Guillaume & Jack, R. & Fitton, R., 2021. "Measuring the heat transfer coefficient (HTC) in buildings: A stakeholder's survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. Lin, Yu-Hao & Tsai, Kang-Ting & Lin, Min-Der & Yang, Ming-Der, 2016. "Design optimization of office building envelope configurations for energy conservation," Applied Energy, Elsevier, vol. 171(C), pages 336-346.
    9. Langtry, Max & Choudhary, Ruchi, 2025. "Quantifying the benefit of load uncertainty reduction for the design of district energy systems under grid constraints using the value of information," Applied Energy, Elsevier, vol. 400(C).
    10. Li, X. & Arbabi, H. & Bennett, G. & Oreszczyn, T. & Densley Tingley, D., 2022. "Net zero by 2050: Investigating carbon-budget compliant retrofit measures for the English housing stock," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    11. 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.
    12. Jeong, Kwangbok & Hong, Taehoon & Kim, Jimin & Cho, Kyuman, 2019. "Development of a multi-objective optimization model for determining the optimal CO2 emissions reduction strategies for a multi-family housing complex," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 118-131.
    13. Li, Sihui & Gong, Guangcai & Peng, Jinqing, 2019. "Dynamic coupling method between air-source heat pumps and buildings in China’s hot-summer/cold-winter zone," Applied Energy, Elsevier, vol. 254(C).
    14. Bai, Yefei & Yu, Cong & Pan, Wei, 2024. "Systematic examination of energy performance gap in low-energy buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    15. Pedro Paulo Fernandes da Silva & Alberto Hernandez Neto & Ildo Luis Sauer, 2021. "Evaluation of Model Calibration Method for Simulation Performance of a Public Hospital in Brazil," Energies, MDPI, vol. 14(13), pages 1-20, June.
    16. Su, Ziyi & Li, Xiaofeng, 2022. "Extraction of key parameters and simplification of sub-system energy models using sensitivity analysis in subway stations," Energy, Elsevier, vol. 261(PA).
    17. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2022. "Risk assessment of energy investment in the industrial framework – Uncertainty and Sensitivity Analysis for energy design and operation optimisation," Energy, Elsevier, vol. 239(PA).
    18. Zhang, Hu & Tian, Wei & Tan, Jingyuan & Yin, Juchao & Fu, Xing, 2024. "Sensitivity analysis of multiple time-scale building energy using Bayesian adaptive spline surfaces," Applied Energy, Elsevier, vol. 363(C).
    19. Hamed Yassaghi & Simi Hoque, 2021. "Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use," Energies, MDPI, vol. 14(2), pages 1-27, January.
    20. Li, Hangxin & Wang, Shengwei, 2020. "Coordinated robust optimal design of building envelope and energy systems for zero/low energy buildings considering uncertainties," Applied Energy, Elsevier, vol. 265(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:17:y:2025:i:15:p:6673-:d:1707068. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.