IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v326y2022ics0306261922012387.html
   My bibliography  Save this article

Energy load prediction on structures and buildings-Effect of numerical model complexity on simulation of heat fluxes across the structure/environment interface

Author

Listed:
  • Görtz, J.
  • Jürgensen, J.
  • Stolz, D.
  • Wieprecht, S.
  • Terheiden, K.

Abstract

Civil structures, including buildings, constantly exchange heat fluxes with the environment. This includes heat exchange through conduction, convection, radiation and latent heat. A detailed description of the heat fluxes and corresponding transport processes is essential to estimate cooling and heating requirements and inhibit extreme local strains. Moreover, the temperature distribution inside a structure can be predicted by assessing the thermal loads of the environment with respect to the particular material properties of the structure. This is especially substantial for massive structures as thermal stresses can cause cracking. However, in the design of low-energy and passive houses, knowledge about the incoming and outgoing heat fluxes is also of great importance. Considering the numerous meteorological impacts on civil structures, the exact determination of the heat fluxes is quite complex. Most of the studies from literature on heat exchange of civil structures with the environment rely on multiple, not well-founded hypotheses to compensate for the lack of precise data. Therefore, this work aims to improve the understanding and quantification of the heat fluxes between a civil structure and the environment. Various measurement devices have been installed on a gravity dam to capture spatially distributed environmental impacts as well as the temperature distribution inside the structure. This data is used as input to model, quantify and evaluate the governing heat fluxes and thermal transport processes. It can be shown that the temperature fields in civil structures can be modelled even under complex environmental conditions with high accuracy when all essential key processes are incorporated. Furthermore, it is concluded that some simplified models can also yield a good fit even when the modelling parameters are extended beyond their actual definition.

Suggested Citation

  • Görtz, J. & Jürgensen, J. & Stolz, D. & Wieprecht, S. & Terheiden, K., 2022. "Energy load prediction on structures and buildings-Effect of numerical model complexity on simulation of heat fluxes across the structure/environment interface," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012387
    DOI: 10.1016/j.apenergy.2022.119981
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922012387
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119981?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    2. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Yu Hu & Zheng Zuo & Qingbin Li & Yunling Duan, 2013. "Boolean-Based Surface Procedure for the External Heat Transfer Analysis of Dams during Construction," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-17, December.
    4. Zhang, Tiantian & Yang, Hongxing, 2019. "Heat transfer pattern judgment and thermal performance enhancement of insulation air layers in building envelopes," Applied Energy, Elsevier, vol. 250(C), pages 834-845.
    5. Buonomano, Annamaria & Palombo, Adolfo, 2014. "Building energy performance analysis by an in-house developed dynamic simulation code: An investigation for different case studies," Applied Energy, Elsevier, vol. 113(C), pages 788-807.
    6. Zhang, Chaobo & Li, Junyang & Zhao, Yang & Li, Tingting & Chen, Qi & Zhang, Xuejun & Qiu, Weikang, 2021. "Problem of data imbalance in building energy load prediction: Concept, influence, and solution," Applied Energy, Elsevier, vol. 297(C).
    7. Lee, Louis S.H. & Jim, C.Y., 2019. "Energy benefits of green-wall shading based on novel-accurate apportionment of short-wave radiation components," Applied Energy, Elsevier, vol. 238(C), pages 1506-1518.
    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. Pisello, Anna Laura & Asdrubali, Francesco, 2014. "Human-based energy retrofits in residential buildings: A cost-effective alternative to traditional physical strategies," Applied Energy, Elsevier, vol. 133(C), pages 224-235.
    2. Gao, Hao & Koch, Christian & Wu, Yupeng, 2019. "Building information modelling based building energy modelling: A review," Applied Energy, Elsevier, vol. 238(C), pages 320-343.
    3. Simon Wenninger & Christian Wiethe, 2021. "Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(3), pages 223-242, June.
    4. Arkar, C. & Žižak, T. & Domjan, S. & Medved, S., 2020. "Dynamic parametric models for the holistic evaluation of semi-transparent photovoltaic/thermal façade with latent storage inserts," Applied Energy, Elsevier, vol. 280(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. Jakob Carlander & Bahram Moshfegh & Jan Akander & Fredrik Karlsson, 2020. "Effects on Energy Demand in an Office Building Considering Location, Orientation, Façade Design and Internal Heat Gains—A Parametric Study," Energies, MDPI, vol. 13(23), pages 1-22, November.
    7. Kontoleon, K.J., 2015. "Glazing solar heat gain analysis and optimization at varying orientations and placements in aspect of distributed radiation at the interior surfaces," Applied Energy, Elsevier, vol. 144(C), pages 152-164.
    8. 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).
    9. 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.
    10. Abdulazeez Rotimi & Ali Bahadori-Jahromi & Anastasia Mylona & Paulina Godfrey & Darren Cook, 2017. "Estimation and Validation of Energy Consumption in UK Existing Hotel Building Using Dynamic Simulation Software," Sustainability, MDPI, vol. 9(8), pages 1-18, August.
    11. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
    12. Luo, Yongqiang & Zhang, Ling & Liu, Zhongbing & Yu, Jinghua & Xu, Xinhua & Su, Xiaosong, 2020. "Towards net zero energy building: The application potential and adaptability of photovoltaic-thermoelectric-battery wall system," Applied Energy, Elsevier, vol. 258(C).
    13. 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.
    14. Zulay Giménez & Claudio Mourgues & Luis F. Alarcón & Harrison Mesa & Eugenio Pellicer, 2020. "Value Analysis Model to Support the Building Design Process," Sustainability, MDPI, vol. 12(10), pages 1-24, May.
    15. Naylor, Sophie & Gillott, Mark & Lau, Tom, 2018. "A review of occupant-centric building control strategies to reduce building energy use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 1-10.
    16. Shiyi Song & Hong Leng & Ran Guo, 2022. "Multi-Agent-Based Model for the Urban Macro-Level Impact Factors of Building Energy Consumption on Different Types of Land," Land, MDPI, vol. 11(11), pages 1-24, November.
    17. Michał Dziadkiewicz & Renata Włodarczyk & Katarzyna Sukiennik, 2022. "Innovative Ecological Transformations in the Management of Municipal Real Estate," Sustainability, MDPI, vol. 14(21), pages 1-14, November.
    18. Zhao, Dong-Xue & He, Bao-Jie & Johnson, Christine & Mou, Ben, 2015. "Social problems of green buildings: From the humanistic needs to social acceptance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1594-1609.
    19. Ljubomir Jankovic, 2016. "Reducing Simulation Performance Gap in Hemp-Lime Buildings Using Fourier Filtering †," Sustainability, MDPI, vol. 8(9), pages 1-16, August.
    20. Evola, G. & Marletta, L., 2015. "The Solar Response Factor to calculate the cooling load induced by solar gains," Applied Energy, Elsevier, vol. 160(C), pages 431-441.

    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:eee:appene:v:326:y:2022:i:c:s0306261922012387. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.