IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v189y2024ipas1364032123007335.html
   My bibliography  Save this article

A systematic review of methods for evaluating the thermal performance of buildings through energy simulations

Author

Listed:
  • Mendes, Vítor Freitas
  • Cruz, Alexandre Santana
  • Gomes, Adriano Pinto
  • Mendes, Júlia Castro

Abstract

Energy simulations are extensively employed by researchers and designers to assess strategies aimed at enhancing the thermal performance or energy efficiency of buildings. However, the methodologies for conducting these simulations and analyzing their outcomes vary according to regional standards and among research groups. This work aims to investigate the methods for evaluating the thermal performance of buildings through energy simulations, through a systematic literature review. Initially, the authors surveyed articles published between 2011 and 2022 on Scopus and Web of Science. From an initial pool of 1463 articles that met the research criteria, a refined list of 158 papers were read completely. The authors examined prevalent trends in the methods and identified future research directions. Most publications originated from authors in Northern Hemisphere, with only Brazil showing expressive numbers in the Southern Hemisphere. The results revealed the three most employed methods: Thermal Load (94 %), Degree-Hour (or Degree-Day) (9 %), and Design Days (5 %). EnergyPlus stands out as the widely preferred software (65 % of studies), and 62 % of them incorporate some form of validation or calibration. Articles often lack complete simulations parameters and/or clarity in their presentation. In response to the identified gaps, this study introduces a checklist of best practices for presenting the simulation steps. As such, this work serves as a valuable theoretical framework for future assessments of building thermal performance through computer simulation.

Suggested Citation

  • Mendes, Vítor Freitas & Cruz, Alexandre Santana & Gomes, Adriano Pinto & Mendes, Júlia Castro, 2024. "A systematic review of methods for evaluating the thermal performance of buildings through energy simulations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    • Handle: RePEc:eee:rensus:v:189:y:2024:i:pa:s1364032123007335
    DOI: 10.1016/j.rser.2023.113875
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032123007335
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2023.113875?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. Krstić-Furundžić, Aleksandra & Vujošević, Milica & Petrovski, Aleksandar, 2019. "Energy and environmental performance of the office building facade scenarios," Energy, Elsevier, vol. 183(C), pages 437-447.
    2. Rastogi, Ankush & Choi, Jun-Ki & Hong, Taehoon & Lee, Minhyun, 2017. "Impact of different LEED versions for green building certification and energy efficiency rating system: A Multifamily Midrise case study," Applied Energy, Elsevier, vol. 205(C), pages 732-740.
    3. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    4. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
    5. Roman Hoffmann & Raya Muttarak & Jonas Peisker & Piero Stanig, 2022. "Climate change experiences raise environmental concerns and promote Green voting," Nature Climate Change, Nature, vol. 12(2), pages 148-155, February.
    6. Nafisa Bhikhoo & Arman Hashemi & Heather Cruickshank, 2017. "Improving Thermal Comfort of Low-Income Housing in Thailand through Passive Design Strategies," Sustainability, MDPI, vol. 9(8), pages 1-23, August.
    7. Nurlybekova, Gauhar & Memon, Shazim Ali & Adilkhanova, Indira, 2021. "Quantitative evaluation of the thermal and energy performance of the PCM integrated building in the subtropical climate zone for current and future climate scenario," Energy, Elsevier, vol. 219(C).
    8. Burleyson, Casey D. & Voisin, Nathalie & Taylor, Z. Todd & Xie, Yulong & Kraucunas, Ian, 2018. "Simulated building energy demand biases resulting from the use of representative weather stations," Applied Energy, Elsevier, vol. 209(C), pages 516-528.
    9. De Rosa, Mattia & Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2014. "Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach," Applied Energy, Elsevier, vol. 128(C), pages 217-229.
    10. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    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. Omarov, Bekarys & Memon, Shazim Ali & Kim, Jong, 2023. "A novel approach to develop climate classification based on degree days and building energy performance," Energy, Elsevier, vol. 267(C).
    2. Amir Mortazavigazar & Nourehan Wahba & Paul Newsham & Maharti Triharta & Pufan Zheng & Tracy Chen & Behzad Rismanchi, 2021. "Application of Artificial Neural Networks for Virtual Energy Assessment," Energies, MDPI, vol. 14(24), pages 1-18, December.
    3. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
    4. Guglielmo Zappalà, 2023. "Drought Exposure and Accuracy: Motivated Reasoning in Climate Change Beliefs," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 85(3), pages 649-672, August.
    5. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    6. Omar, M.N. & Samak, A.A. & Keshek, M.H. & Elsisi, S.F., 2020. "Simulation and validation model for using the energy produced from broiler litter waste in their house and its requirement of energy," Renewable Energy, Elsevier, vol. 159(C), pages 920-928.
    7. F. Wang & G. H. Huang & Y. Fan & Y. P. Li, 2020. "Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3199-3217, August.
    8. Liang Chen & Yuanfan Zheng & Jia Yu & Yuanhang Peng & Ruipeng Li & Shilingyun Han, 2024. "A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution," Energies, MDPI, vol. 17(17), pages 1-24, August.
    9. 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.
    10. Jan K. Kazak & Joanna A. Kamińska & Rafał Madej & Marta Bochenkiewicz, 2020. "Where Renewable Energy Sources Funds are Invested? Spatial Analysis of Energy Production Potential and Public Support," Energies, MDPI, vol. 13(21), pages 1-26, October.
    11. Papada, Lefkothea & Kaliampakos, Dimitris, 2016. "Developing the energy profile of mountainous areas," Energy, Elsevier, vol. 107(C), pages 205-214.
    12. Ren, Miao & Zhao, Hua & Gao, Xiaojian, 2022. "Effect of modified diatomite based shape-stabilized phase change materials on multiphysics characteristics of thermal storage mortar," Energy, Elsevier, vol. 241(C).
    13. 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).
    14. Xiaoyu Chen & Xin Yang & Yue Zhang, 2022. "Investigation on C and ESR Estimation of DC-Link Capacitor in Maglev Choppers Using Artificial Neural Network," Energies, MDPI, vol. 15(22), pages 1-17, November.
    15. Mastrucci, Alessio & Marvuglia, Antonino & Leopold, Ulrich & Benetto, Enrico, 2017. "Life Cycle Assessment of building stocks from urban to transnational scales: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 316-332.
    16. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    17. Lahoucine Ouhsaine & Mohammed El Ganaoui & Abdelaziz Mimet & Jean-Michel Nunzi, 2021. "A Substitutive Coefficients Network for the Modelling of Thermal Systems: A Mono-Zone Building Case Study," Energies, MDPI, vol. 14(9), pages 1-19, April.
    18. 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.
    19. Fahlstedt, Oskar & Temeljotov-Salaj, Alenka & Lohne, Jardar & Bohne, Rolf André, 2022. "Holistic assessment of carbon abatement strategies in building refurbishment literature — A scoping review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    20. 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).

    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:rensus:v:189:y:2024:i:pa:s1364032123007335. 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/600126/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.