IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v68y2017ip2p953-963.html
   My bibliography  Save this article

Building Information Modelling for analysis of energy efficient industrial buildings – A case study

Author

Listed:
  • Gourlis, Georgios
  • Kovacic, Iva

Abstract

Industrial buildings demand higher amount of energy than other building typologies, thus powerful modelling and simulation tools for energy-optimisation and identification of synergies-potentials between the building envelope, building services and production systems are needed.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:68:y:2017:i:p2:p:953-963
    DOI: 10.1016/j.rser.2016.02.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032116002173
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2016.02.009?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. Chandel, S.S. & Sharma, Aniket & Marwaha, Bhanu M., 2016. "Review of energy efficiency initiatives and regulations for residential buildings in India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1443-1458.
    2. 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.
    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.
    4. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.
    5. Mirakyan, Atom & De Guio, Roland, 2015. "Modelling and uncertainties in integrated energy planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 62-69.
    6. Ascough, J.C. & Maier, H.R. & Ravalico, J.K. & Strudley, M.W., 2008. "Future research challenges for incorporation of uncertainty in environmental and ecological decision-making," Ecological Modelling, Elsevier, vol. 219(3), pages 383-399.
    7. Ionescu, Constantin & Baracu, Tudor & Vlad, Gabriela-Elena & Necula, Horia & Badea, Adrian, 2015. "The historical evolution of the energy efficient buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 243-253.
    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. 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.
    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. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    4. Sanhudo, Luís & Ramos, Nuno M.M. & Poças Martins, João & Almeida, Ricardo M.S.F. & Barreira, Eva & Simões, M. Lurdes & Cardoso, Vítor, 2018. "Building information modeling for energy retrofitting – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 249-260.
    5. Pessoa, S. & Guimarães, A.S. & Lucas, S.S. & Simões, N., 2021. "3D printing in the construction industry - A systematic review of the thermal performance in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    6. Jia, Shanghui & Chen, Xinhui & Jin, Jiayu, 2024. "Digital disruption and energy efficiency: The impact of regional digitalization on China's industrial sector," Energy, Elsevier, vol. 300(C).
    7. Balali, Amirhossein & Yunusa-Kaltungo, Akilu & Edwards, Rodger, 2023. "A systematic review of passive energy consumption optimisation strategy selection for buildings through multiple criteria decision-making techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    8. Kamel, Ehsan & Memari, Ali M., 2018. "Automated Building Energy Modeling and Assessment Tool (ABEMAT)," Energy, Elsevier, vol. 147(C), pages 15-24.
    9. de Rubeis, Tullio & Nardi, Iole & Ambrosini, Dario & Paoletti, Domenica, 2018. "Is a self-sufficient building energy efficient? Lesson learned from a case study in Mediterranean climate," Applied Energy, Elsevier, vol. 218(C), pages 131-145.

    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. 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.
    2. Gourlis, Georgios & Kovacic, Iva, 2016. "A study on building performance analysis for energy retrofit of existing industrial facilities," Applied Energy, Elsevier, vol. 184(C), pages 1389-1399.
    3. Kangji Li & Lei Pan & Wenping Xue & Hui Jiang & Hanping Mao, 2017. "Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study," Energies, MDPI, vol. 10(2), pages 1-23, February.
    4. Karunathilake, Hirushie & Hewage, Kasun & Sadiq, Rehan, 2018. "Opportunities and challenges in energy demand reduction for Canadian residential sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2005-2016.
    5. Ohlsson, K.E. Anders & Nair, Gireesh & Olofsson, Thomas, 2022. "Uncertainty in model prediction of energy savings in building retrofits: Case of thermal transmittance of windows," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Rashidi, Saman & Esfahani, Javad Abolfazli & Karimi, Nader, 2018. "Porous materials in building energy technologies—A review of the applications, modelling and experiments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 229-247.
    7. Salata, Ferdinando & Ciancio, Virgilio & Dell'Olmo, Jacopo & Golasi, Iacopo & Palusci, Olga & Coppi, Massimo, 2020. "Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms," Applied Energy, Elsevier, vol. 260(C).
    8. Baudry, Gino & Macharis, Cathy & Vallée, Thomas, 2018. "Range-based Multi-Actor Multi-Criteria Analysis: A combined method of Multi-Actor Multi-Criteria Analysis and Monte Carlo simulation to support participatory decision making under uncertainty," European Journal of Operational Research, Elsevier, vol. 264(1), pages 257-269.
    9. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    10. Kim, Jimin & Hong, Taehoon & Jeong, Jaemin & Lee, Myeonghwi & Lee, Minhyun & Jeong, Kwangbok & Koo, Choongwan & Jeong, Jaewook, 2017. "Establishment of an optimal occupant behavior considering the energy consumption and indoor environmental quality by region," Applied Energy, Elsevier, vol. 204(C), pages 1431-1443.
    11. Ceballos-Fuentealba, Irlanda & Álvarez-Miranda, Eduardo & Torres-Fuchslocher, Carlos & del Campo-Hitschfeld, María Luisa & Díaz-Guerrero, John, 2019. "A simulation and optimisation methodology for choosing energy efficiency measures in non-residential buildings," Applied Energy, Elsevier, vol. 256(C).
    12. Santos-Herrero, J.M. & Lopez-Guede, J.M. & Flores-Abascal, I., 2021. "Modeling, simulation and control tools for nZEB: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    13. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
    14. Cezar-Petre Simion & Ciprian Nicolescu & Mihai Cioc, 2018. "Selection of Energy Efficiency Projects for Dwelling Stock to Achieve Optimal Project Portfolio at the Regional Level by Applying LCC. An Analysis Based on Three Scenarios in the South-Muntenia Region," Energies, MDPI, vol. 11(6), pages 1-21, June.
    15. 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).
    16. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    17. Ana Paula Coelho Clauberg & Renato de Mello & Flávio José Simioni & Simone Sehnem, 2021. "System for assessing the sustainability conditions of small hydro plants by fuzzy logic," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(2), pages 300-317, March.
    18. 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.
    19. 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.
    20. Seidl, Rupert & Fernandes, Paulo M. & Fonseca, Teresa F. & Gillet, François & Jönsson, Anna Maria & Merganičová, Katarína & Netherer, Sigrid & Arpaci, Alexander & Bontemps, Jean-Daniel & Bugmann, Hara, 2011. "Modelling natural disturbances in forest ecosystems: a review," Ecological Modelling, Elsevier, vol. 222(4), pages 903-924.

    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:68:y:2017:i:p2:p:953-963. 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.