IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v224y2018icp527-537.html
   My bibliography  Save this article

A framework for producing gbXML building geometry from Point Clouds for accurate and efficient Building Energy Modelling

Author

Listed:
  • Garwood, Tom Lloyd
  • Hughes, Ben Richard
  • O'Connor, Dominic
  • Calautit, John K.
  • Oates, Michael R.
  • Hodgson, Thomas

Abstract

The industrial sector accounts for 17% of end-use energy in the United Kingdom, and 54% globally. Therefore, there is substantial scope to accurately simulate and efficiently assess potential energy retrofit options for industrial buildings to lower end use energy. Due to potentially years of facility renovation and expansion Building Energy Modelling, also called Building Energy Simulation, applied to industrial buildings poses a complex challenge; but it is an important opportunity for reducing global energy demand especially considering the increase of readily available computational power compared with a few years ago. Large and complex industrial buildings make modelling existing geometry for Building Energy Modelling difficult and time consuming which impacts analysis workflow and assessment options available within reasonable budgets. This research presents a potential framework for quickly capturing and processing as-built geometry of a factory, or other large scale buildings, to be utilised in Building Energy Modelling by storing the geometry in a green building eXtensible Mark-up Language (gbXML) format, which is compatible with most commercially available Building Energy Modelling tools. Laser scans were captured from the interior of an industrial facility to produce a Point Cloud. The existing capabilities of a Point Cloud processing software and previous research were assessed to identify the potential development opportunities to automate the conversion of Point Clouds to building geometry for Building Energy Modelling applications. This led to the novel identification of a framework for storing the building geometry in the gbXML format and plans for verification of a future Point Cloud processing solution. This resulted in a sample Point Cloud, of a portion of a building, being converted into a gbXML model that met the validation requirements of the gbXML definition schema. In conclusion, an opportunity exists for increasing the speed of 3D geometry creation of existing industrial buildings for application in BEM and subsequent thermal simulation.

Suggested Citation

  • Garwood, Tom Lloyd & Hughes, Ben Richard & O'Connor, Dominic & Calautit, John K. & Oates, Michael R. & Hodgson, Thomas, 2018. "A framework for producing gbXML building geometry from Point Clouds for accurate and efficient Building Energy Modelling," Applied Energy, Elsevier, vol. 224(C), pages 527-537.
    • Handle: RePEc:eee:appene:v:224:y:2018:i:c:p:527-537
    DOI: 10.1016/j.apenergy.2018.04.046
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918306044
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.04.046?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. Chaudhary, Gaurav & New, Joshua & Sanyal, Jibonananda & Im, Piljae & O’Neill, Zheng & Garg, Vishal, 2016. "Evaluation of “Autotune” calibration against manual calibration of building energy models," Applied Energy, Elsevier, vol. 182(C), pages 115-134.
    2. Enríquez, R. & Jiménez, M.J. & Heras, M.R., 2017. "Towards non-intrusive thermal load Monitoring of buildings: BES calibration," Applied Energy, Elsevier, vol. 191(C), pages 44-54.
    3. Wright, A.J. & Oates, M.R. & Greenough, R., 2013. "Concepts for dynamic modelling of energy-related flows in manufacturing," Applied Energy, Elsevier, vol. 112(C), pages 1342-1348.
    4. Eguaras-Martínez, María & Vidaurre-Arbizu, Marina & Martín-Gómez, César, 2014. "Simulation and evaluation of Building Information Modeling in a real pilot site," Applied Energy, Elsevier, vol. 114(C), pages 475-484.
    5. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    6. Ascione, Fabrizio & Ceroni, Francesca & De Masi, Rosa Francesca & de’ Rossi, Filippo & Pecce, Maria Rosaria, 2017. "Historical buildings: Multidisciplinary approach to structural/energy diagnosis and performance assessment," Applied Energy, Elsevier, vol. 185(P2), pages 1517-1528.
    7. Wang, Qiang & Zhou, Kan, 2017. "A framework for evaluating global national energy security," Applied Energy, Elsevier, vol. 188(C), pages 19-31.
    8. Garwood, Tom Lloyd & Hughes, Ben Richard & Oates, Michael R. & O’Connor, Dominic & Hughes, Ruby, 2018. "A review of energy simulation tools for the manufacturing sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 895-911.
    9. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    10. Chen, Yixing & Hong, Tianzhen, 2018. "Impacts of building geometry modeling methods on the simulation results of urban building energy models," Applied Energy, Elsevier, vol. 215(C), pages 717-735.
    11. 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.
    12. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
    13. Ma, Jun & Cheng, Jack C.P., 2016. "Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology," Applied Energy, Elsevier, vol. 183(C), pages 182-192.
    14. Giorgio Mustafaraj & John Cosgrove & Maria J. Rivas-Duarte & Frances Hardiman & John Harrington, 2015. "A methodology for determining auxiliary and value-added electricity in manufacturing machines," International Journal of Production Research, Taylor & Francis Journals, vol. 53(17), pages 5265-5277, September.
    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. 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.
    2. Filippos Lygerakis & Nikos Kampelis & Dionysia Kolokotsa, 2022. "Knowledge Graphs’ Ontologies and Applications for Energy Efficiency in Buildings: A Review," Energies, MDPI, vol. 15(20), pages 1-32, October.
    3. Shariq, M. Hasan & Hughes, Ben Richard, 2020. "Revolutionising building inspection techniques to meet large-scale energy demands: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    4. Mushk Bughio & Muhammad Shoaib Khan & Waqas Ahmed Mahar & Thorsten Schuetze, 2021. "Impact of Passive Energy Efficiency Measures on Cooling Energy Demand in an Architectural Campus Building in Karachi, Pakistan," Sustainability, MDPI, vol. 13(13), pages 1-35, June.

    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. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
    2. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
    3. Sanober Hassan Khattak & Michael Oates & Rick Greenough, 2018. "Towards Improved Energy and Resource Management in Manufacturing," Energies, MDPI, vol. 11(4), pages 1-15, April.
    4. Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    5. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    6. Fahad Haneef & Giovanni Pernigotto & Andrea Gasparella & Jérôme Henri Kämpf, 2021. "Application of Urban Scale Energy Modelling and Multi-Objective Optimization Techniques for Building Energy Renovation at District Scale," Sustainability, MDPI, vol. 13(20), pages 1-26, October.
    7. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    8. Shi, Long, 2018. "Theoretical models for wall solar chimney under cooling and heating modes considering room configuration," Energy, Elsevier, vol. 165(PB), pages 925-938.
    9. Walsh, Angélica & Cóstola, Daniel & Labaki, Lucila Chebel, 2022. "Performance-based climatic zoning method for building energy efficiency applications using cluster analysis," Energy, Elsevier, vol. 255(C).
    10. Kočí, Jan & Kočí, Václav & Maděra, Jiří & Černý, Robert, 2019. "Effect of applied weather data sets in simulation of building energy demands: Comparison of design years with recent weather data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 22-32.
    11. Rosenfelder, Markus & Wussow, Moritz & Gust, Gunther & Cremades, Roger & Neumann, Dirk, 2021. "Predicting residential electricity consumption using aerial and street view images," Applied Energy, Elsevier, vol. 301(C).
    12. 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.
    13. Wenliang Li, 2020. "Quantifying the Building Energy Dynamics of Manhattan, New York City, Using an Urban Building Energy Model and Localized Weather Data," Energies, MDPI, vol. 13(12), pages 1-22, June.
    14. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2019. "The feasibility and importance of considering climate change impacts in building retrofit analysis," Applied Energy, Elsevier, vol. 233, pages 254-270.
    15. Johari, F. & Peronato, G. & Sadeghian, P. & Zhao, X. & Widén, J., 2020. "Urban building energy modeling: State of the art and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    16. Zinzi, Michele & Carnielo, Emiliano & Mattoni, Benedetta, 2018. "On the relation between urban climate and energy performance of buildings. A three-years experience in Rome, Italy," Applied Energy, Elsevier, vol. 221(C), pages 148-160.
    17. Javanroodi, Kavan & Mahdavinejad, Mohammadjavad & Nik, Vahid M., 2018. "Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate," Applied Energy, Elsevier, vol. 231(C), pages 714-746.
    18. Zhang Deng & Yixing Chen & Xiao Pan & Zhiwen Peng & Jingjing Yang, 2021. "Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling," Energies, MDPI, vol. 14(4), pages 1-17, February.
    19. Hye Gi Kim & Hyun Jun Kim & Chae Hwan Jeon & Myeong Won Chae & Young Hum Cho & Sun Sook Kim, 2020. "Analysis of Energy Saving Effect and Cost Efficiency of ECMs to Upgrade the Building Energy Code," Energies, MDPI, vol. 13(18), pages 1-22, September.
    20. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(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:appene:v:224:y:2018:i:c:p:527-537. 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.