IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v139y2017icp142-154.html
   My bibliography  Save this article

Novel validated method for GIS based automated dynamic urban building energy simulations

Author

Listed:
  • Nageler, P.
  • Zahrer, G.
  • Heimrath, R.
  • Mach, T.
  • Mauthner, F.
  • Leusbrock, I.
  • Schranzhofer, H.
  • Hochenauer, C.

Abstract

The modelling of whole urban districts requires an automated process to parameterize simulation tools. This paper presents a validated methodology for fully automated building modelling within urban districts based on publicly available data. Dynamic building models with detailed heating systems are created in the simulation environment IDA ICE. The method of data collecting and processing and result visualization in a geographical information system (GIS) and the data storage procedure in a PostgreSQL database is described in detail. The building simulation model is validated with consumption data available from 69 buildings of the town Gleisdorf (Austria). The results of the annual heating and domestic hot water demand show a good approximation to the measurement data with a mean deviation of −0.98%. The urban simulation process was then extended to the whole community with its 1945 buildings. This method helps to model and quantitatively describe current building stock in an efficient and timesaving way and enables to develop future smart energy systems, in which the buildings interact with the district heating networks, with limited effort.

Suggested Citation

  • Nageler, P. & Zahrer, G. & Heimrath, R. & Mach, T. & Mauthner, F. & Leusbrock, I. & Schranzhofer, H. & Hochenauer, C., 2017. "Novel validated method for GIS based automated dynamic urban building energy simulations," Energy, Elsevier, vol. 139(C), pages 142-154.
  • Handle: RePEc:eee:energy:v:139:y:2017:i:c:p:142-154
    DOI: 10.1016/j.energy.2017.07.151
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421731335X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2017.07.151?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. Janke, Jason R., 2010. "Multicriteria GIS modeling of wind and solar farms in Colorado," Renewable Energy, Elsevier, vol. 35(10), pages 2228-2234.
    2. Schiel, Kerry & Baume, Olivier & Caruso, Geoffrey & Leopold, Ulrich, 2016. "GIS-based modelling of shallow geothermal energy potential for CO2 emission mitigation in urban areas," Renewable Energy, Elsevier, vol. 86(C), pages 1023-1036.
    3. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    4. Aydin, Nazli Yonca & Kentel, Elcin & Duzgun, Sebnem, 2010. "GIS-based environmental assessment of wind energy systems for spatial planning: A case study from Western Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 364-373, January.
    5. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    6. Fuchs, Marcus & Teichmann, Jens & Lauster, Moritz & Remmen, Peter & Streblow, Rita & Müller, Dirk, 2016. "Workflow automation for combined modeling of buildings and district energy systems," Energy, Elsevier, vol. 117(P2), pages 478-484.
    7. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
    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. Nageler, P. & Schweiger, G. & Schranzhofer, H. & Mach, T. & Heimrath, R. & Hochenauer, C., 2018. "Novel method to simulate large-scale thermal city models," Energy, Elsevier, vol. 157(C), pages 633-646.
    2. Ferrari, Simone & Zagarella, Federica & Caputo, Paola & D'Amico, Antonino, 2019. "Results of a literature review on methods for estimating buildings energy demand at district level," Energy, Elsevier, vol. 175(C), pages 1130-1137.
    3. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Mauree, Dasaraden & Naboni, Emanuele & Coccolo, Silvia & Perera, A.T.D. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 733-746.
    6. Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    7. 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.
    8. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    9. Hernández-Escobedo, Q. & Rodríguez-García, E. & Saldaña-Flores, R. & Fernández-García, A. & Manzano-Agugliaro, F., 2015. "Solar energy resource assessment in Mexican states along the Gulf of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 216-238.
    10. Baseer, M.A. & Rehman, S. & Meyer, J.P. & Alam, Md. Mahbub, 2017. "GIS-based site suitability analysis for wind farm development in Saudi Arabia," Energy, Elsevier, vol. 141(C), pages 1166-1176.
    11. Gorsevski, Pece V. & Cathcart, Steven C. & Mirzaei, Golrokh & Jamali, Mohsin M. & Ye, Xinyue & Gomezdelcampo, Enrique, 2013. "A group-based spatial decision support system for wind farm site selection in Northwest Ohio," Energy Policy, Elsevier, vol. 55(C), pages 374-385.
    12. 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.
    13. Sofia Spyridonidou & Dimitra G. Vagiona, 2020. "Systematic Review of Site-Selection Processes in Onshore and Offshore Wind Energy Research," Energies, MDPI, vol. 13(22), pages 1-26, November.
    14. 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.
    15. Sánchez-Lozano, J.M. & García-Cascales, M.S. & Lamata, M.T., 2014. "Identification and selection of potential sites for onshore wind farms development in Region of Murcia, Spain," Energy, Elsevier, vol. 73(C), pages 311-324.
    16. Marquant, Julien F. & Evins, Ralph & Bollinger, L. Andrew & Carmeliet, Jan, 2017. "A holarchic approach for multi-scale distributed energy system optimisation," Applied Energy, Elsevier, vol. 208(C), pages 935-953.
    17. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    18. Frayssinet, Loïc & Merlier, Lucie & Kuznik, Frédéric & Hubert, Jean-Luc & Milliez, Maya & Roux, Jean-Jacques, 2018. "Modeling the heating and cooling energy demand of urban buildings at city scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2318-2327.
    19. Höfer, Tim & Sunak, Yasin & Siddique, Hafiz & Madlener, Reinhard, 2016. "Wind farm siting using a spatial Analytic Hierarchy Process approach: A case study of the Städteregion Aachen," Applied Energy, Elsevier, vol. 163(C), pages 222-243.
    20. Hasan Eroğlu, 2021. "Multi-criteria decision analysis for wind power plant location selection based on fuzzy AHP and geographic information systems," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 18278-18310, December.

    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:energy:v:139:y:2017:i:c:p:142-154. 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.journals.elsevier.com/energy .

    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.