IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v190y2020ics0360544219319875.html
   My bibliography  Save this article

Urban models enrichment for energy applications: Challenges in energy simulation using different data sources for building age information

Author

Listed:
  • Zirak, Maryam
  • Weiler, Verena
  • Hein, Martin
  • Eicker, Ursula

Abstract

3D city models are increasingly used for heating demand analyses at urban scale. Many studies have been done for standardization of required attribute data for energy analysis of buildings. The U-values which can be derived from the building age are one of the main influencing attributes for heat demand modelling. The question remains how building age can be provided. Often, the information on the year of construction of each building is not accessible. On the other hand, statistics about building ages are often available on an aggregated level. This paper compares data provided by municipalities to two statistical data sources: Census 2011 data on municipality level and country-wide statistics for Germany. The result shows building age distribution presented by the census leads to an acceptable total heat demand prediction compared with the results based on the data from the municipality. Therefore, the decision-making at urban level can rely on census data if more detailed information is unavailable or inaccessible. Moreover, the role of refurbishment data is discussed in the paper. Finally, it is recommended to standardise census data for different applications. For energy application, distribution of building age over living area is more demanded than over the number of buildings.

Suggested Citation

  • Zirak, Maryam & Weiler, Verena & Hein, Martin & Eicker, Ursula, 2020. "Urban models enrichment for energy applications: Challenges in energy simulation using different data sources for building age information," Energy, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:energy:v:190:y:2020:i:c:s0360544219319875
    DOI: 10.1016/j.energy.2019.116292
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219319875
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116292?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. 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.
    2. 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.
    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. Constantinos A. Balaras & Andreas I. Theodoropoulos & Elena G. Dascalaki, 2023. "Geographic Information Systems for Facilitating Audits of the Urban Built Environment," Energies, MDPI, vol. 16(11), pages 1-26, May.
    2. Malte Schwanebeck & Marcus Krüger & Rainer Duttmann, 2021. "Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales," Energies, MDPI, vol. 14(4), pages 1-18, February.
    3. Benedetta Grassi & Edoardo Alessio Piana & Gian Paolo Beretta & Mariagrazia Pilotelli, 2020. "Dynamic Approach to Evaluate the Effect of Reducing District Heating Temperature on Indoor Thermal Comfort," Energies, MDPI, vol. 14(1), pages 1-25, December.
    4. Bao, Keyu & Thrän, Daniela & Schröter, Bastian, 2023. "Land resource allocation between biomass and ground-mounted PV under consideration of the food–water–energy nexus framework at regional scale," Renewable Energy, Elsevier, vol. 203(C), pages 323-333.
    5. Annette Steingrube & Keyu Bao & Stefan Wieland & Andrés Lalama & Pithon M. Kabiro & Volker Coors & Bastian Schröter, 2021. "A Method for Optimizing and Spatially Distributing Heating Systems by Coupling an Urban Energy Simulation Platform and an Energy System Model," Resources, MDPI, vol. 10(5), pages 1-19, May.
    6. Hettinga, Sanne & van ’t Veer, Rein & Boter, Jaap, 2023. "Large scale energy labelling with models: The EU TABULA model versus machine learning with open data," Energy, Elsevier, vol. 264(C).
    7. Keyu Bao & Rushikesh Padsala & Volker Coors & Daniela Thrän & Bastian Schröter, 2021. "A GIS-Based Simulation Method for Regional Food Potential and Demand," Land, MDPI, vol. 10(8), pages 1-18, August.

    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. Verena Weiler & Jonas Stave & Ursula Eicker, 2019. "Renewable Energy Generation Scenarios Using 3D Urban Modeling Tools—Methodology for Heat Pump and Co-Generation Systems with Case Study Application †," Energies, MDPI, vol. 12(3), pages 1-19, January.
    2. 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.
    3. Zhang, Xingxing & Lovati, Marco & Vigna, Ilaria & Widén, Joakim & Han, Mengjie & Gal, Csilla & Feng, Tao, 2018. "A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions," Applied Energy, Elsevier, vol. 230(C), pages 1034-1056.
    4. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(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. Heidenthaler, Daniel & Deng, Yingwen & Leeb, Markus & Grobbauer, Michael & Kranzl, Lukas & Seiwald, Lena & Mascherbauer, Philipp & Reindl, Patricia & Bednar, Thomas, 2023. "Automated energy performance certificate based urban building energy modelling approach for predicting heat load profiles of districts," Energy, Elsevier, vol. 278(PB).
    7. 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.
    8. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    9. Younghun Choi & Takuro Kobashi & Yoshiki Yamagata & Akito Murayama, 2021. "Assessment of waterfront office redevelopment plan on optimal building energy demand and rooftop photovoltaics for urban decarbonization," Papers 2108.09029, arXiv.org.
    10. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    11. Haleh Moghaddasi & Charles Culp & Jorge Vanegas & Saptarshi Das & Mehrdad Ehsani, 2022. "An Adaptable Net Zero Model: Energy Analysis of a Monitored Case Study," Energies, MDPI, vol. 15(11), pages 1-24, May.
    12. Axel Bruck & Luca Casamassima & Ardak Akhatova & Lukas Kranzl & Kostas Galanakis, 2022. "Creating Comparability among European Neighbourhoods to Enable the Transition of District Energy Infrastructures towards Positive Energy Districts," Energies, MDPI, vol. 15(13), pages 1-21, June.
    13. Guglielmina Mutani & Valeria Todeschi & Simone Beltramino, 2020. "Energy Consumption Models at Urban Scale to Measure Energy Resilience," Sustainability, MDPI, vol. 12(14), pages 1-31, July.
    14. Edmund Widl & Giorgio Agugiaro & Jan Peters-Anders, 2021. "Linking Semantic 3D City Models with Domain-Specific Simulation Tools for the Planning and Validation of Energy Applications at District Level," Sustainability, MDPI, vol. 13(16), pages 1-24, August.
    15. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    16. Aldubyan, Mohammad & Krarti, Moncef, 2022. "Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study," Energy, Elsevier, vol. 238(PA).
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:190:y:2020:i:c:s0360544219319875. 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.