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

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  • 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
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    References listed on IDEAS

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    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.
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    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.
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    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.
    8. Davidson, Eleni & Schwartz, Yair & Williams, Joe & Mumovic, Dejan, 2024. "Resilience of the higher education sector to future climates: A systematic review of predicted building energy performance and modelling approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).

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