IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p8782-d609294.html
   My bibliography  Save this article

Linking Semantic 3D City Models with Domain-Specific Simulation Tools for the Planning and Validation of Energy Applications at District Level

Author

Listed:
  • Edmund Widl

    (Center for Energy, AIT Austrian Institute of Technology, 1210 Vienna, Austria)

  • Giorgio Agugiaro

    (3D Geoinformation Group, Faculty of the Built Environment and Architecture, Delft University of Technology, 2628 BL Delft, The Netherlands)

  • Jan Peters-Anders

    (Center for Energy, AIT Austrian Institute of Technology, 1210 Vienna, Austria)

Abstract

Worldwide, cities are nowadays formulating their own sustainability goals, including ambitious targets related to the generation and consumption of energy. In order to support decision makers in reaching these goals, energy experts typically rely on simulation models of urban energy systems, which provide a cheap and efficient way to analyze potential solutions. The availability of high-quality, well-formatted and semantically structured data is a crucial prerequisite for such simulation-based assessments. Unfortunately, best practices for data modelling are rarely utilized in the context of energy-related simulations, so data management and data access often become tedious and cumbersome tasks. However, with the steady progress of digitalization, more and more spatial and semantic city data also become available and accessible. This paper addresses the challenge to represent these data in a way that ensures simulation tools can make use of them in an efficient and user-friendly way. Requirements for an effective linking of semantic 3D city models with domain-specific simulation tools are presented and discussed. Based on these requirements, a software prototype implementing the required functionality has been developed on top of the CityGML standard. This prototype has been applied to a simple yet realistic use case, which combines data from various sources to analyze the operating conditions of a gas network in a city district. The aim of the presented approach is to foster a stronger collaboration between experts for urban data modelling and energy simulations, based on a concrete proof-of-concept implementation that may serve as an inspiration for future developments.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:8782-:d:609294
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/8782/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/16/8782/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sommer, Tobias & Sulzer, Matthias & Wetter, Michael & Sotnikov, Artem & Mennel, Stefan & Stettler, Christoph, 2020. "The reservoir network: A new network topology for district heating and cooling," Energy, Elsevier, vol. 199(C).
    2. Widl, Edmund & Jacobs, Tobias & Schwabeneder, Daniel & Nicolas, Sebastien & Basciotti, Daniele & Henein, Sawsan & Noh, Tae-Gil & Terreros, Olatz & Schuelke, Anett & Auer, Hans, 2018. "Studying the potential of multi-carrier energy distribution grids: A holistic approach," Energy, Elsevier, vol. 153(C), pages 519-529.
    3. Edmund Widl & Benedikt Leitner & Daniele Basciotti & Sawsan Henein & Tarik Ferhatbegovic & René Hofmann, 2020. "Combined Optimal Design and Control of Hybrid Thermal-Electrical Distribution Grids Using Co-Simulation," Energies, MDPI, vol. 13(8), pages 1-21, April.
    4. Leitner, Benedikt & Widl, Edmund & Gawlik, Wolfgang & Hofmann, René, 2019. "A method for technical assessment of power-to-heat use cases to couple local district heating and electrical distribution grids," Energy, Elsevier, vol. 182(C), pages 729-738.
    5. 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.
    6. Keyu Bao & Rushikesh Padsala & Volker Coors & Daniela Thrän & Bastian Schröter, 2020. "A Method for Assessing Regional Bioenergy Potentials Based on GIS Data and a Dynamic Yield Simulation Model," Energies, MDPI, vol. 13(24), pages 1-24, December.
    7. Arnaudo, Monica & Topel, Monika & Puerto, Pablo & Widl, Edmund & Laumert, Björn, 2019. "Heat demand peak shaving in urban integrated energy systems by demand side management - A techno-economic and environmental approach," Energy, Elsevier, vol. 186(C).
    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. Cezar-Petre Simion & Cătălin-Alexandru Verdeș & Alexandra-Andreea Mironescu & Florin-Gabriel Anghel, 2023. "Digitalization in Energy Production, Distribution, and Consumption: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-30, February.

    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. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    2. Min-Hwi Kim & Deuk-Won Kim & Dong-Won Lee, 2021. "Feasibility of Low Carbon Renewable Energy City Integrated with Hybrid Renewable Energy Systems," Energies, MDPI, vol. 14(21), pages 1-24, November.
    3. Edmund Widl & Benedikt Leitner & Daniele Basciotti & Sawsan Henein & Tarik Ferhatbegovic & René Hofmann, 2020. "Combined Optimal Design and Control of Hybrid Thermal-Electrical Distribution Grids Using Co-Simulation," Energies, MDPI, vol. 13(8), pages 1-21, April.
    4. Arnaudo, Monica & Topel, Monika & Laumert, Björn, 2020. "Techno-economic analysis of demand side flexibility to enable the integration of distributed heat pumps within a Swedish neighborhood," Energy, Elsevier, vol. 195(C).
    5. Daniel Lohmeier & Dennis Cronbach & Simon Ruben Drauz & Martin Braun & Tanja Manuela Kneiske, 2020. "Pandapipes: An Open-Source Piping Grid Calculation Package for Multi-Energy Grid Simulations," Sustainability, MDPI, vol. 12(23), pages 1-39, November.
    6. Lyden, A. & Brown, C.S. & Kolo, I. & Falcone, G. & Friedrich, D., 2022. "Seasonal thermal energy storage in smart energy systems: District-level applications and modelling approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    7. Nielsen, Tore Bach & Lund, Henrik & Østergaard, Poul Alberg & Duic, Neven & Mathiesen, Brian Vad, 2021. "Perspectives on energy efficiency and smart energy systems from the 5th SESAAU2019 conference," Energy, Elsevier, vol. 216(C).
    8. Wendel, Frank & Blesl, Markus & Brodecki, Lukasz & Hufendiek, Kai, 2022. "Expansion or decommission? – Transformation of existing district heating networks by reducing temperature levels in a cost-optimum network design," Applied Energy, Elsevier, vol. 310(C).
    9. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    10. Min-Hwi Kim & Dong-Won Lee & Deuk-Won Kim & Young-Sub An & Jae-Ho Yun, 2021. "Energy Performance Investigation of Bi-Directional Convergence Energy Prosumers for an Energy Sharing Community," Energies, MDPI, vol. 14(17), pages 1-17, September.
    11. 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.
    12. Dong, Lijun & Kang, Xiaojun & Pan, Mengqi & Zhao, Man & Zhang, Feng & Yao, Hong, 2020. "B-matching-based optimization model for energy allocation in sea surface monitoring," Energy, Elsevier, vol. 192(C).
    13. 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.
    14. 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.
    15. 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).
    16. 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.
    17. Manoharan, S. & Gnanambal, K., 2019. "Optimized FOPID controller for improving steady state and transient response of Microturbine Generation system," Energy, Elsevier, vol. 189(C).
    18. 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.
    19. 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.
    20. 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.

    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:gam:jsusta:v:13:y:2021:i:16:p:8782-:d:609294. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.