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

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  • 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.

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  • 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
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    1. De Jaeger, Ina & Reynders, Glenn & Ma, Yixiao & Saelens, Dirk, 2018. "Impact of building geometry description within district energy simulations," Energy, Elsevier, vol. 158(C), pages 1060-1069.
    2. 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).
    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. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Nageler, P. & Heimrath, R. & Mach, T. & Hochenauer, C., 2019. "Prototype of a simulation framework for georeferenced large-scale dynamic simulations of district energy systems," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Johan Simonsson & Khalid Tourkey Atta & Gerald Schweiger & Wolfgang Birk, 2021. "Experiences from City-Scale Simulation of Thermal Grids," Resources, MDPI, vol. 10(2), pages 1-20, January.
    7. 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.
    8. 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.
    9. Moser, A. & Muschick, D. & Gölles, M. & Nageler, P. & Schranzhofer, H. & Mach, T. & Ribas Tugores, C. & Leusbrock, I. & Stark, S. & Lackner, F. & Hofer, A., 2020. "A MILP-based modular energy management system for urban multi-energy systems: Performance and sensitivity analysis," Applied Energy, Elsevier, vol. 261(C).
    10. 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.
    11. Hedegaard, Rasmus Elbæk & Kristensen, Martin Heine & Pedersen, Theis Heidmann & Brun, Adam & Petersen, Steffen, 2019. "Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response," Applied Energy, Elsevier, vol. 242(C), pages 181-204.
    12. Schweiger, Gerald & Heimrath, Richard & Falay, Basak & O'Donovan, Keith & Nageler, Peter & Pertschy, Reinhard & Engel, Georg & Streicher, Wolfgang & Leusbrock, Ingo, 2018. "District energy systems: Modelling paradigms and general-purpose tools," Energy, Elsevier, vol. 164(C), pages 1326-1340.
    13. Heendeniya, Charitha Buddhika & Sumper, Andreas & Eicker, Ursula, 2020. "The multi-energy system co-planning of nearly zero-energy districts – Status-quo and future research potential," Applied Energy, Elsevier, vol. 267(C).
    14. 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).
    15. 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).
    16. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    17. Valeria Todeschi & Roberto Boghetti & Jérôme H. Kämpf & Guglielmina Mutani, 2021. "Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    18. Simone Ferrari & Federica Zagarella & Paola Caputo & Giuliano Dall’O’, 2021. "A GIS-Based Procedure for Estimating the Energy Demand Profiles of Buildings towards Urban Energy Policies," Energies, MDPI, vol. 14(17), pages 1-16, September.
    19. Ang, Yu Qian & Berzolla, Zachary Michael & Reinhart, Christoph F., 2020. "From concept to application: A review of use cases in urban building energy modeling," Applied Energy, Elsevier, vol. 279(C).
    20. Salman Siddiqui & Mark Barrett & John Macadam, 2021. "A High Resolution Spatiotemporal Urban Heat Load Model for GB," Energies, MDPI, vol. 14(14), pages 1-28, July.
    21. Kristensen, Martin Heine & Hedegaard, Rasmus Elbæk & Petersen, Steffen, 2020. "Long-term forecasting of hourly district heating loads in urban areas using hierarchical archetype modeling," Energy, Elsevier, vol. 201(C).
    22. Walsh, Angélica & Cóstola, Daniel & Labaki, Lucila Chebel, 2019. "Validation of the climatic zoning defined by ASHRAE standard 169-2013," Energy Policy, Elsevier, vol. 135(C).
    23. Michael Mans & Tobias Blacha & Thomas Schreiber & Dirk Müller, 2022. "Development and Application of an Open-Source Framework for Automated Thermal Network Generation and Simulations in Modelica," Energies, MDPI, vol. 15(12), pages 1-25, June.
    24. 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).

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