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Impact of building geometry description within district energy simulations

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  • De Jaeger, Ina
  • Reynders, Glenn
  • Ma, Yixiao
  • Saelens, Dirk

Abstract

To assess the feasibility of district energy systems as well as to design them in an optimal way, district energy simulations are often deployed, requiring an accurate spatial and temporal quantification of the district energy demand. Geographical information models and systems can provide input data to quantify the district energy demand, but the available levels of detail (LOD) of these data vary significantly between regions. Therefore, this work investigates the usability of LOD1 and LOD2 representations as well as the impact of building geometry within district energy simulations, by quantifying the differences in geometrical and energy characteristics between five variants of LOD1 or LOD2 representations. The most detailed LOD2 representation is thereby used as a reference. The results show that the significantly decreasing accuracy using LOD1 models may be compensated by assuming the roof shape from regional statistics. Also, aggregation of wall and roof components into a limited number of orientations significantly reduces simulation time, while maintaining the accuracy. It is concluded that geographical information models contain a significant amount of useful data, but the error that results from the deployed level of detail must be kept in mind when assessing the simulation results.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:158:y:2018:i:c:p:1060-1069
    DOI: 10.1016/j.energy.2018.06.098
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    3. van der Heijde, Bram & Vandermeulen, Annelies & Salenbien, Robbe & Helsen, Lieve, 2019. "Representative days selection for district energy system optimisation: a solar district heating system with seasonal storage," Applied Energy, Elsevier, vol. 248(C), pages 79-94.
    4. Annelies Vandermeulen & Ina De Jaeger & Tijs Van Oevelen & Dirk Saelens & Lieve Helsen, 2020. "Analysis of Building Parameter Uncertainty in District Heating for Optimal Control of Network Flexibility," Energies, MDPI, vol. 13(23), pages 1-25, November.
    5. 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).
    6. Avichal Malhotra & Simon Raming & Jérôme Frisch & Christoph van Treeck, 2021. "Open-Source Tool for Transforming CityGML Levels of Detail," Energies, MDPI, vol. 14(24), pages 1-26, December.
    7. Scapino, Luca & De Servi, Carlo & Zondag, Herbert A. & Diriken, Jan & Rindt, Camilo C.M. & Sciacovelli, Adriano, 2020. "Techno-economic optimization of an energy system with sorption thermal energy storage in different energy markets," Applied Energy, Elsevier, vol. 258(C).
    8. Ali Bagheri & Konstantinos N. Genikomsakis & Véronique Feldheim & Christos S. Ioakimidis, 2021. "Sensitivity Analysis of 4R3C Model Parameters with Respect to Structure and Geometric Characteristics of Buildings," Energies, MDPI, vol. 14(3), pages 1-20, January.
    9. Enghok Leang & Pierre Tittelein & Laurent Zalewski & Stéphane Lassue, 2020. "Impact of a Composite Trombe Wall Incorporating Phase Change Materials on the Thermal Behavior of an Individual House with Low Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-32, September.
    10. Lund, Henrik & Duic, Neven & Østergaard, Poul Alberg & Mathiesen, Brian Vad, 2018. "Future district heating systems and technologies: On the role of smart energy systems and 4th generation district heating," Energy, Elsevier, vol. 165(PA), pages 614-619.

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