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A combined GIS-archetype approach to model residential space heating energy: A case study for the Netherlands including validation

Author

Listed:
  • Yang, Xining
  • Hu, Mingming
  • Heeren, Niko
  • Zhang, Chunbo
  • Verhagen, Teun
  • Tukker, Arnold
  • Steubing, Bernhard

Abstract

High spatial resolution is critical for a building stock energy model to identify spatial hotspots and provide targeted recommendations for reducing regional energy consumption. However, input uncertainties due to lacking high-resolution spatial data (e.g. building information and occupant behavior) can cause great discrepancies between modeled and actual energy consumption. We present a modeling framework that can act as a blueprint model for most European countries based on geo- referenced data, building archetypes, and public algorithms. Further sophistication is added in a step-wise approach, including the shift from average to hourly weather data, refurbishment, and occupants’ heating schedules. The model is demonstrated for the city of Leiden, the Netherlands, and the simulated results are spatially validated against the measured natural gas consumption reported at postcode level. Results show that when these factors are considered, the model can provide a good estimate of the energy consumption at the city scale (overestimated by 6%). At postcode level, nearly 83% of the absolute differences between modeled and measured natural gas consumption are within one standard deviation (±25 kWh/m2a, about 30% of the mean measured natural gas consumption). Further research and data would be required to provide reliable results at the level of individual buildings, e.g. information on refurbishment and occupant behavior. The model is well suited to identify spatial hotspots of residential energy consumption and could thus provide a practical basis (e.g. maps) for targeted measures to mitigate climate change.

Suggested Citation

  • Yang, Xining & Hu, Mingming & Heeren, Niko & Zhang, Chunbo & Verhagen, Teun & Tukker, Arnold & Steubing, Bernhard, 2020. "A combined GIS-archetype approach to model residential space heating energy: A case study for the Netherlands including validation," Applied Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314082
    DOI: 10.1016/j.apenergy.2020.115953
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    References listed on IDEAS

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    3. Tatjana Vilutienė & Rasa Džiugaitė-Tumėnienė & Diana Kalibatienė & Darius Kalibatas, 2021. "How BIM Contributes to a Building’s Energy Efficiency throughout Its Whole Life Cycle: Systematic Mapping," Energies, MDPI, vol. 14(20), pages 1-27, October.
    4. Abdulraheem Salaymeh & Irene Peters & Stefan Holler, 2024. "Factoring Building Refurbishment and Climatic Effect into Heat Demand Assessments and Forecasts: Case Study and Open Datasets for Germany," Energies, MDPI, vol. 17(3), pages 1-20, January.
    5. 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).
    6. Friebe, Maximilian & Karasu, Arda & Kriegel, Martin, 2023. "Methodology to compare and optimize district heating and decentralized heat supply for energy transformation on a municipality level," Energy, Elsevier, vol. 282(C).
    7. Li, Shengping & Rismanchi, Behzad & Aye, Lu, 2022. "A simulation-based bottom-up approach for analysing the evolution of residential buildings’ material stocks and environmental impacts – A case study of Inner Melbourne," Applied Energy, Elsevier, vol. 314(C).
    8. Agbonaye, Osaru & Keatley, Patrick & Huang, Ye & Ademulegun, Oluwasola O. & Hewitt, Neil, 2021. "Mapping demand flexibility: A spatio-temporal assessment of flexibility needs, opportunities and response potential," Applied Energy, Elsevier, vol. 295(C).
    9. Yamaguchi, Yohei & Shoda, Yuto & Yoshizawa, Shinya & Imai, Tatsuya & Perwez, Usama & Shimoda, Yoshiyuki & Hayashi, Yasuhiro, 2023. "Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework," Applied Energy, Elsevier, vol. 333(C).
    10. Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    11. Yang, Xining & Hu, Mingming & Tukker, Arnold & Zhang, Chunbo & Huo, Tengfei & Steubing, Bernhard, 2022. "A bottom-up dynamic building stock model for residential energy transition: A case study for the Netherlands," Applied Energy, Elsevier, vol. 306(PA).
    12. Athanasia Apostolopoulou & Mingyu Zhu & Jiayi Jin, 2023. "Parametric Assessment of Building Heating Demand for Different Levels of Details and User Comfort Levels: A Case Study in London, UK," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    13. Cristina Villanueva-Díaz & Milagros Álvarez-Sanz & Álvaro Campos-Celador & Jon Terés-Zubiaga, 2024. "The Open Data Potential for the Geospatial Characterisation of Building Stock on an Urban Scale: Methodology and Implementation in a Case Study," Sustainability, MDPI, vol. 16(2), pages 1-24, January.

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