IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v280y2020ics0306261920314082.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920314082
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115953?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Majcen, Daša & Itard, Laure & Visscher, Henk, 2013. "Actual and theoretical gas consumption in Dutch dwellings: What causes the differences?," Energy Policy, Elsevier, vol. 61(C), pages 460-471.
    2. 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.
    3. Hall, Lisa M.H. & Buckley, Alastair R., 2016. "A review of energy systems models in the UK: Prevalent usage and categorisation," Applied Energy, Elsevier, vol. 169(C), pages 607-628.
    4. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    5. Ballarini, Ilaria & Corgnati, Stefano Paolo & Corrado, Vincenzo, 2014. "Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project," Energy Policy, Elsevier, vol. 68(C), pages 273-284.
    6. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    7. Filogamo, Luana & Peri, Giorgia & Rizzo, Gianfranco & Giaccone, Antonino, 2014. "On the classification of large residential buildings stocks by sample typologies for energy planning purposes," Applied Energy, Elsevier, vol. 135(C), pages 825-835.
    8. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    9. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    10. Delmastro, Chiara & Gargiulo, Maurizio, 2020. "Capturing the long-term interdependencies between building thermal energy supply and demand in urban planning strategies," Applied Energy, Elsevier, vol. 268(C).
    11. Heeren, Niko & Jakob, Martin & Martius, Gregor & Gross, Nadja & Wallbaum, Holger, 2013. "A component based bottom-up building stock model for comprehensive environmental impact assessment and target control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 45-56.
    12. Majcen, D. & Itard, L.C.M. & Visscher, H., 2013. "Theoretical vs. actual energy consumption of labelled dwellings in the Netherlands: Discrepancies and policy implications," Energy Policy, Elsevier, vol. 54(C), pages 125-136.
    13. van den Brom, Paula & Hansen, Anders Rhiger & Gram-Hanssen, Kirsten & Meijer, Arjen & Visscher, Henk, 2019. "Variances in residential heating consumption – Importance of building characteristics and occupants analysed by movers and stayers," Applied Energy, Elsevier, vol. 250(C), pages 713-728.
    14. 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.
    15. Mastrucci, Alessio & Marvuglia, Antonino & Leopold, Ulrich & Benetto, Enrico, 2017. "Life Cycle Assessment of building stocks from urban to transnational scales: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 316-332.
    16. Johansson, Tim & Olofsson, Thomas & Mangold, Mikael, 2017. "Development of an energy atlas for renovation of the multifamily building stock in Sweden," Applied Energy, Elsevier, vol. 203(C), pages 723-736.
    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. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. Alexander Jüstel & Elias Humm & Eileen Herbst & Frank Strozyk & Peter Kukla & Rolf Bracke, 2024. "Unveiling the Spatial Distribution of Heat Demand in North-West-Europe Compiled with National Heat Consumption Data," Energies, MDPI, vol. 17(2), pages 1-36, January.
    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 1-21, 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. 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).
    11. 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.
    12. 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.

    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. Aldubyan, Mohammad & Krarti, Moncef, 2022. "Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study," Energy, Elsevier, vol. 238(PA).
    2. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    3. 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.
    4. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    5. Mastrucci, Alessio & Marvuglia, Antonino & Benetto, Enrico & Leopold, Ulrich, 2020. "A spatio-temporal life cycle assessment framework for building renovation scenarios at the urban scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 126(C).
    6. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    7. Nägeli, Claudio & Jakob, Martin & Catenazzi, Giacomo & Ostermeyer, York, 2020. "Policies to decarbonize the Swiss residential building stock: An agent-based building stock modeling assessment," Energy Policy, Elsevier, vol. 146(C).
    8. Buso, Tiziana & Corgnati, Stefano Paolo, 2017. "A customized modelling approach for multi-functional buildings – Application to an Italian Reference Hotel," Applied Energy, Elsevier, vol. 190(C), pages 1302-1315.
    9. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    10. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    11. Ozarisoy, B. & Altan, H., 2022. "Significance of occupancy patterns and habitual household adaptive behaviour on home-energy performance of post-war social-housing estate in the South-eastern Mediterranean climate: Energy policy desi," Energy, Elsevier, vol. 244(PB).
    12. 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.
    13. 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.
    14. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
    15. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    16. Wenliang Li, 2020. "Quantifying the Building Energy Dynamics of Manhattan, New York City, Using an Urban Building Energy Model and Localized Weather Data," Energies, MDPI, vol. 13(12), pages 1-22, June.
    17. Yang, Xiu'e & Liu, Shuli & Zou, Yuliang & Ji, Wenjie & Zhang, Qunli & Ahmed, Abdullahi & Han, Xiaojing & Shen, Yongliang & Zhang, Shaoliang, 2022. "Energy-saving potential prediction models for large-scale building: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    18. Copiello, Sergio & Grillenzoni, Carlo, 2017. "Is the cold the only reason why we heat our homes? Empirical evidence from spatial series data," Applied Energy, Elsevier, vol. 193(C), pages 491-506.
    19. Mauree, Dasaraden & Naboni, Emanuele & Coccolo, Silvia & Perera, A.T.D. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 733-746.
    20. Bischof, Julian & Duffy, Aidan, 2022. "Life-cycle assessment of non-domestic building stocks: A meta-analysis of current modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).

    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:eee:appene:v:280:y:2020:i:c:s0306261920314082. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.