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Mapping Urban Heat Demand with the Use of GIS-Based Tools

Author

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  • Artur Wyrwa

    (AGH University of Science and Technology, Faculty of Energy and Fuels, Al. Adama Mickiewicza 30, Krakow 30-059, Poland)

  • Yi-kuang Chen

    (AGH University of Science and Technology, Faculty of Energy and Fuels, Al. Adama Mickiewicza 30, Krakow 30-059, Poland)

Abstract

This article presents a bottom-up approach for calculation of the useful heat demand for space heating and hot water preparation using geo-referenced datasets for buildings at the city level. This geographic information system (GIS) based approach was applied in the case study for the city of Krakow, where on the one hand the district heat network is well developed, while on the other hand there are still substantial number of buildings burning solid fuels in individual boilers and stoves, causing air pollution. The calculated heat demand was aggregated in the grid with 100 m × 100 m spatial resolution to deliver the heat map depicting the current situation for 21 buildings types. The results show that the residential buildings, in particular one- and multi-family buildings, have the highest share in overall demand for heat. By combining the results with location of the district heat (DH) network, the potential areas in its close vicinity that have sufficient heat demand density for developing the net were pointed out. Future evolution in heat demand for space heating in one-family houses was evaluated with the use of deterministic method employing building stock model. The study lays a foundation for planning the development of the heating system at the city level.

Suggested Citation

  • Artur Wyrwa & Yi-kuang Chen, 2017. "Mapping Urban Heat Demand with the Use of GIS-Based Tools," Energies, MDPI, vol. 10(5), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:720-:d:99149
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    References listed on IDEAS

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    Cited by:

    1. Malte Schwanebeck & Marcus Krüger & Rainer Duttmann, 2021. "Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales," Energies, MDPI, vol. 14(4), pages 1-18, February.
    2. Borna Doračić & Tomislav Novosel & Tomislav Pukšec & Neven Duić, 2018. "Evaluation of Excess Heat Utilization in District Heating Systems by Implementing Levelized Cost of Excess Heat," Energies, MDPI, vol. 11(3), pages 1-14, March.
    3. Michel Noussan & Benedetto Nastasi, 2018. "Data Analysis of Heating Systems for Buildings—A Tool for Energy Planning, Policies and Systems Simulation," Energies, MDPI, vol. 11(1), pages 1-15, January.
    4. Sahoo, Somadutta & Zuidema, Christian & van Stralen, Joost N.P. & Sijm, Jos & Faaij, André, 2022. "Detailed spatial analysis of renewables’ potential and heat: A study of Groningen Province in the northern Netherlands," Applied Energy, Elsevier, vol. 318(C).
    5. Ilaria Ballarini & Vincenzo Corrado, 2017. "A New Methodology for Assessing the Energy Consumption of Building Stocks," Energies, MDPI, vol. 10(8), pages 1-22, July.
    6. Steffen Nielsen & Lars Grundahl, 2018. "District Heating Expansion Potential with Low-Temperature and End-Use Heat Savings," Energies, MDPI, vol. 11(2), pages 1-17, January.
    7. Prades-Gil, C. & Viana-Fons, J.D. & Masip, X. & Cazorla-Marín, A. & Gómez-Navarro, T., 2023. "An agile heating and cooling energy demand model for residential buildings. Case study in a mediterranean city residential sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    8. Meha, Drilon & Dragusha, Bedri & Thakur, Jagruti & Novosel, Tomislav & Duić, Neven, 2021. "A novel spatial based approach for estimation of space heating demand saving potential and CO2 emissions reduction in urban areas," Energy, Elsevier, vol. 225(C).
    9. Meha, Drilon & Novosel, Tomislav & Duić, Neven, 2020. "Bottom-up and top-down heat demand mapping methods for small municipalities, case Gllogoc," Energy, Elsevier, vol. 199(C).

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