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A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution

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  • Liang Chen

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China)

  • Yuanfan Zheng

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China)

  • Jia Yu

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China)

  • Yuanhang Peng

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China)

  • Ruipeng Li

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China)

  • Shilingyun Han

    (School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China)

Abstract

The energy demand and associated greenhouse gas (GHG) emissions of buildings are significantly affected by the characteristics of the building and local climate conditions. While energy use datasets with high spatial and temporal resolution are highly needed in the context of climate change, energy use monitoring data are not available for most cities. This study introduces an approach combining building energy simulation, climate change modeling, and GIS spatial analysis techniques to develop an energy demand data inventory enabling assessment of the impacts of climate change on building energy consumption in Shanghai, China. Our results suggest that all types of buildings exhibit a net increase in their annual energy demand under the projected future (2050) climate conditions, with the highest increase in energy demand attributed to Heating, Ventilation, and Cooling (HVAC) systems. Variations in building energy demand are found across building types. Due to the large number of residential buildings, they are the main contributor to the increases in energy demand and associated CO 2 emissions. The hourly residential building energy demand on a typical hot summer day (29 July) under the 2050 climate condition at 1 p.m. is found to increase by more than 40%, indicating a risk of energy supply shortage if no actions are taken. The spatial pattern of total annual building energy demand at the individual building level exhibited high spatial heterogeneity with some hotspots. This study provides an alternative method to develop a building energy demand inventory with high temporal resolution at the individual building scale for cities lacking energy use monitoring data, supporting the assessment of building energy and GHG emissions under both current and future climate scenarios at minimal cost.

Suggested Citation

  • Liang Chen & Yuanfan Zheng & Jia Yu & Yuanhang Peng & Ruipeng Li & Shilingyun Han, 2024. "A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution," Energies, MDPI, vol. 17(17), pages 1-24, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4313-:d:1466196
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    References listed on IDEAS

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    1. Helmut Haberl & Markus Löw & Alejandro Perez-Laborda & Sarah Matej & Barbara Plank & Dominik Wiedenhofer & Felix Creutzig & Karl-Heinz Erb & Juan Antonio Duro, 2023. "Built structures influence patterns of energy demand and CO2 emissions across countries," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. De Rosa, Mattia & Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2014. "Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach," Applied Energy, Elsevier, vol. 128(C), pages 217-229.
    3. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
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