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

Liang Chen, Yuanfan Zheng (), Jia Yu, Yuanhang Peng, Ruipeng Li and Shilingyun Han
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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

Energies, 2024, vol. 17, issue 17, 1-24

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.

Keywords: building energy demand; GHG emission; high spatial and temporal resolution; climate change; GIS spatial analysis; individual building scale (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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