Volume 23: Sustainable Energy Solutions for a Post-COVID Recovery towards a Better Future: Part VI

Correspondence Between Urban Form Typology and Residential Building Energy Use Patterns in Seoul Na Li, DongHyuk Yi, Steven Jige Quan



With the increasing availability of urban factors and building energy datasets, more studies have emerged in the urban building energy field examining the urban form–energy relationship to support energy-oriented urban planning and urban energy system management. However, two limitations exist in current studies: the oversimplified quantification of urban form and the lack of consideration of temporal energy use pattern. Recent studies focused more on urban and building factors that are theoretically relevant to building energy, but how these factors are related to energy use patterns is still far from clear. This study aims to fill this research gap by examining the relationship between urban form typology and residential building energy patterns in Seoul using clustering and the Sankey diagram. The study used the Gaussian mixture model to identify four typical urban form typologies based on energy-relevant urban factors and k-shape clustering to detect three distinct monthly primary energy use patterns of residential buildings. The urban form typologies and energy use patterns are then compared through the Sankey diagram. The comparison shows a complex correspondence. The Mid-rise Open typology achieves a general balance among the three patterns, while the Mid-rise High-density typology, Low-rise Compact typology, and High-rise Low-density typology are all dominated by a U-shaped pattern with a varying balance between the Flat pattern and W-shaped pattern. The findings of this study depict the correspondence between urban form typologies and building energy use patterns, which are highly interpretable and thus informative for energy-oriented urban planning and energy system management toward sustainable urban development.

Keywords urban building energy, urban morphology, urban energy system management, Gaussian mixture model, k-shape clustering

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