Identification of district archetypes for energy policy design and urban planning through a two-step clustering method
Résumé
Districts are now a privileged scale for local authorities in their energy transition policies. A variety of techniques and models have been developed in recent years to analyse energy strategies at this scale. In this context, this study introduces an original two-step clustering method to identify district archetypes. These representative districts can be used as case studies for energy policy investigation and urban planning and allow to extrapolate district-level results to a larger scale. The approach relies on the k-means algorithm initialized with a priori knowledge based on the literature and in line with architectural and urban planning considerations. The proposed method is applied on the French region Ile De France. A reduced panel of 19 district archetypes is identified. Various open databases containing information on buildings, households, and urban fabrics are used. This method can be adapted to investigate various district level issues by selecting appropriate parameters.
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