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Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2022

Identification of a reduced panel of district archetypes for energy policies design and urban planning

Résumé

Districts are now a privileged scale for local authorities in their energy transition policy. Hence, in the last years, diverse methods and models were developed to study energy strategies at this scale. To help the realization of energy studies, urban planners or energy managers often use building or district archetypes. In this paper, in order to identify district archetypes, an original two-step clustering approach is introduced. The approach relies on the k-means algorithm initialized with a priori knowledge from the literature and in line with architectural and urban planning considerations. Different open databases describing buildings, households and urban fabrics are used. A reduced panel of 19 district archetypes, representative of the large territory of Paris area, is identified. These representative districts can be used as case studies for energy policy investigation and allow to extrapolate district-level results to a larger scale. The proposed method is applied on the French region Ile De France and is readily replicable to any other region and where similar data are available. Highlights :-A representative panel of archetypal districts is identified for the purpose to support to the energy policies studies-A two steps district clustering method, considering both urban fabric and residential heating system parameters, is proposed-The method is applied on the French region Ile-De-France, and identify up to 19 clusters and representative archetypes of districts-The method is readily replicable to any other French region and other countries where similar data are available
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Dates et versions

hal-04225528 , version 1 (02-10-2023)
hal-04225528 , version 2 (15-11-2023)

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Paternité

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  • HAL Id : hal-04225528 , version 1

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Victor Marty-Jourjon, Thomas Berthou, Bruno Duplessis. Identification of a reduced panel of district archetypes for energy policies design and urban planning. 2022. ⟨hal-04225528v1⟩
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