A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand

Abstract

Urban form is an important driver of energy demand and therefore of GHG emissions in urban areas. Yet, research on urban form and energy remains sectorial and hasn't been able to deliver a full understanding of the impact of the physical structure of cities upon their energy demand. Most common approaches feature engineering models in buildings, and statistical models in transports. This study aims at contributing to the characterization of the link between urban form and energy considering altogether three distinct energy uses: ambient heating and cooling in buildings, and travel. A high-resolution methodology is proposed. It applies GIS to provide the analysis with a spatially-explicit character, and neural networks to model energy demand based on a set of relevant urban form indicators. The results confirm that the effect of urban form indicators on the overall energy needs is far from being negligible. In particular, the number of floors, the diversity of activities within a walking reach, the floor area and the subdivision of blocks evidenced a significant impact on the overall energy demand of the case study analyzed.