A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand. (15th September 2017)

• Record Type: Journal Article
• Authors: Silva, Mafalda C.; Horta, Isabel M.; Leal, Vítor; Oliveira, Vítor
• Is Part Of: Applied energy. Volume 202(2017)
• Page Start: 386
• Page End: 398
• Publication Date: 2017-09-15
• Subjects: Urban form -- Energy demand -- Model -- Artificial neural networks -- GIS; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.05.113
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A methodology for characterizing the link between urban form and energy is proposed. The methodology combines neural networks with a spatial analysis. For the city of Porto, urban form explains about 78% of the variation of energy use. The most relevant features are the number of floors, mix of uses and floor area. The methodology may be useful for assessing the energy impact of new urban projects. 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.