To analyze various urban typologies using machine learning algorithms in defining representative building forms and their thermo-physical and environmental properties in such context
Energy & Environment
Mitacs
Description
This project investigates performance optimization of urban contexts that can lead to higher efficiency utilization. The aim is to analyze various urban typologies using machine learning algorithms in defining representative building forms and their thermo-physical and environmental properties in such context. The nature of analysis will involve parameters relevant to urban density, energy balance and environmental quality. The essence is to produce a comprehensively integrated analysis that attains optimality under the mostsignificant constraints. The work will also involve sensitivity analysis in interaction among variables, constraints and solution by employing various computational methods in order to provide a benchmark for basic conceptual design and analysis. Engineering students can be great contributors for this research with their background in building modeling and design.