Buildings and Urban Intelligence

Rapidly increasing urban sprawl is evolving into a scenario where about 70% of the world’s population would be living in urban areas by the year 2050. Increased urbanization coupled with an explosion in availability of high-resolution data has opened new avenues of understanding and operating buildings, both as an entity as well in swarms.

The course would explore the creation of a digital twin for upcoming and/or existing neighborhoods based on the fragmented data streams both from individual buildings as well as larger urban areas to make predictive assessments that will allow technology and policy recommendations in the following domains;

Energy and carbon flows / Community Decarbonization
The relative orientation, design and massing of buildings in a new neighborhood can have a profound effect on the operational energy and hence carbon emissions from catering to the energy needs of the neighborhood. Case studies would include but not limited to building retrofits, transactive exchange of energy between buildings and community demand response.

Optimizations with respect to material selection (low extraction and process energy, lower transport requirements etc.) for new neighborhoods would be performed to have overall low embodied carbon footprint with same or better performance.

Climate change and socially just adaptation
Urbanization exacerbates the impact of higher temperatures that are resulting from climate change. Case studies will explore the architectural and urban design choices that a cohort of buildings at block/neighborhood level can adopt resulting in improved local microclimate and mitigation of heat related hazard in built spaces as well as open urban areas.

For example, finding the most cost-effective solution from the space of cool roofs, weatherization and distributed energy resources to lower the heat exposure in a low-income neighborhood for the heat wave scenario in the year 2050.

Course Objectives:
• Mapping and understanding the dynamics between buildings and their neighborhood.
• Simulating collective responses that a cohort of buildings can take to achieve a common goal.
• Learning to use platforms that connect buildings to their geographical area and to design solutions that help municipalities with their sustainability and decarbonization targets.

Independent learning, initiative, and self-guided energy assessment work are critical in this module. Students spend considerable time on web tutorials, trying out different model concepts and testing the outcomes.