Optimizing Into the Future: Predictive Models of Sustainable Building Performance- INTERN

Optimizing Into the Future: Predictive Models of Sustainable Building Performance

This webinar is part of the RAIC 2023 Conference on Architecture, now available to stream!

Topics: Climate Justice and Resilience/Sustainability/ Adaptation and Mitigation

Length: 1 hour | What's Included: Video, Quiz, and Certificate of Completion 

We aim to lead a hands-on discussion that asks how we might consider and anticipate the impact of climate change on buildings and the environment to speculate, imagine, and innovate new models of design intervention that can modulate better performance outcomes. Although parametric analysis is an emergent data-driven approach to building performance analysis, few practitioners understand effective methods of evaluating parametric analysis data. Furthermore, even fewer understand predictive models and how to estimate uncertainties relative to climate change. Designers who equip themselves with these techniques can derive insight beyond hysterics and guess concerning innovation.

Learning Objectives:

By the completion of this session, participants will be able to:

• Explain how to create, access, and use different climate model scenarios
• Describe the fundamentals of parametric analysis.
• Visualize data for qualitative analysis.
• Perform statistical sensitivity analysis to quantify the impact of individual input parameters on the overall results.

Subject Matter Expert:

Dr. Mohamed Imam
Ph.D., CPHD, LEED AP
Designer and Lead Researcher, Perkins&Will

Dr. Mohamed Imam Holds a Ph.D. in Resource Generative Architecture from the University of Calgary and an M.Sc. in Sustainable Buildings: Performance and Design from Oxford Brookes University. Currently, he is a designer and lead researcher at Perkins&Will. In addition, Mohamed is a Certified Passive house designer and LEED AP BD+C. His area of research includes machine learning, performance-based computation and design, and multi-objective optimization to support data-driven decision-making.