Learning with Light
Using simulation to drive data driven design
Sponsored by cove.tool | Presented by Sandeep Ahuja
Webinar On-Demand
This course introduces the general concepts used in building performance and a practical case study on an education building. Practitioners from the AEC industry will benefit from learning about the need for using daylight to guide design moves along with energy, glare, carbon, and cost in the early design process. Concepts will be demonstrated in an automated web-app but will be broadly applicable to a wide range of tools.
Image courtesy of cove.tool
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Sandeep Ahuja, co-founder of cove.tool, brings her experience of building a successful sustainability consulting firm Pattern r+d (the consulting arm of cove.tool). Most recently, Sandeep presented at the UN environment assembly, with 1500 global delegates, on the impact of building on climate change, showcased at the Tedx Atlanta, SXSW, won the Forbes 30 under 30 and Atlanta Startup Battle for developing cove.tool, the automated sustainability consultant. Multiple publications have written about Sandeep's impact in the AEC industry including Architect magazine, Tech Crunch, Site Selection, ArchDaily and more. With her desire to bring automation into the AEC world, she is pioneering the integration of building performance into the design and construction process by developing and using intelligent technology and software. Sandeep has presented at multiple architectural and engineering conferences including AIA, ACUI, ASHRAE, Facades+, Tech+, Build Tech, DBL. Working with high profile clients at university facilities, labs, healthcare, corporate and hospitality, Sandeep informs design decision to impact the building performance, occupant wellness and productivity. |
End-to-end automated performance analysis for every team to stay coordinated and on target. Get beautiful analysis graphics for energy, daylight, carbon, and more in-house. Skillfully select the lowest cost and highest performance material components for new construction and renovations. Use machine learning to effortlessly look through thousands of options
Originally published in Industrial Heating
Originally published in April 2021
LEARNING OBJECTIVES
- Analyze building designs for various performance criteria primarily focused on daylight and glare.
- Compare tradeoffs in the context of other data points including quality views, hours of sun, solar, carbon, energy and cost.
- Develop an understanding of their project’s passive climate strategies and the relationship to daylight.
- Identify which mechanical systems, envelope assemblies, and other engineering inputs provide the best cost optimization.
