Integration of Electrochromic Glazing with Traditional Shading Techniques: Multivariate Analysis for Optimal Configuration
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Abstract
With the advancements in modern energy-efficient building envelopes, glazing
systems have become more prominent as considerable heat transfer elements especially in curtain-wall systems making it extremely difficult for even high-performance
static glazing systems to achieve very low energy consumption levels. This is when electrochromic glazing proves to be effective by adapting to both internal
and external climatic conditions, hence reducing energy consumption and increasing
occupant comfort. But a curtain-wall glazing system entirely of electrochromic glazing might not be the most economical solution for effective daylighting and energy efficiency. The portion of glazing below 2.5ft from floor level (non-vision
glazing) does not provide views or useful daylight at workspace height and that
above 7.5ft from floor level (daylight glazing) is most effective for daylight penetration when coupled with internal light-shelves to distribute daylight deep into the
interior space. This provides an opportunity to tailor the vertical design of the envelope
to better respond to different performance and design issues.
This multivariate study analyzes the energy efficiency, daylight availability and
glare potential of various combinations of electrochromic glazing (SageGlass) and
traditional shading techniques namely external shading devices and internal lightshelves. The ultimate goal is to study the interaction between electrochromic glazing and traditional shading techniques and identify the optimum configuration(s)
that reaps the maximum quantitative and qualitative performance benefits, hence
discovering a more sustainable solution while also making a business/ economic
case (w.r.t. associated energy savings) of replacing static property glazing with dynamic property electrochromic glazing.
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Musunuru, Sravanthi. (2014). Integration of Electrochromic Glazing with Traditional Shading Techniques: Multivariate Analysis for Optimal Configuration. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/163213.
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