Why 'smart' office window shades need to be redesigned
A new article published in the February 27 issue of Bloomberg Businessweek explores the design challenge of getting shade automation right.
Even in the Seattle offices of award-winning architecture firm NBBJ, known for its sustainable architecture, designers complained about the randomness of their window shades rising and falling during the workday--via systems NBBJ itself put into place.
Why so many shade-related dilemmas? In smart buildings, as reporter Karen Weise explains in the Bloomberg Businessweek piece, automated systems tend to adjust heating, cooling, light fixtures, and shades based on data and predictive modeling. However, light can be very difficult to model. Another problem is that there is no federal standard for the amount of light necessary for office buildings, although they exist for other features controlled by smart systems such as temperature and humidity.
And yet another challenge is that sunlight is also reflected off of computer screens. Of course, architects are may not be designing with the precise placement of office equipment in mind. So they might design a building with automatic shades that could very well adjust to the cloudiness or sunniness outside, but not take into consideration how the shades might affect the annoying glare off of computer monitors.
Design solutions already being tested or marketed include:
- a computer program based on a timer for shades to fall when sun is predicted to hit a building
- light sensors that detect sunshine on roofs, individual floors, or on facades, connected to the smart shade system
- virtual building models that predict when sun will hit various office-building floors, which then trigger the smart shades accordingly
- windows that automatically change their tints to darker ones when sun shines on them, instead of automated shades
- glass windows with reflective louvers located between panes, as an alternative to shades
Architects and engineers designing smart buildings are obviously figuring out how to improve automated systems after learning about real-life problems that occur once they're in place. The human reactions to automation can't always be predicted via algorithms alone. But they can be part of a redesign that aims to make smart systems more intelligent.
(Via Bloomberg Businessweek)
Image: Ia Ezwa/Flickr
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This post was originally published on Smartplanet.com