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Proceedings Paper

Affordable and personalized lighting using inverse modeling and virtual sensors
Author(s): Chandrayee Basu; Benjamin Chen; Jacob Richards; Aparna Dhinakaran; Alice Agogino; Rodney Martin
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Paper Abstract

Wireless sensor networks (WSN) have great potential to enable personalized intelligent lighting systems while reducing building energy use by 50%-70%. As a result WSN systems are being increasingly integrated in state-ofart intelligent lighting systems. In the future these systems will enable participation of lighting loads as ancillary services. However, such systems can be expensive to install and lack the plug-and-play quality necessary for user-friendly commissioning. In this paper we present an integrated system of wireless sensor platforms and modeling software to enable a↵ordable and user-friendly intelligent lighting. It requires ⇠ 60% fewer sensor deployments compared to current commercial systems. Reduction in sensor deployments has been achieved by optimally replacing the actual photo-sensors with real-time discrete predictive inverse models. Spatially sparse and clustered sub-hourly photo-sensor data captured by the WSN platforms are used to develop and validate a piece-wise linear regression of indoor light distribution. This deterministic data-driven model accounts for sky conditions and solar position. The optimal placement of photo-sensors is performed iteratively to achieve the best predictability of the light field desired for indoor lighting control. Using two weeks of daylight and artificial light training data acquired at the Sustainability Base at NASA Ames, the model was able to predict the light level at seven monitored workstations with 80%-95% accuracy. We estimate that 10% adoption of this intelligent wireless sensor system in commercial buildings could save 0.2-0.25 quads BTU of energy nationwide.

Paper Details

Date Published: 8 March 2014
PDF: 14 pages
Proc. SPIE 9061, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014, 90612S (8 March 2014); doi: 10.1117/12.2048681
Show Author Affiliations
Chandrayee Basu, Univ. of California, Berkeley (United States)
Benjamin Chen, Univ. of California, Berkeley (United States)
Jacob Richards, Univ. of California, Berkeley (United States)
Aparna Dhinakaran, Univ. of California, Berkeley (United States)
Alice Agogino, Univ. of California, Berkeley (United States)
Rodney Martin, NASA Ames Research Ctr. (United States)


Published in SPIE Proceedings Vol. 9061:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014
Jerome P. Lynch; Kon-Well Wang; Hoon Sohn, Editor(s)

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