Intelligent light control using sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
Towards Embedded Wireless-Networked Intelligent Daylighting Systems for Commercial Buildings
SUTC '06 Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing -Vol 1 (SUTC'06) - Volume 01
Design and implementation of a wireless sensor network for intelligent light control
Proceedings of the 6th international conference on Information processing in sensor networks
Power management in energy harvesting sensor networks
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
Real-time scheduling for energy harvesting sensor nodes
Real-Time Systems
Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks
Proceedings of the 6th ACM conference on Embedded network sensor systems
Minimum Variance Energy Allocation for a Solar-Powered Sensor System
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
AdaptSens: An Adaptive Data Collection and Storage Service for Solar-Powered Sensor Networks
RTSS '09 Proceedings of the 2009 30th IEEE Real-Time Systems Symposium
Solar radiation prediction using statistical approaches
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Smart blueprints: automatically generated maps of homes and the devices within them
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Solar harvest prediction supported by cloud cover forecasts
Proceedings of the 1st International Workshop on Energy Neutral Sensing Systems
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Daylight harvesting is the use of natural sunlight to reduce the need for artificial lighting in buildings. The key challenge of daylight harvesting is to provide stable indoor lighting levels even though natural sunlight is not a stable light source. In this paper, we present a new technique called SunCast that improves lighting stability by predicting changes in future sunlight levels. The system has two parts: 1) it learns predictable sunlight patterns due to trees, nearby buildings, or other environmental factors, and 2) it controls the window transparency based on a quadratic optimization over predicted sunlight levels. To evaluate the system, we record daylight levels at 39 different windows for up to 12 weeks at a time, and apply our control algorithm on the data traces. Our results indicate that SunCast can reduce glare by 59% over a baseline approach with only a marginal increase in artificial lighting energy.