SunCast: Fine-grained prediction of natural sunlight levels for improved daylight harvesting

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.

[1]  Andreas Krause,et al.  Intelligent light control using sensor networks , 2005, SenSys '05.

[2]  Heemin Park,et al.  Illumimote: Multimodal and High-Fidelity Light Sensor Module for Wireless Sensor Networks , 2007, IEEE Sensors Journal.

[3]  Richard C. Willson,et al.  Secular total solar irradiance trend during solar cycles 21–23 , 2003 .

[4]  Mark Stanley Rea,et al.  The IESNA lighting handbook : reference & application , 2000 .

[5]  Adrian E. Raftery,et al.  Weather Forecasting with Ensemble Methods , 2005, Science.

[6]  Luca Benini,et al.  Real-time scheduling for energy harvesting sensor nodes , 2007, Real-Time Systems.

[7]  Mani B. Srivastava,et al.  Power management in energy harvesting sensor networks , 2007, TECS.

[8]  Prasun Sinha,et al.  Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks , 2008, SenSys '08.

[9]  Sarah Stevens,et al.  INTELLIGENT FACADES: OCCUPANT CONTROL AND SATISFACTION , 2001 .

[10]  Clemens Moser,et al.  Power management in energy harvesting embedded systems , 2009 .

[11]  Robert Clear,et al.  Advancement of Electrochromic Windows , 2006 .

[12]  Tm Chung,et al.  Potential energy saving for a side-lit room using daylight-linked fluorescent lamp installations , 2002 .

[13]  Francis Rubinstein,et al.  Comparison of control options in private offices in an advanced lighting controls testbed , 1999 .

[14]  Luca Benini,et al.  Algorithms for harvested energy prediction in batteryless wireless sensor networks , 2009, 2009 3rd International Workshop on Advances in sensors and Interfaces.

[15]  Alice M. Agogino,et al.  Towards embedded wireless-networked intelligent daylighting systems for commercial buildings , 2006, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC'06).

[16]  Prashant J. Shenoy,et al.  Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems , 2010, 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[17]  A. Zeroual,et al.  Prediction of daily global solar radiation using fuzzy systems , 2007 .

[18]  Jessica Ann Granderson.  Human -centered sensor -based Bayesian control: Increased energy efficiency and user satisfaction in commercial lighting , 2007 .

[19]  Wu Ji,et al.  Solar radiation prediction using statistical approaches , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[20]  Sara J. Graves,et al.  CASA and LEAD: adaptive cyberinfrastructure for real-time multiscale weather forecasting , 2006, Computer.

[21]  Dong Kun Noh,et al.  Minimum Variance Energy Allocation for a Solar-Powered Sensor System , 2009, DCOSS.

[22]  Yukio Akashi,et al.  Detectability and Acceptability of Illuminance Reduction for Load Shedding , 2004 .

[23]  Dong Kun Noh,et al.  AdaptSens: An Adaptive Data Collection and Storage Service for Solar-Powered Sensor Networks , 2009, 2009 30th IEEE Real-Time Systems Symposium.

[24]  Andrew G. Barto,et al.  Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks , 2007, 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[25]  David Blaauw,et al.  Millimeter-scale nearly perpetual sensor system with stacked battery and solar cells , 2010, 2010 IEEE International Solid-State Circuits Conference - (ISSCC).

[26]  H. Suehrcke,et al.  A performance prediction method for solar energy systems , 1992 .

[27]  Danny H.W. Li,et al.  Evaluation of lighting performance in office buildings with daylighting controls , 2001 .

[28]  Roger G. Marchand,et al.  Detection and Acceptance of Demand-Responsive Lighting in Offices with and without Daylight , 2008 .

[29]  Heemin Park,et al.  Design and Implementation of a Wireless Sensor Network for Intelligent Light Control , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[30]  Stephen Selkowitz,et al.  Integrating automated shading and smart glazings with daylight controls , 2004 .

[31]  Mariana G. Figueiro,et al.  Daylight and Productivity – A Field Study , 2002 .