Inverse Modeling using a Wireless Sensor Network (WSN) for Personalized Daylight Harvesting

Ryan Paulson, Chandrayee Basu, Alice M. Agogino, Scott Poll


Smart lighting systems in low energy commercial buildings can be expensive to implement and commission. Studies have also shown that only 50% of these systems are used after installation, and those used are not operated at full capacity due to inadequate commissioning and lack of personalization. Wireless sensor networks (WSN) have great potential to enable personalized smart lighting systems for real-time model predictive control of integrated smart building systems. In this paper we present a framework for using a WSN to develop a real-time indoor lighting inverse model as a piecewise linear function of window and artificial light levels, discretized by sub-hourly sun angles. Applied on two days of daylight and ten days of artificial light data, this model was able to predict the light level at seven monitored workstations with accuracy sufficient for daylight harvesting and lighting control around fixed work surfaces. The reduced order model was also designed to be used for long term evaluation of energy and comfort performance of the predictive control algorithms. This paper describes a WSN experiment from an implementation at the Sustainability Base at NASA Ames, a living laboratory that offers opportunities to test and validate information-centric smart building control systems.


  1. American Society of Heating, Refrigerating, and AirConditioning Engineers, Inc. (ASHRAE). (2010). ASHRAE Standard Ventilation for Acceptable Air Quality, Standard 62.1-2010. Atlanta: ASHRAE.
  2. Ashe, M., Chwastyk, D., de Monasterio, C., Gupta, M., Pegors, M. (2012). 2010 U.S. Lighting Market Characterization. Retrieved September 20, 2012, from 2010-lmc-final-jan-2012.pdf.
  3. Brambley, M. R., Haves, P., McDonald, S. C., Torcellini, P., Hansen, D., Holmberg, D. R., Roth, K. W. (2005). Advanced Sensors and Controls for Building Applications: Market Assessment and Potential R&D Pathways. Oak Ridge: Pacific Northwest National Laboratory.
  4. California Energy Commission (CEC) and Itron Inc. (2006). California Commercial End-Use Survey. Retrieved November 14, 2012, from 2006-005/CEC-400-2006-005.PDF.
  5. California Public Utilities Commission. 2011. Average Rate by Customer Class Years 2000-2011. Retrieved November 14, 2012, from NGRD/ratesNCharts_elect.htm.
  6. Dawson-Haggerty, S. (2011). Introduction to sMAP. Retrieved September 20, 2012 from ml.
  7. Dawson-Haggerty, S., Krioukov, A., Culler, D. (2012). Experiences integrating building data with sMAP. Retrieved September 20, 2012, from CS-2012-21.pdf.
  8. Department of Energy, (2010). Buildings Energy Data Book. Washington D. C.: Department of Energy. Retrieved August 26, 2012, from ?table=3.1.4.
  9. Deru et al. U.S. Department of Energy Commercial Reference Building Models of the National Building Stock. (2011). National Renewable Energy Laboratories.
  10. Guillemin, A., (2003). Using Genetic Algorithms to Take into Account User Wishes in an Advanced Building Control System. Ph.D.. École Polytechnique Fédérale de Lausanne.
  11. Hayashi, F., (2000). Econometrics. Princeton: Princeton University Press.
  12. Ibarra, D. I., Reinhart, C. F., (2009). Daylight Factor simulation, How close do simulation beginners “really” get?. In: Building Simulation, 11th International IBPSA Conference. Glasgow, Scotland. 27-30 July 2009.
  13. Illuminating Engineering Society of North America (IESNA). (2000). The Lighting Handbook, distributed through the Illuminating Engineering Society of North America, 9th edition.
  14. Lee, E. S., Tavil, A., (2007). Energy and visual comfort performance of electrochromic windows with overhangs. Building and Environment, 42(6), pp.2439- 2449.
  15. Levis, P., Madden, S., Polastre, J., Szewczyk, R., Woo, A., Gay, D., Hill, J., Welsh, M., Brewer, E., Culler, D., (2005). Tinyos: An operating system for sensor networks. Ambient Intelligence, W. Weber, J. M. Rabaey, and E. Aarts, (Ed.). New York: Springer Berlin Heidelberg, 2005, 115-148.
  16. Lindelhöf, D., (2007). Bayesian Optimization of Visual Comfort. Ph.D. École Polytechnique Fédérale de Lausanne.
  17. Li, S. (2006). Wireless Sensor Actuator Network for Light Monitoring and Control Application. In: Proceedings of Consumer Communications and Networking Conference, Las Vegas, NV, USA, January 8-10, 2006; 974-978.
  18. Lin, Y., Megerian, S. (2005). Low Cost Distributed Actuation in Large-scale Ad Hoc Sensor-actuator Networks. In: Proceedings of 2005 International Conference on Wireless Networks, Communications and Mobile Computing, Maui, HI, USA, 2005; 975- 980.
  19. Liu, S., and Henze, G., (2006). Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory, Part 1: Theoretical foundation, Energy and Buildings, 38(2), 142-147.
  20. Lu, J. and Whitehouse, K., (2012). SunCast: Fine-grained Prediction of Natural Sunlight Levels for Improved Daylight Harvesting. In: IPSN, 11th ACM Conference on Information Processing in Sensor Networks. Beijing, China. 16-20 April 2012.
  21. Ma, Y., Kelman, A., Daly, A., Borrelli, F., (2012). Predictive Control of Energy Efficient Buildings with Thermal Storage: Modeling, Simulation and Experiments, IEEE Control Systems Magazine, 44-64.
  22. MEMSIC, Inc. (2012). TelosB_datasheet. Retrieved July 19, 2011, from
  23. Michalsky J. J., (1988). The Astronomical Almanac's algorithm for approximate solar position (1950-2050). Solar Energy, 40(3), 227-235.
  24. Mukherjee, S., Birru, D., Cavalcanti, D., Das, S., Patel, M., Shen, E., and Wen Y.-J., (2010). Closed loop integrated lighting and daylighting control for low energy buildings. Proceedings of the 2010 ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, 2010.
  25. Paulson, R. (2012). Personalized Illuminance Modeling Using Inverse Modeling and Piecewise Linear Regression. M.S. University of California, Berkeley.
  26. Philips, (2011). Rapid-Prototyping Control Implementation using the Building Controls Virtual Test Bed. Philips Technical Report. Briarcliff Manor, NY.
  27. Rude, D. (2006). Why do daylight harvesting projects succeed or fail? Construction Specifier, 59(9), 108.
  28. Sandhu, J. S., Agogino, A. M., Agogino, A. K. (2004). Wireless Sensor Networks for Commercial Lighting Control: Decision Making with Multi-agent Systems. In: Proceedings of Working Notes of the AAAI-04 Sensor Networks Workshop, San Jose, CA, USA, July 26, 2004; 88-92.
  29. Singhvi, V., Krause, A., Guestrin, C., Garrett, J. H. Jr., Matthews, H. S. (2005). Intelligent Light Control using Sensor Networks. In: Proceedings of SenSys'05, San Diego, CA, USA, November 2-4, 2005; 218-229.
  30. Tarantola, A., (2005). Inverse model theory and methods for model parameter estimation. United States of America: Society of Industrial and Applied Mathematics.
  31. Walton, M., Lee, E. S., Clear, R. D., Fernandes, L. L., Kiliccote, S., Piette, M. A., Rubinstein, F. M., Selkowitz, S. E., (2007). Daylighting the New York Times Headquarters Building, Final Report: Commissioning Daylighting Systems and Estimation of Demand Response. Retrieved August 26, 2012, from
  32. Wen, Y.-J. (2008). Wireless Sensor and Actuator Networks for Lighting Energy Efficiency and User Satisfaction. Ph.D. University of California, Berkeley.
  33. Wen, Y.-J., Agogino, A. M., (2011a). Control of WirelessNetworked Lighting in an Open-plan Office. Journal of Lighting Research and Technology, 43(2), 235-248.
  34. Wen, Y.-J., Agogino, A. M., (2011b). Personalized Dynamic Design of Networked Lighting for EnergyEfficiency in Open-Plan Offices. Energy and Buildings, 43(8), 1919-1924.
  35. Wen, Y.-J., Bartolomeo, D. D., and Rubinstein, F, (2011). Co-simulation Based Building Controls Implementation with Networked Sensors and Actuators. In: BuildSys, 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency In Buildings. Seattle, WA, USA. 1 November 2011.

Paper Citation

in Harvard Style

Paulson R., Basu C., M. Agogino A. and Poll S. (2013). Inverse Modeling using a Wireless Sensor Network (WSN) for Personalized Daylight Harvesting . In Proceedings of the 2nd International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-8565-45-7, pages 213-221. DOI: 10.5220/0004314302130221

in Bibtex Style

author={Ryan Paulson and Chandrayee Basu and Alice M. Agogino and Scott Poll},
title={Inverse Modeling using a Wireless Sensor Network (WSN) for Personalized Daylight Harvesting},
booktitle={Proceedings of the 2nd International Conference on Sensor Networks - Volume 1: SENSORNETS,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Inverse Modeling using a Wireless Sensor Network (WSN) for Personalized Daylight Harvesting
SN - 978-989-8565-45-7
AU - Paulson R.
AU - Basu C.
AU - M. Agogino A.
AU - Poll S.
PY - 2013
SP - 213
EP - 221
DO - 10.5220/0004314302130221