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

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

Abstract

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.

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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

@conference{sensornets13,
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,},
year={2013},
pages={213-221},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004314302130221},
isbn={978-989-8565-45-7},
}


in EndNote Style

TY - CONF
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