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Authors: Ryan Paulson 1 ; Chandrayee Basu 1 ; Alice M. Agogino 1 and Scott Poll 2

Affiliations: 1 University of California at Berkeley, United States ; 2 NASA Ames Research Center, United States

ISBN: 978-989-8565-45-7

ISSN: 2184-4380

Keyword(s): Intelligent Lighting Control, Wireless Sensor Network, Inverse Model, Predictive, Daylight Harvesting, Piecewise Linear Regression, Building Energy Efficiency.

Related Ontology Subjects/Areas/Topics: Applications and Uses ; Environment Monitoring ; Sensor Networks ; Smart Buildings and Smart Cities ; Smart Grids and Energy Control Systems

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 t erm 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. (More)

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Paper citation in several formats:
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, ISSN 2184-4380, pages 213-221. DOI: 10.5220/0004314302130221

@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},
}

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

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