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

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.