Sensor-Based Predictive Modeling for Smart Lighting in Grid-Integrated Buildings

Studies show that if we retrofit all the lighting systems in the buildings of California with dimming ballasts, then it would be possible to obtain a 450 MW of regulation, 2.5 GW of nonspinning reserve, and 380 MW of contingency reserve from participation of lighting loads in the energy market. However, in order to guarantee participation, it will be important to monitor and model lighting demand and supply in buildings. To this end, wireless sensor and actuator networks have proven to bear a great potential for personalized intelligent lighting with reduced energy use at 50%-70%. Closed-loop control of these lighting systems relies upon instantaneous and dense sensing. Such systems can be expensive to install and commission. In this paper, we present a sensor-based intelligent lighting system for future grid-integrated buildings. The system is intended to guarantee participation of lighting loads in the energy market, based on predictive models of indoor light distribution, developed using sparse sensing. We deployed similar to 60% fewer sensors compared with state-of-art systems using one photosensor per luminaire. The sensor modules contained small solar panels that were powered by ambient light. Reduction in sensor deployments is achieved using piecewise linear predictive models of indoor light, discretized by clustering for sky conditions and sun positions. Day-ahead daylight is predicted from forecasts of temperature, humidity, and cloud cover. With two weeks of daylight and artificial light training data acquired at the sustainability base at NASA Ames, our model was able to predict the illuminance at seven monitored workstations with 80%-95% accuracy. Moreover, our support vector regression model was able to predict day-ahead daylight at similar to 92% accuracy.