A power-efficient self-calibrating smart lighting system
Y. Aussat, Ansis Rosmanis, Srinivasan Keshav
Abstract
Lighting load accounts for a significant portion of overall energy consumption in office buildings. To reduce this load, we have designed and built a smart self-calibrating lighting control system that minimizes power consumption that automatically responds to changes in daylight and occupancy, while simultaneously providing personalized lighting comfort to each occupant. The system measures illuminance and occupancy from sensors located at each work station. Using an unobtrusive self-calibration process, it estimates the relationship between the dimming level of each bulb and the illuminance at each work station. Subsequently, an adaptive control algorithm maintains the desired illuminance at work surfaces despite environmental fluctuations by periodically recalculating the power-efficient and comfort-preserving dimming level for each bulb. Based on a realistic deployment of our system, we find that our system quickly responds to changes in occupancy, daylight and user preferences. We also show, through extensive simulations using 7 months of collected daylight and occupancy data, that our system reduces energy consumption by about 40% compared to conventional LED lighting systems.