Typical input of lighting control systems are driven by the following one or more aspects: time (chronological or astronomical time), occupancy (occupancy sensors), daylight availability (photocells) and program logic etc. (Wikipedia, 2014). This summary will present a brief review of the energy performance of the daylight-availability and occupancy based systems.
Niels van de Meugheuvel et al. studied the effect of enabling communications between neighboring controllers with simulation (Meugheuvel, Pandharipande, Caicedo, & Hof, 2014). They modeled a 24m × 19m × 2.6m open plan office in DIALux with 80 evenly distributed luminaires (Philips BBS 560). They divide the space into 36 zones based on the workstation settings. They compared the lighting and energy performance of the standalone setting, where each controller worked purely on its own sensor input without collaboration with its neighbors, and the networked setting, where there are information sharing between neighboring luminaires. In the standalone setting, some of the zones cannot reach the required illuminance because of saturation. By enabling the networked control, the required 500lx illuminance level of all zones are achieved. The control is based on both occupancy and daylight availability using PI controllers. In the networked control setting with communications, each luminaire only contributes 30% to 50% of the required illuminance while the remaining are supplied by the neighboring luminaires. The comfort threshold of the sudden increase of illuminance is 141lx , thus the overshoot of the controller should not exceed 28% (141lx / 500lx). Hence the study put the acceptable overshoot to be no more than 20%. With the simulated step responses of both systems, for the stand alone case, 99% of the simulations showed an overshoot of below 13%; for the networked system, 99% of the simulations showed an overshoot of below 11%. The energy performance of the two systems are evaluated based on the average duty cycle in steady state. They discovered the networked system uses approximately 10% less energy than the standalone system, but still, it is less energy efficient than the centralized lighting control systems.
Li and Pandharipande (li & Pandharipande, 2015) designed an augmented networked distributed lighting control system that achieves “neutral energy”. Each luminaire is paired with a “distributed light-harvesting wireless sensing modules (WSM)”. Each WSM consists of “a light sensor, an occupancy sensor, a wireless radio transceiver, a micro-controller-unit (MCU)”, a PV cell and a pair of EnerChip for energy storing. The PV cell can capture the energy from daylight or artificial lighting and sends out “continuous constant current” that can either charge the EnerChips or be directly used in illuminating the space. Upon occupancy of the current zone, the controller is designed to achieve an illuminance of 500lx, upon non-occupancy of the current zone but with global occupancy, the controller dims the illuminance level to 300lx, and upon global non-occupancy, the controller completely shuts down the luminaires. Several strategies are applied in order to achieve energy neutral: 1) choosing the low energy consuming sensors, for example the “1 uA Panasonic passive infrared motion sensor”; 2) designing the transceiver operation schedule so that the peak consumption will not exceed the available PV-harvested energy at any given time; 3) designing an “energy-neutral MAC protocol” with “single radio multichannel” that facilitates the “low-power listening”. The system is implemented and tested in an 8m x 6.5m x 3m office setting with eight LED lighting devices, each of which is of 1500 lumen. The space is divided into 6.5 sqft zones based on the daylighting availability. The information communicated between neighboring luminaires include “dimming level, difference of reference set-point and light sensor measurement, and the channel index”, the system is implemented using a Matlab testbed and is tested to have met the lighting and energy goals (li & Pandharipande, 2015).
From the review, we can see that the application of automatic lighting control system has great energy saving potentials. Although the distributed control system is not as energy efficient as the centralized system (Meugheuvel, Pandharipande, Caicedo, & Hof, 2014), its advantages in modularity still gains great popularity. Within the distributed system, the networked system achieves better lighting performance than the standalone system. However as a result of increased system complexity, the energy consumption of the control system itself needs to be considered carefully in order to achieve good energy performance.
References li, s., & Pandharipande, A. (2015). Networked Illumination Control With Distributed Light-Harvesting Wireless Sensors. Sensors Journal, IEEE, 1662-1669.
Meugheuvel, N., Pandharipande, A., Caicedo, D., & Hof, P. (2014). Distributed lighting control with daylight and occupancy adaptation. Energy and Buildings, 321-329.
U.S. Energy Information Administration. (2014, 5 23). How much electricity is used for lighting in the United States. Retrieved from EIA U.S. Information Administration: http://www.eia.gov/tools/faqs/faq.cfm?id=99&t=3
Wikipedia. (2014, 12 13). Lighting control system. Retrieved from Wikipedia: http://en.wikipedia.org/wiki/Lighting_control_system
Wikipedia. (2014, 10 27). Occupancy sensor. Retrieved from Wikipedia: http://en.wikipedia.org/wiki/Occupancy_sensor