CLTC partnered with Bonneville Power Administration, Pacific Gas and Electric Company and Southern California Edison to survey occupancy at four test sites in California and four test sites in Washington State. The sites selected for the research study represent market sectors identified as having the greatest potential to achieve energy savings with exterior adaptive lighting solutions. Market analysis completed during an earlier phase of this effort indicates these sites, including commercial office buildings, retail stores and K–12 educational facilities, have minimal pedestrian traffic at night and constitute a significant presence in utilities’ service territories.
In 2013 CLTC’s project team collected occupancy pattern data using occupancy sensors, data loggers and receivers at the eight sites. The sensors were installed in groups at the chosen survey locations, attached to receivers that would emit a voltage pulse whenever any sensor in the group detected motion. This information was logged by a data logger and later used to study occupancy patterns in the respective spaces. After a 90-day data collection period, the data was reduced and plotted.
The collected results show occupancy rates of the eight locations during dark hours of the night. The occupancy results varied among the areas with an office building located in Richmond, WA (referred to as Large Office Building A) having a relatively low nighttime occupancy rate while the Big Box Store in West Sacramento, CA has a high rate with one of the receivers detecting an average occupancy rate of 55% within the 30-second bin rate. Areas that showed the majority of nighttime traffic were often areas closest to the building facilities while the exterior perimeter of the parking lots would often experienced the lowest rates.
Based on analysis done by the CLTC, potential energy savings were mapped according to separate control strategies that could be implemented during the commissioning of occupancy-based adaptive lighting systems for both 50% and 20% power low modes. The energy savings calculated used a software simulation to mimic motion sensor time delays of 5, 10, and 15 minutes and were reported accordingly. The maximum energy savings calculated in this report assumes that the light fixture would only operate on high when the sensor monitoring the immediate area to the fixture detects occupancy with an occupancy sensor using a 5-minute delay. The analysis of all areas demonstrates a large capacity for energy savings (between 68%–94%) if fixtures are upgraded to LED technology and occupancy based adaptive controls are implemented.
Principal Investigator: Konstantinos Papamichael