Report | Fact Sheet
This report collected, aggregated, and analyzed zone- and fixture-level energy monitoring interval data from networked lighting controls (NLC) systems in 114 buildings across a variety of building types in North America, representing over 1,200 zones with an average of 60 days of monitoring data per building.
The California Lighting Technology Center, in collaboration with the California Energy Commission, is conducting research to develop and evaluate technology that integrates automated controls for HVAC, electric lighting and dynamic fenestration systems.
Understanding the needs of the industry and the customer to support the adoption of IoT technologies that can increase the uptake of energy saving products (e.g., LED lighting, sensors and controls) through energy savings opportunities (e.g., plug loads and HVAC) or valued non-energy benefits.
This is a case study for the Tinker Air Force Base. By replacing the existing lighting with LED fixtures, this project saved more than 60% energy compared to the existing technology. This is consistent with savings of converting either fluorescent or high-intensity discharge fixtures with either new LED fixtures or retrofit kits, which typically result in at least 45% savings. The lighting controls saved between 8-23% compared to the LED baseline. Because LEDs are very efficient, the new LED baseline uses less energy. As a result, the 20%+ savings does not result in sufficient savings for a reasonable payback. However, using lighting controls to control other building systems can make the lighting and control system more cost effective.
The Lighting Research Center investigated potential opportunities for using lighting controls to reduce HVAC energy using lighting sensors in commercial buildings. Research report conducted for the Lighting Energy Alliance.
Fact sheet that addresses the implementation of connected building technology being evaluated by Nantum by Prescriptive Data, that has been developed by a real-estate company to manage its portfolio. Nantum is cloud based, powered by machine learning, and predictively ramps the building HVAC systems up and down to optimize efficiency based on occupancy patterns, weather conditions, and real-time electricity consumption. The system also provides energy efficiency recommendations to building operators in real-time.
GSA’s Green Proving Ground program recently assessed the potential of wireless sensor technology to provide a cost-effective and facilities-friendly way of helping data center operators visualize and implement system changes that reduce overall energy consumption. Findings include significant cost savings, as well as a substantial reduction in cooling load and CO2 emissions. Sensors utilizing a wireless mesh network and data management software to capture and graphically display real time conditions for energy optimization were installed in a demonstration project.