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Foundational research evaluated the performance of commercially-available connected lighting systems, documented the range of performance for key characteristics, and identified market adoption issues and technology development needs.

PNNL research is transitioning from its original focus on characterizing commercially available lighting systems to the development and demonstration of new data-driven approaches that address market adoption issues for a broader scope of building electric systems – including lighting, information technology, battery energy storage, and electric vehicle charging.

NightSpire is an adjustable spot and flood LED light with either a battery-powered or ignition-powered Bluetooth wireless controller.

Lighting systems with distributed intelligence sensors and modern network interfaces – often referred to as “connected,” “networked,” or “Internet-of-Things” lighting systems – have been on the market for many years. Their ability to monitor their operation and the environment they serve holds much potential to adapt their behavior to specific installation conditions and needs and facilitate many data-driven use cases. However, the deployment of such systems has been limited to date, and many questions remain about how well they will work, whether they will actually save more energy than conventional systems, and whether they will offer enough benefits and value-added features to justify the investment. PNNL investigations of data-driven lighting systems are designed to answer these questions, to identify market adoption issues and technology development needs, and to contribute to development of industry standards and model specifications.

If you are interested in participating in DOE CLTB studies, send an email to michael.poplawski@pnnl.gov with your contact information and area of interest.

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LP-8 is enough? Updates to the standard for (true) commissioning for lighting and lighting control systems (IES Annual Conference presentation, August 2023)

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PNNL developed a connected lighting test bed (CLTB) in Portland, Oregon, to meet the laboratory needs of this research. The CLTB enables the efficient installation of indoor and outdoor lighting devices, with a focus on electrical performance evaluation rather than emulation of real-world lighting environments.

Many industry partners have supported studies in the CLTB over the past years, including Autani, Cisco, Cree Lighting, Digital Lumens, Enlighted, GE Current, Focal Point, Hubbell Lighting, Igor, Leviton, MHT Lighting, Molex, NuLEDs, Signify, Silver Spring Networks – now part of Itron, Silvair, Synapse Wireless, Telematics Wireless, Xicato, and others. These partners donate lighting devices and systems, software, network communication infrastructure, and configuration expertise.

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The connected lighting test bed (CLTB) enables the efficient installation of indoor and outdoor lighting devices, with a focus on electrical performance evaluation rather than emulation of real-world lighting environments. Two ceiling grids are available for installing indoor lighting luminaires. Modular wall and flooring systems enable the creation of mock rooms that facilitate the evaluation of how obstacles affect wireless communication and sensor performance. The CLTB also has dedicated infrastructure for streetlighting luminaires, network communication (including ethernet switches and wireless communication gateways), circuit-level power and energy metering, and programmable power sources to study the impact of varying power conditions.

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How do we utilize the remote monitoring capabilities of connected lighting systems to facilitate automated fault detection, diagnostics, and prediction? (IES-PNNL webinar presentation, July 2023)

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PNNL provides technical support for early adopters who often pave the way for broad adoption by taking chances and providing valuable feedback. Contact us at michael.poplawski@pnnl.gov to explore potential collaborations.

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T Aoki · 2016 · 14 — Bright-field imaging of compound semiconductors using aberration-corrected scanning transmission electron microscopy, Toshihiro Aoki, Jing Lu, ...

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