Developing Flexible, Networked Lighting Control Systems That Reliably Save Energy in California Buildings

Brown, Richard E.; Schwartz, Peter; Nordman, Bruce; Shackelford, Jordan; Khandekar, Aditya; Jackson, Neal; Prakash, Anand Krishnan; Ghosh, Srijit; Page, Erik

doi:10.20357/b7zk5d

Cited by 1 publication

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“…The increase in the greenhouse effect puts pressure on the private and public sectors to find more sustainable solutions. According to Brown et al [30], in California in 2020, there is a bill that requires that energy savings must be doubled by 2030. The solution is to reduce energy consumption while increasing the performance of the building management system and its supporting infrastructure.…”

Section: B Smart Lighting Issues 1) Energy Efficiencymentioning

confidence: 99%

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

et al. 2022

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Smart lighting has become a universal smart product solution, with global revenues of up to US$5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird's eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof's comfort curve, correlated color temperature, and relative mean square error. Finally, we discuss in-depth open issues and future challenges in increasing user comfort in smart lighting using activity recognition.

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Section: B Smart Lighting Issues 1) Energy Efficiencymentioning

confidence: 99%