Location-aware smart lighting system for individual visual comfort in buildings

Kumar, Arun; Kajale, Aditi; Kar, Pushpendu; Shareef, Arish; Panda, Sanjib Kumar

doi:10.1109/gcce.2017.8229490

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…User comfort becomes a research opportunity in the field of smart residential lighting. Kumar et al [54] explain that visual comfort can increase by using an Android App. Android comes with a powerful interface that allows a person to adjust the lights, either automatically or manually.…”

Section: ) User Comfortmentioning

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: ) User Comfortmentioning

confidence: 99%

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

et al. 2022

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“…These energy savings have huge impacts, considering that lighting accounts for about 19% of the electrical energy generated worldwide. In commercial buildings, lighting accounts for even more, 30-40% [6][7][8][9][10][11]. This being said, smart lighting seems to be the next logical step in saving electricity and tackling the problems of world-wide electrification in today's world.…”

Section: Introductionmentioning

confidence: 99%

Ambient light adaptive LED light dimmer

et al. 2019

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This paper presents the design of an Adaptive Light Dimmer based on the method of sensingambient light content to adjust lamp's light intensity accordingly, and thus regulating the room's light content. The device is designed to work with renewable energy sources such as wind and solar energy. This would be useful in less developed countries where AC electricity is not well spread and renewable DC sources, such as solar, can be better utilized. It functions by using the TSL2561 light sensor, ATTiny85 microcontroller to output PWM to the LED driver, LT3795 LED driver to output current to an LED and LT3014 LDO to lower the input voltage and power the microcontroller and sensor. The dimmer is designed to work with a 48V input voltage and operate from an input light range of 20 to 100 Lux. Above 100 Lux the light is off and below 20 Lux the light is fully on.

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