Comparison of simulated energy consumption by smart and conventional lighting systems in a residential setting

Soheilian, Moe; Moadab, Nima Hafezparast; Fischl, Géza; Aries, Mbc Myriam

doi:10.1088/1742-6596/1343/1/012155

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…Europe carried out programs to replace fossil-based energy sources with alternative energy sources. Soheilian et al [27] stated that from 1993 to 2009, there was an increase in electricity consumption in housing by 32%. The problem stems from lamps with a low lifespan compared to office buildings and inefficient electricity use.…”

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%

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

et al. 2022

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“…Besides the visual and non-visual benefits, daylight use in the built environment is important to decrease the energy consumption caused by electric lighting [33]. When daylight is not present or sufficient, electric lighting systems are used to satisfy lighting requirements [33,34]. The energy efficiency of these artificial systems is significantly determined by occupants' behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…Sensor-based lighting has been used for a very long time as a central control system. The sensors detect occupancy, motion, or other external factors and adjust lighting levels based on these factors [33,34]. Daylight sensors are placed in spaces where natural light can satisfy full or partial lighting requirements.…”

Section: Introductionmentioning

confidence: 99%

“…A lighting control system is considered a smart lighting system (SLS) if it uses advanced building technology and an internet-based network to convey data [33]. LEDs are the most efficient artificial light sources today, and they form the foundation of many emerging technologies and current lighting systems, including SLSs [33,34]. LEDs have the ability to change light intensity and color temperature, but they need to be combined with control systems and sensors to form SLSs.…”

Section: Introductionmentioning

confidence: 99%

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Role of Architectural Design in Creating Circadian-Effective Interior Settings

Alkhatatbeh

Asadi

2021

Energies

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Daylight variability throughout the day makes it an ideal light source for the stimulation of humans’ circadian systems. However, the key criteria, including proper quantity, quality, and hours of access to daylight, are not always present inside the built environment. Therefore, artificial light is necessary to complement the human’s visual and non-visual needs for light. Architectural design parameters, such as window area, orientation, glazing material, and surface reflectance alter the characteristics of both daylight and artificial light inside buildings. These parameters and their impact on lighting design should be considered from the early design stages to attain a circadian-effective design. In response to this need, a design approach called Human-Centric Lighting (HCL) was introduced. HCL places humans, and their visual and non-visual needs, in the center of the design process. It manipulates the light-related factors, such as spectrum and intensity, within the built environment for circadian benefits. The effect of HCL on lighting energy efficiency is still not clear. This paper reviews essential architectural design parameters and their impacts on circadian lighting design, considers the HCL design process and explores the most widely used circadian lighting metrics and standards.

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“…We conducted an experiment over 8 days in the living room of a home environment, which is also selected as the evaluation area for the lighting control in Ref. [9], The reason for selecting the living room as the evaluation area is that saving lighting energy in the living room is important, because the occupants spend most of their time in the living room in the evening, when light is required to maintain the illumination intensity [32].…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Reduction Technique for Time Delay of Turning Off Light to Occupancy Lighting Control

Futagami

Hayasaka

Taniguchi

et al. 2020

IEEJ Transactions Elec Engng

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In this study, we clarify the effectiveness of the energy-effective techniques for the occupancy lighting control that is based on an occupancy sensor. The time delay from detecting no occupant to turning off the light is used for performing the occupancy control to avoid erroneously turning off the light (hereafter referred to as false-off). Our experiment was conducted at an actual home environment over 8 days. We evaluated the performance of an image-based sensor, which detects the occupants using image-processing techniques and time-delay adaptation, which decreases the time delay without false-off. Compared with a passive infrared sensor, the image-based sensor increased the average sensing accuracy for occupants with small movements by 21.58% or more. In addition, compared with the no-adaptation case, the time-delay adaptation increased the rate of average lighting energy saving by 3.94% or more. However, the experimental results suggested that the parameter of the time-delay adaptation should be carefully set in terms of the energy-saving effect and false-off. The analysis and its practical discussions are included in this paper to achieve further energy efficiency.

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Comparison of simulated energy consumption by smart and conventional lighting systems in a residential setting

Cited by 7 publications

References 9 publications

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

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

Role of Architectural Design in Creating Circadian-Effective Interior Settings

Experimental Evaluation of Reduction Technique for Time Delay of Turning Off Light to Occupancy Lighting Control

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