Smart lighting: The way forward? Reviewing the past to shape the future

Chew, Ivan; Karunatilaka, Dilukshan; Tan, Chee Pin; Kalavally, Vineetha

doi:10.1016/j.enbuild.2017.04.083

Cited by 125 publications

(72 citation statements)

References 78 publications

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“…The introduction of lowpowered LED lights and the use of low voltage DC distribution system has increased the potential for higher energy savings due to a higher system level efficiency as compared to AC distribution system [4]. This is in addition to the energy savings that can be achieved from occupancy detection and daylight harvesting [5]. Furthermore, the need for having personalized lighting to increase the occupantsâ€™ comfort is also becoming critical [6].…”

Section: Introductionmentioning

confidence: 99%

Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting

Kandasamy

Karunagaran

Spanos

et al. 2018

Building and Environment

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Lighting contributes a significant portion to the overall energy consumption in an office building. It is thus important to reduce the energy consumption of lighting systems especially for Net Zero Energy Buildings (NZEB). Maximizing daylight harvesting can significantly increase the energy savings. With increase in demand for satisfying occupant preferences in visual comfort, the need for personalized lighting in the office space is also rising. In this paper, a novel lighting control system for Net Zero Energy Buildings (NZEB) is proposed which models the lighting system using Artificial Neural Network (ANN) and utilizes this model with the Internal Model Control (IMC) principle for controller design. Modeling the lighting system using ANN reduces the challenge of modeling a large and complex system with inherent process variability without the need to analyze extensive data-sets. The proposed ANN-IMC controller uses feedback from sensors on the task table to maintain desired illuminance, is easy to tune with just one parameter and is robust to process variability. The proposed control design is applicable to square systems where the number of lights and number of sensors are equal. However, the proposed architecture can also be extended for controlling other lighting accessories such as roller blinds. The performance of the proposed lighting control system to harvest the daylight effectively is demonstrated using both simulation results and an experimental setup in test-bed environment. The versatility of the proposed system will allow an operator to deploy personalized lighting in an office space.

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Section: Introductionmentioning

confidence: 99%

Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting

Kandasamy

Karunagaran

Spanos

et al. 2018

Building and Environment

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“…As can be seen, the majority of studies on lighting control systems focused on energy saving [37], and they achieved considerable outcomes. As summarized in a review on lighting control technologies [38], the saving on energy ranged from 35% to 68% in classroom and office environment.…”

Section: Related Workmentioning

confidence: 99%

Development and Implementation of a Self-Optimizable Smart Lighting System Based on Learning Context in Classroom

Sun

Zhang²,

Cao

2020

IJERPH

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Illumination is one of the most important environmental factors in the classroom. Researchers have discovered that lighting settings have significant impact on students’ performance. Although light-emitting diode (LED) lighting systems can precisely control brightness level and correlated color temperature (CCT), existing designs of LED lighting control systems for classrooms are focused on energy-saving but lack context-based illumination control ability. In this study, a smart lighting system with continuous evolution capability was developed. It can adjust brightness, CCT, and illuminance distribution dynamically according to specific learning context. This system allows not only manual control, but also automatic switching of scenes by integrating with school schedules. Based on existing knowledge about lighting preference, 10 lighting modes confined in the comfortable zone of Kruithof curve were proposed for various classroom scenarios. Moreover, a classroom environmental data-processing framework for collecting and analyzing learning context, illumination settings, environmental data, and students’ performance data was introduced. This framework can help researchers explore the correlation between student performance and environmental parameters.

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“…Während nach der Useful Daylight Illuminence (UDI) die optimale horizontale Ausleuchtung des Arbeitsbereichs für Nutzer zwischen 100-2000 lx liegen soll [28], zeigt sich, dass Büroangestellte vergleichsweise niedrigere Beleuchtungsstärken an ihren PC-Arbeitsplätzen bevorzugen (300 lx) [6]. Gleichzeitig soll Blendung durch Tageslicht am Arbeitsplatz, welche die Arbeitsleistung und den Nutzerkomfort maßgeblich reduziert [29], minimiert werden. Blendung tritt gewöhnlich dann auf, wenn Teilbereiche der visuellen Umgebung viel heller beleuchtet sind als der Rest.…”

Section: Visueller Komfortunclassified

Die Bedeutung von Nutzerzentrierung in automatisierten Beleuchtungssystemen

et al. 2020

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Derzeitig erwerbbare Beleuchtungssteuerungen versuchen Anforderungen allgemeiner Nutzerkomfortkriterien wie die normative Mindestbeleuchtungsstärke mittels Allgemeinbeleuchtungen und vordefinierten Kontrollkurven gerecht zu werden. Da diese Lösungen sowohl individuelle Nutzeranforderungen als auch Effekte, die sich aus spezifischen Arbeitsanforderungen ableiten lassen, weitestgehend vernachlässigen, können Akzeptanzraten von Nutzern signifikant in Mitleidenschaft gezogen werden. Darauf zurückzuführende Nutzereingriffe in automatisierte Steuerungen können intendierte Systemperformanzen sowohl in energetischer Hinsicht als auch in der biologischen Wirksamkeit beschränken. Um dem entgegenzuwirken, müssen Nutzerindividualitäten in die Steuerungslogik zurückgeführt werden. Prämisse einer erfolgreichen Umsetzung ist die Differenzierung von Raumausleuchtung und bedarfsorientierter Arbeitsplatzbeleuchtung. Positive Folge dieser Differenzierung ist eine Reduzierung des Energiebedarfs. Im Folgenden berichten die Autoren von ersten Ergebnissen einer Langzeitstudie in einem Großraumbüro mit zonaler Einzelarbeitsplatzbeleuchtung unter realen Arbeitsbedingungen. Die Ergebnisse der Studie führen auf, dass durch geeignete Zonierung der Energiebedarf für Kunstlicht reduziert werden kann. Darüber hinaus konnte über Langzeit‐Monitoring herausgefunden werden, dass Nutzereingriffen, die sich negativ auf den Energiebedarf auswirken, über erweiterte Inbetriebnahmephasen entgegengewirkt werden kann.

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Smart lighting: The way forward? Reviewing the past to shape the future

Cited by 125 publications

References 78 publications

Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting

Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting

Development and Implementation of a Self-Optimizable Smart Lighting System Based on Learning Context in Classroom

Die Bedeutung von Nutzerzentrierung in automatisierten Beleuchtungssystemen

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