Minimizing Electricity Cost through Smart Lighting Control for Indoor Plant Factories

Lork, Clement; Cubillas, Michael; Ng, Benny Kai Kiat; Yuen, Chau; Tan, Matthew

doi:10.1109/iecon43393.2020.9255061

Cited by 8 publications

(14 citation statements)

References 13 publications

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“…Lork et al [30] used a standard ISL29125 lux meter to measure PAR light with an unspecified method of calibration, likely a conversion formula. This is a common approach also used in all versions of MIT's Food Computer [11,31].…”

Section: Introductionmentioning

confidence: 99%

“…The Lork et al [30] system was classified as a MISH-C system and uses the Nutrient Film Technique (NFT), applying a film of nutrients to the roots of the plant with a stream of water. The system was built on a RPi 3 with an Arduino-based microcontroller and standard hydroponic system sensors attached: humidity, temperature, EC pH, as well as a camera.…”

Section: Introductionmentioning

confidence: 99%

“…The system was built on a RPi 3 with an Arduino-based microcontroller and standard hydroponic system sensors attached: humidity, temperature, EC pH, as well as a camera. The system included image monitoring, a full data logging function, and a smart adapting light system that tracked the price of electricity and leaf size and adjusted the lighting based on these among other variables [30]. While it can be safely assumed that their PAR readings were not as accurate as other studies, they were still able to save money, by accounting for electricity price in their smart lighting algorithm and maintain acceptable biomass for their lettuce crop.…”

Section: Introductionmentioning

confidence: 99%

“…Mohagheghi and Moallem [34] used the set points of the highest PAR value provided by the manufacturer, the wattage, and the values provided for the spectrum by Ryer [38] in their calibration formula. As with Lork et al [30], there was no comparison to any quantum sensor or lab spectrometer to validate the accuracy of their PAR measurements. The experiment took place in a closed grow tent that used a centrally positioned halogen light to simulate sunlight and two red and blue spectrum grow lights positioned at a 45-degree angle on both sides of the plants.…”

Section: Introductionmentioning

confidence: 99%

“…The systems that do use cheap sensors to estimate PAR in full IoT systems, use sensors that have limited abilities to capture the red spectrum for PAR, stopping at 615 nm [33,34]. Otherwise, they use sensors designed to measure luminous flux that focus on 555 nm range [30]. Additionally, Lork et al [30] and Mohagheghi and Moallem [33] use standard techniques that convert various sensor inputs to PAR.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Adaptalight: An Inexpensive PAR Sensor System for Daylight Harvesting in a Micro Indoor Smart Hydroponic System

et al. 2022

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Environmental changes and the reduction in arable land have led to food security concerns around the world, particularly in urban settings. Hydroponic soilless growing methods deliver plant nutrients using water, conserving resources and can be constructed nearly anywhere. Hydroponic systems have several complex attributes that need to be managed, and this can be daunting for the layperson. Micro Indoor Smart Hydroponics (MISH) leverage Internet of Things (IoT) technology to manage the complexities of hydroponic techniques, for growing food at home for everyday citizens. Two prohibitive costs in the advancement of MISH systems are power consumption and equipment expense. Reducing cost through harvesting ambient light can potentially reduce power consumption but must be done accurately to sustain sufficient plant yields. Photosynthetic Active Radiation (PAR) meters are commercially used to measure only the light spectrum that plants use, but are expensive. This study presents Adaptalight, a MISH system that harvests ambient light using an inexpensive AS7265x IoT sensor to measure PAR. The system is built on commonly found IoT technology and a well-established architecture for MISH systems. Adpatalight was deployed in a real-world application in the living space of an apartment and experiments were carried out accordingly. A two-phase experiment was conducted over three months, each phase lasting 21 days. Phase one measured the IoT sensor’s capability to accurately measure PAR. Phase two measured the ability of the system to harvest ambient PAR light and produce sufficient yields, using the calibrated IoT sensor from phase one. The results showed that the Adaptalight system was successful in saving a significant amount of power, harvesting ambient PAR light and producing yields with no significant differences from the control. The amount of power savings would be potentially greater in a location with more ambient light. Additionally, the findings show that, when calibrated, the AS7265x sensor is well suited to accurately measure PAR light in MISH systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptalight: An Inexpensive PAR Sensor System for Daylight Harvesting in a Micro Indoor Smart Hydroponic System

et al. 2022

View full text Add to dashboard Cite

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Energy and Economic Sustainable Illumination Design for a Gymnasium: A Case Study

Rosdi,

Sathyamohan,

Maniyarasu

et al. 2023

J. Phys.: Conf. Ser.

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Gymnasium lighting design is crucial to provide better visibility, improve overall mood and serve a good experience to the occupant. Conventional artificial lighting might serve these well, but it consumes a significant amount of energy. In this report, a sustainable lighting design for a gymnasium at Taylor’s University, Subang Jaya, Malaysia, is developed per the SIRIM 33:2019. The aims are to provide better energy-efficient and sustainable lighting design solutions while maintaining good visibility, overall mood, and meeting the required illumination level. The design is developed in a 3D lighting simulation software, the DIALux Evo 10. The proposed LED lighting design is compared with the existing high-pressure sodium high-bay lighting system. Criterion such as the illuminance level, glare rating index, colour rendering, energy performance and economic assessment are analysed. Result of the analysis shows that the proposed LED luminaires achieved of 68% potential energy consumption reduction, a payback period of 4.9 years and an annual life cost cycle of RM 1, 345.76, compared to the existing fluorescent luminaires’ annual life cost cycle of RM 3,570.29. This shows that LED luminaires have better energy performance, and improved cost saving while producing better illuminance level output. The outcome of this study can be considered for future gymnasium lighting design to achieve energy and economic sustainability.

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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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Minimizing Electricity Cost through Smart Lighting Control for Indoor Plant Factories

Cited by 8 publications

References 13 publications

Adaptalight: An Inexpensive PAR Sensor System for Daylight Harvesting in a Micro Indoor Smart Hydroponic System

Adaptalight: An Inexpensive PAR Sensor System for Daylight Harvesting in a Micro Indoor Smart Hydroponic System

Energy and Economic Sustainable Illumination Design for a Gymnasium: A Case Study

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

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