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Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

Author

Listed:
  • Fouad Agramelal

    (Networking Embedded Systems and Telecommunications (NEST) Research Group, Engineering Research Laboratory (LRI), Department of Electrical Engineering, National Higher School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, Casablanca 8118, Morocco)

  • Mohamed Sadik

    (Networking Embedded Systems and Telecommunications (NEST) Research Group, Engineering Research Laboratory (LRI), Department of Electrical Engineering, National Higher School of Electricity and Mechanics (ENSEM), Hassan II University of Casablanca, Casablanca 8118, Morocco)

  • Youssef Moubarak

    (Laboratory of Information Technologies, ENSA University of Chouaib Doukkali El Jadida, El Jadida 24002, Morocco)

  • Saad Abouzahir

    (Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi P.O. Box 5224, United Arab Emirates)

Abstract

As urbanization increases, streetlights have become significant consumers of electrical power, making it imperative to develop effective control methods for sustainability. This paper offers a comprehensive review on control methods of smart streetlight systems, setting itself apart by introducing a novel light scheme framework that provides a structured classification of various light control patterns, thus filling an existing gap in the literature. Unlike previous studies, this work dives into the technical specifics of individual research papers and methodologies, ranging from basic to advanced control methods like computer vision and deep learning, while also assessing the energy consumption associated with each approach. Additionally, the paper expands the discussion to explore alternative functionalities for streetlights, such as serving as communication networks, environmental monitors, and electric vehicle charging stations. This multidisciplinary research aims to be a pivotal resource for both academics and industry professionals, laying the groundwork for future innovation and sustainable solutions in urban lighting.

Suggested Citation

  • Fouad Agramelal & Mohamed Sadik & Youssef Moubarak & Saad Abouzahir, 2023. "Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications," Energies, MDPI, vol. 16(21), pages 1-42, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7415-:d:1273360
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    References listed on IDEAS

    as
    1. Igor Wojnicki & Sebastian Ernst & Leszek Kotulski, 2016. "Economic Impact of Intelligent Dynamic Control in Urban Outdoor Lighting," Energies, MDPI, vol. 9(5), pages 1-14, April.
    2. Chen, S.X. & Gooi, H.B. & Wang, M.Q., 2013. "Solar radiation forecast based on fuzzy logic and neural networks," Renewable Energy, Elsevier, vol. 60(C), pages 195-201.
    3. Ricardo Alvarez & Fabio Duarte & Dennis Frenchman & Carlo Ratti, 2022. "Sensing Lights: The Challenges of Transforming Street Lights into an Urban Intelligence Platform," Journal of Urban Technology, Taylor & Francis Journals, vol. 29(4), pages 25-40, October.
    4. Nixon, J.D. & Bhargava, K. & Halford, A. & Gaura, E., 2021. "Analysis of standalone solar streetlights for improved energy access in displaced settlements," Renewable Energy, Elsevier, vol. 177(C), pages 895-914.
    5. Lin, Boqiang & Zhu, Junpeng, 2019. "Impact of energy saving and emission reduction policy on urban sustainable development: Empirical evidence from China," Applied Energy, Elsevier, vol. 239(C), pages 12-22.
    6. Igor Wojnicki & Leszek Kotulski, 2018. "Empirical Study of How Traffic Intensity Detector Parameters Influence Dynamic Street Lighting Energy Consumption: A Case Study in Krakow, Poland," Sustainability, MDPI, vol. 10(4), pages 1-16, April.
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