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

Fouad Agramelal (), Mohamed Sadik, Youssef Moubarak and Saad Abouzahir
Additional contact information
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

Energies, 2023, vol. 16, issue 21, 1-42

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.

Keywords: smart streetlight; deep learning; artificial intelligence; smart control; computer vision; YOLO; Li-Fi (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/21/7415/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/21/7415/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:21:p:7415-:d:1273360

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().