Document Type

Article

Publication Title

Energies

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.

DOI

10.3390/en16217415

Publication Date

11-3-2023

Keywords

artificial intelligence, computer vision, deep learning, Li-Fi, smart control, smart streetlight, YOLO

Comments

Open Access, archived thanks to MDPI

License: CC BY 4.0

Uploaded: June 19, 2024

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