TY - GEN
T1 - Energy Efficient Control Approach for Street Lighting
AU - Ntshangase, Mbongiseni Samuel
AU - Musawenkosi Langa, Hendrick
AU - Donald Mathaba, Tebello N.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In the ever-evolving landscape of urban development, traditional street lighting proves static and energy-inefficient. This article shows an adaptive control system using LED technology, real-time traffic analysis, and ultrasonic sensors. The approach, backed by recent field studies, aims to reform the street illumination. Through thorough engineering, this paper presents a dynamic system capable of retorting to the dynamic demands of the urban environment. The system not only curtails unnecessary energy consumption and environmental impact but introduces a cost-effective model for energy savings. Leveraging state-of-the-art LED advancements, joined with the precision of real-time traffic insights, our system excels in adaptability and sustainability. The experiments conducted showcase remarkable reductions in energy consumption 29.33% compared to traditional methods and underscore the potential for widespread implementation. This article not only addresses the need for energy optimization in urban areas but marks a transformative leap towards efficient, intelligent, and eco-conscious street lighting.
AB - In the ever-evolving landscape of urban development, traditional street lighting proves static and energy-inefficient. This article shows an adaptive control system using LED technology, real-time traffic analysis, and ultrasonic sensors. The approach, backed by recent field studies, aims to reform the street illumination. Through thorough engineering, this paper presents a dynamic system capable of retorting to the dynamic demands of the urban environment. The system not only curtails unnecessary energy consumption and environmental impact but introduces a cost-effective model for energy savings. Leveraging state-of-the-art LED advancements, joined with the precision of real-time traffic insights, our system excels in adaptability and sustainability. The experiments conducted showcase remarkable reductions in energy consumption 29.33% compared to traditional methods and underscore the potential for widespread implementation. This article not only addresses the need for energy optimization in urban areas but marks a transformative leap towards efficient, intelligent, and eco-conscious street lighting.
KW - Cost Minimization
KW - Energy Usage
KW - Random Variable
KW - Smart Traffic Optimization
KW - Ultrasonic Sensor
UR - http://www.scopus.com/inward/record.url?scp=85202911854&partnerID=8YFLogxK
U2 - 10.1109/SEB4SDG60871.2024.10629873
DO - 10.1109/SEB4SDG60871.2024.10629873
M3 - Conference contribution
AN - SCOPUS:85202911854
T3 - International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024
BT - International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024
Y2 - 2 April 2024 through 4 April 2024
ER -