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A Hierarchical Smart Street Lighting System With Brute-Force Energy Optimization
Abstract
This paper presents a novel smart street lighting (SmSL) system in which energy consumption by a group of street lighting poles is minimized based on Brute-Force search algorithm. While outdoor lighting imposes considerable cost, maintenance, safety, and environmental issues; utilization of advanced street lighting system with energy saving, autonomous fault detection, and monitoring capabilities benefits all the players involved including municipalities and distribution companies. The proposed SmSL has a hierarchical platform. The segment or intermediate controller determines the scheduling, switching, and dimming level of each pole based on the proposed optimization subroutine and transmits the controller set points to the local pole controller through power line communications. Optimization of street lighting electrical energy is achieved by minimizing a cost function, considering operational constraints, ambient luminance, and local traffic flow. The local controller acts as an actuator and applies the received commands. The controller inherently responses to lamp fault. Moreover, pole electrical parameters and status of the lamp and its capacitor is transmitted to the intermediate controller. The supervisory controller, which is installed on a server in distribution center monitors the whole system and sends appropriate commands such as minimum required luminance in the area to the segment controller based on WiMAX wireless communications. The whole system is developed and implemented in a pilot street. The experimental results show considerable energy saving with the proposed SmSL and reduced maintenance costs.
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“Mahoor et al. [60] said that the problems surrounding street lighting are reliability, resilience, energy costs reduction, and environmental issue management.”
From Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey (2022) · cites this paper as Mahoor et al. [60]
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Cited by
Sorted by citations
Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · IEEE Access · doi:10.1109/access.2022.3169765 · Cited by 101
“Mahoor et al. [60] said that the problems surrounding street lighting are reliability, resilience, energy costs reduction, and environmental issue management.”
Background
Metadata
- Authors
- Mahoor, M., Salmasi, F. R., & Najafabadi, T. A.
- Affiliations
- University of Tehran (IR)
- Venue
- IEEE Sensors Journal
- Published
- 2017-05-01
- Volume
- 17
- Issue
- 9
- Pages
- 2871-2879
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W2595981893
- Cited by
- 81
- References
- 0
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 5 · 2023: 11 · 2022: 10 · 2021: 13 · 2020: 9 · 2019: 10 · 2018: 10 · 2017: 4
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Sensors Journal (publishedVersion)
- Funders
- Tehran Regional Electric Energy Distribution Co
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Environmental Science › Global and Planetary Change › Impact of Light on Environment and Health
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Keywords
- smart street lighting, energy optimization, hierarchical control, power line communication, WiMAX, ambient luminance, traffic flow, energy conservation, maintenance cost reduction
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- Mahoor, M., Salmasi, F. R., & Najafabadi, T. A. (2017). A Hierarchical Smart Street Lighting System With Brute-Force Energy Optimization. IEEE Sensors Journal. https://doi.org/10.1109/jsen.2017.2684240