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Energy-efficient traffic-aware street lighting using autonomous networked sensors

Energy-efficient traffic-aware street lighting using autonomous networked sensors
Energy-efficient traffic-aware street lighting using autonomous networked sensors
Street lighting is a ubiquitous utility. It does not only illuminate the streets during the night but also helps to prevent crime and traffic collisions. However, to sustain its operation is a heavy burden both financially and environmentally. Because of this, several initiatives have been proposed to reduce its energy consumption. However, most initiatives are mainly aimed at energy conservation and have given little consideration about the usefulness of street lighting.
A Streetlight Usefulness Model, an evaluation metric used to measure the usefulness of street lighting to road users, is proposed. Using StreetlightSim, a real-time co-simulation environment developed as part of this research, the energy efficiency and usefulness of six existing street lighting schemes have been evaluated. Their performances were used as baseline results which later justified the proposal of Traffic-aware Lighting Scheme Management Network (TALiSMaN). Simulation results show that TALiSMaN can achieve comparable or improved usefulness (> 90%) to existing schemes, while consuming as little as 1 – 55% of the energy required by existing schemes.
To consider the limitation of ‘off-grid’ streetlights – those powered locally by renewable energy, TALiSMaN has been enhanced with an energy demand predictor to ensure that a limited energy budget can be used fairly throughout the whole night. This enhanced scheme is known as TALiSMaN-Green. Combined with knowledge of the amount of energy stored, and predicting sunrise times, TALiSMaN-Green modulates the lighting levels requested by TALiSMaN if the energy stored is predicted to be insufficient for an entire night. The results show that this scheme extends the operational lifetime of solar-powered streetlights from 2 to 16 hours. Evaluated with real traffic flow and solar readings, TALiSMaN-Green can maintain streetlight usefulness at 60 – 80% (mean = 73% with standard deviation of 9%). In comparison, the streetlight usefulness of TALiSMaN was reduced to below 30%.
University of Southampton
Lau, Sei Ping
7f257719-b0b1-4666-8fc8-6c442f4dfc40
Lau, Sei Ping
7f257719-b0b1-4666-8fc8-6c442f4dfc40
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Weddell, Alexander
3d8c4d63-19b1-4072-a779-84d487fd6f03
White, Neil
c7be4c26-e419-4e5c-9420-09fc02e2ac9c

Lau, Sei Ping (2016) Energy-efficient traffic-aware street lighting using autonomous networked sensors. Doctoral Thesis, 185pp.

Record type: Thesis (Doctoral)

Abstract

Street lighting is a ubiquitous utility. It does not only illuminate the streets during the night but also helps to prevent crime and traffic collisions. However, to sustain its operation is a heavy burden both financially and environmentally. Because of this, several initiatives have been proposed to reduce its energy consumption. However, most initiatives are mainly aimed at energy conservation and have given little consideration about the usefulness of street lighting.
A Streetlight Usefulness Model, an evaluation metric used to measure the usefulness of street lighting to road users, is proposed. Using StreetlightSim, a real-time co-simulation environment developed as part of this research, the energy efficiency and usefulness of six existing street lighting schemes have been evaluated. Their performances were used as baseline results which later justified the proposal of Traffic-aware Lighting Scheme Management Network (TALiSMaN). Simulation results show that TALiSMaN can achieve comparable or improved usefulness (> 90%) to existing schemes, while consuming as little as 1 – 55% of the energy required by existing schemes.
To consider the limitation of ‘off-grid’ streetlights – those powered locally by renewable energy, TALiSMaN has been enhanced with an energy demand predictor to ensure that a limited energy budget can be used fairly throughout the whole night. This enhanced scheme is known as TALiSMaN-Green. Combined with knowledge of the amount of energy stored, and predicting sunrise times, TALiSMaN-Green modulates the lighting levels requested by TALiSMaN if the energy stored is predicted to be insufficient for an entire night. The results show that this scheme extends the operational lifetime of solar-powered streetlights from 2 to 16 hours. Evaluated with real traffic flow and solar readings, TALiSMaN-Green can maintain streetlight usefulness at 60 – 80% (mean = 73% with standard deviation of 9%). In comparison, the streetlight usefulness of TALiSMaN was reduced to below 30%.

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Published date: January 2016

Identifiers

Local EPrints ID: 440758
URI: http://eprints.soton.ac.uk/id/eprint/440758
PURE UUID: 8d86a8c3-a150-40b8-887f-a8f9b53a33ef
ORCID for Geoff Merrett: ORCID iD orcid.org/0000-0003-4980-3894
ORCID for Alexander Weddell: ORCID iD orcid.org/0000-0002-6763-5460
ORCID for Neil White: ORCID iD orcid.org/0000-0003-1532-6452

Catalogue record

Date deposited: 15 May 2020 16:32
Last modified: 19 May 2020 00:35

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Contributors

Author: Sei Ping Lau
Thesis advisor: Geoff Merrett ORCID iD
Thesis advisor: Alexander Weddell ORCID iD
Thesis advisor: Neil White ORCID iD

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