Poster Abstract: Solar-Powered Adaptive Street Lighting Evaluated with Real Traffic and Sunlight Data
Poster Abstract: Solar-Powered Adaptive Street Lighting Evaluated with Real Traffic and Sunlight Data
Street lighting is an important resource; it has been shown to reduce crime, improve road safety, and increase economic activity. These benefits, however, come with a cost: an annual emission of 64 million tonnes of CO2. Solar-powered street lighting is attractive for its use of renewable energy and its ease of installation (particularly in off-grid applications), but sizing and control is a non-trivial task. This paper describes TALiSMaN-Green, a traffic-aware street lighting scheme which takes account of road users as well as the available energy to dynamically adjust lighting levels. Simulations using real traffic and sunlight data illustrate that solar-powered streetlights can be managed to deliver consistent usefulness throughout the night.
Energy prediction, street lighting
978-1-4503-3631-4/15/11
Lau, Sei Ping
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Weddell, Alex S.
3d8c4d63-19b1-4072-a779-84d487fd6f03
White, Neil M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Lau, Sei Ping
7f257719-b0b1-4666-8fc8-6c442f4dfc40
Weddell, Alex S.
3d8c4d63-19b1-4072-a779-84d487fd6f03
White, Neil M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Lau, Sei Ping, Weddell, Alex S., White, Neil M. and Merrett, Geoff V.
(2015)
Poster Abstract: Solar-Powered Adaptive Street Lighting Evaluated with Real Traffic and Sunlight Data.
13th ACM Conference on Embedded Networked Sensor Systems (SenSys 2015), Korea, Republic of, Korea, Republic of.
01 - 04 Nov 2015.
2 pp
.
(In Press)
(doi:10.1145/2809695.2817886).
Record type:
Conference or Workshop Item
(Poster)
Abstract
Street lighting is an important resource; it has been shown to reduce crime, improve road safety, and increase economic activity. These benefits, however, come with a cost: an annual emission of 64 million tonnes of CO2. Solar-powered street lighting is attractive for its use of renewable energy and its ease of installation (particularly in off-grid applications), but sizing and control is a non-trivial task. This paper describes TALiSMaN-Green, a traffic-aware street lighting scheme which takes account of road users as well as the available energy to dynamically adjust lighting levels. Simulations using real traffic and sunlight data illustrate that solar-powered streetlights can be managed to deliver consistent usefulness throughout the night.
Text
sensys-abstract.pdf
- Author's Original
More information
Accepted/In Press date: 23 August 2015
Venue - Dates:
13th ACM Conference on Embedded Networked Sensor Systems (SenSys 2015), Korea, Republic of, Korea, Republic of, 2015-11-01 - 2015-11-04
Keywords:
Energy prediction, street lighting
Organisations:
Electronic & Software Systems, EEE
Identifiers
Local EPrints ID: 381001
URI: http://eprints.soton.ac.uk/id/eprint/381001
ISBN: 978-1-4503-3631-4/15/11
PURE UUID: 87c7d62a-46c9-469b-b5cb-a29404df37ac
Catalogue record
Date deposited: 27 Aug 2015 13:20
Last modified: 15 Mar 2024 03:25
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