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Energy-efficient street lighting through embedded adaptive intelligence

Energy-efficient street lighting through embedded adaptive intelligence
Energy-efficient street lighting through embedded adaptive intelligence
Streetlights place a heavy demand on electricity usage, providing significant financial and environmental burdens. Consequently, initiatives to reduce energy consumption have been proposed, usually by turning off or dimming the streetlight. In this paper, we propose an adaptive lighting scheme based on traffic sensing, which adaptively adjusts streetlight brightness based on current traffic conditions. The algorithm has been validated through simulation using the SUMO and OMNeT++ tools and, for two different geographical locations, the energy consumption evaluated with respect to traffic speed and volume. The simulation results presented indicate that the proposed lighting scheme can consume up to 30% less energy when compared to the state-of-the-art.
978-1-4799-0314-6
Lau, Sei Ping
7f257719-b0b1-4666-8fc8-6c442f4dfc40
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
White, Neil M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Lau, Sei Ping, Merrett, Geoff V. and White, Neil M. (2013) Energy-efficient street lighting through embedded adaptive intelligence At Symposium on Intelligent Transportation Systems (ICALT-ITS’2013), Tunisia. 29 - 31 May 2013.

Lau, Sei Ping, Merrett, Geoff V. and White, Neil M. (2013) Energy-efficient street lighting through embedded adaptive intelligence At Symposium on Intelligent Transportation Systems (ICALT-ITS’2013), Tunisia. 29 - 31 May 2013.

Record type: Conference or Workshop Item (Paper)

Abstract

Streetlights place a heavy demand on electricity usage, providing significant financial and environmental burdens. Consequently, initiatives to reduce energy consumption have been proposed, usually by turning off or dimming the streetlight. In this paper, we propose an adaptive lighting scheme based on traffic sensing, which adaptively adjusts streetlight brightness based on current traffic conditions. The algorithm has been validated through simulation using the SUMO and OMNeT++ tools and, for two different geographical locations, the energy consumption evaluated with respect to traffic speed and volume. The simulation results presented indicate that the proposed lighting scheme can consume up to 30% less energy when compared to the state-of-the-art.

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More information

Published date: 29 May 2013
Venue - Dates: Symposium on Intelligent Transportation Systems (ICALT-ITS’2013), Tunisia, 2013-05-29 - 2013-05-31
Organisations: Electronic & Software Systems, EEE

Identifiers

Local EPrints ID: 350780
URI: http://eprints.soton.ac.uk/id/eprint/350780
ISBN: 978-1-4799-0314-6
PURE UUID: e8c57853-3f22-4a5d-a781-f4caa2831071
ORCID for Geoff V. Merrett: ORCID iD orcid.org/0000-0003-4980-3894
ORCID for Neil M. White: ORCID iD orcid.org/0000-0003-1532-6452

Catalogue record

Date deposited: 08 Apr 2013 13:15
Last modified: 13 Aug 2017 15:17

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Contributors

Author: Sei Ping Lau
Author: Geoff V. Merrett ORCID iD
Author: Neil M. White ORCID iD

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