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Artificial Neural Network based Smart and Energy Efficient Street Lighting System: A Case Study for Residential area in Hosur

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Item embargoed until 2021-04-24. Restrictions imposed by the publisher
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final draft
Date
2019
Author(s)
Mohandas, Prabu
Dhanaraj, Jerline Sheebha Anni
Gao, Xiao-Zhi
Unique identifier
10.1016/j.scs.2019.101499
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Citation
Mohandas, Prabu. Dhanaraj, Jerline Sheebha Anni. Gao, Xiao-Zhi. (2019). Artificial Neural Network based Smart and Energy Efficient Street Lighting System: A Case Study for Residential area in Hosur.  Sustainable cities and society, 48, 101499. 10.1016/j.scs.2019.101499.
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© Elsevier Ltd.
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CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract

Smart city is the term described to integrate all facilities to the people in a frequently accessible manner. Street lighting system is one of the part of the facility provided in smart cities. The unwanted utilisation of the street lighting affects the economic status of the country indirectly. Power consumption through street lighting is major problem, hence action plan is taken to promote the reduction policies of the power consumption. Reducing the unnecessary power consumption is not a simple task, but with soft computing approaches power consumption can be reduced. The objective of this article is to present an ANN based energy efficient smart street lighting systems. The proposed design were implemented and executed in a residential area, Hosur and the results are carried out at different scenarios and various seasons. The decision making module exploits the analysis factors obtained via lighting sensor, motion sensor, PIR sensor, etc. artificial neural network and fuzzy logic controller makes an efficient decision making process for demand based utilisation and to avoid the unnecessary utilisation of street lights. The five levels of scenarios are tested and implemented in a real time. Through this work, the smart and energy efficiency street lighting system reduced the unwanted utilisation by 34% and reduced the power consumption rate of 13.5%.

Subjects
street lighting   artificial neural network   fuzzy logic controller   sensors   
URI
https://erepo.uef.fi/handle/123456789/7600
Link to the original item
http://dx.doi.org/10.1016/j.scs.2019.101499
Publisher
Elsevier BV
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  • Luonnontieteiden ja metsätieteiden tiedekunta [1123]
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