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An energy efficient street lighting framework: ANN-based approach

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Abstract

An energy efficient street lighting framework is proposed in this paper to reduce energy consumption obtained from the street lights. It is determined for various possible inter-distances offered by International Commission on Illumination. An ANN model is approached to obtain such reduced energy consumption for various traffic volumes on the road with minimum mean square error. The results of the proposed approach show an improvement over existing works.

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References

  1. Allen DM (1971) Mean square error of prediction as a criterion for selecting variables. Technometrics 13(3):469–475

    Article  Google Scholar 

  2. Campisi D, Gitto S, Morea D (2017) Light emitting diodes technology in public light system of the municipality of rome: an economic and financial analysis. Int J Energy Econ Policy 7(1):200–208

    Google Scholar 

  3. Carli R, Dotoli M, Cianci E (2017) An optimization tool for energy efficiency of street lighting systems in smart cities. IFAC-PapersOnLine 50(1):14460–14464

    Article  Google Scholar 

  4. Chakraborty A, Goswami D (2017) Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arab J Geosci 10(17):385

    Article  Google Scholar 

  5. Cho S, Dhingra V (2008) Street lighting control based on lonworks power line communication. In: 2008 IEEE international symposium on power line communications and its applications. IEEE, pp 396–398

  6. de l’Éclairage CI (2010) Lighting of roads for motor and pedestrian traffic: CIE 115: 2010. CIE

  7. Garces-Jimenez A, Castillo-Sequera JL, Del Corte-Valiente A, Gómez-Pulido JM, González-Seco EPD (2019) Analysis of artificial neural network architectures for modeling smart lighting systems for energy savings. IEEE Access 7:119881–119891

    Article  Google Scholar 

  8. Gómez-Lorente D, Rabaza O, Estrella AE, Peña-García A (2013) A new methodology for calculating roadway lighting design based on a multi-objective evolutionary algorithm. Expert Syst Appl 40(6):2156–2164

    Article  Google Scholar 

  9. Lau SP, Merrett GV, Weddell AS, White NM (2015) A traffic-aware street lighting scheme for smart cities using autonomous networked sensors. Comput Electr Eng 45:192–207

    Article  Google Scholar 

  10. Lau SP, Merrett GV, White NM (2013) Energy-efficient street lighting through embedded adaptive intelligence. In: 2013 international conference on advanced logistics and transport. IEEE, pp 53–58

  11. Marino F, Leccese F, Pizzuti S (2017) Adaptive street lighting predictive control. Energy Procedia 111:790–799

    Article  Google Scholar 

  12. Merkulov D, Oseledets IV (2019) Empirical study of extreme overfitting points of neural networks. J Commun Technol Electron 64(12):1527–1534

    Article  Google Scholar 

  13. Mohandas P, Dhanaraj JSA, Gao XZ (2019) Artificial neural network based smart and energy efficient street lighting system: a case study for residential area in hosur. Sustain Cities Soc 48:101499

    Article  Google Scholar 

  14. Ożadowicz A, Grela J (2017) Energy saving in the street lighting control system—a new approach based on the EN-15232 standard. Energ Eff 10(3):563–576

    Article  Google Scholar 

  15. Rabaza O, Gómez-Lorente D, Pérez-Ocón F, Peña-García A (2016) A simple and accurate model for the design of public lighting with energy efficiency functions based on regression analysis. Energy 107:831–842

    Article  Google Scholar 

  16. Rabaza O, Gómez-Lorente D, Pozo AM, Pérez-Ocón F (2019) Application of a differential evolution algorithm in the design of public lighting installations maximizing energy efficiency. LEUKOS, pp 1–11

  17. Räsänen T, Ruuskanen J, Kolehmainen M (2008) Reducing energy consumption by using self-organizing maps to create more personalized electricity use information. Appl Energy 85(9):830–840

    Article  Google Scholar 

  18. Sikdar PL, Thakurta PKG (2020) An energy efficient autonomous street lighting system. In: Proceedings of the global AI congress 2019. Springer, pp 589–599

  19. Sikdar PL, Thakurta PKG (2020) An improved energy-efficient street lighting system. In: 2020 7th international conference on signal processing and integrated networks (SPIN). IEEE, pp 372–376

  20. Tavares M, Carrasquilla A, Lima I (2014) Comparing different artificial neural network algorithms to estimate the lithology of albian carbonate reservoirs in Campos Basin–Brazil. In: 15th International congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 31 July–3 August 2017. Brazilian Geophysical Society, pp 834–839

  21. Zhang J, Qiao G, Song G, Sun H, Ge J (2013) Group decision making based autonomous control system for street lighting. Measurement 46(1):108–116

    Article  Google Scholar 

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Correspondence to Pragna Labani Sikdar.

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Sikdar, P.L., Thakurta, P.K.G. An energy efficient street lighting framework: ANN-based approach. Innovations Syst Softw Eng 17, 131–139 (2021). https://doi.org/10.1007/s11334-020-00375-2

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  • DOI: https://doi.org/10.1007/s11334-020-00375-2

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