Abstract
The rapid growth in urbanization increases the number of street lamps worldwide in rural areas and cities. This places a heavy demand on electric energy, which stems from fossil fuels, thus increasing greenhouse emissions. By using stand-alone photo-voltaic street lamps, gas emissions are lowered. However, the vital aspect of street lamps is the safety of users at nighttime. The batteries of these systems are quickly depleted due to adverse weather conditions. In order to increase safety and smartly manage energy consumption, in this work we suggest using a fuzzy controller with traffic and solar radiation forecasts in this work. We suggest using a fuzzy controller with traffic and solar radiation forecasts in this work. The controller adapts light according to traffic demand, solar radiation in the upcoming three days, and battery level. First, we tested and validated several multi-step forecast models to predict solar radiation. Then the description of the system, along with simulations, was carried out. The obtained results indicate that the designed light controller is capable of lowering energy consumption, thus prolonging the system’s autonomy while at the same time assuring road safety.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
IEA, greenhouse gas emissions kernel description. https://www.iea.org/data-and-statistics/data-tools/greenhouse-gas-emissions-from-energy-data-explorer. Accessed 10 Nov 2022
LUMILEDS, luXEon Rebel ES kernel description. https://lumileds.com/wp-content/uploads/files/DS61.pdf. Accessed 30 Mar 2022
UNEP, COP-27 Annoucement kernel description. https://www.unep.org/news-and-stories/story/cop27-ends-announcement-historic-loss-and-damage. Accessed 10 Nov 2022
CIE 115:2010 recommendations for the lighting of roads for motor and pedestrian traffic. International Commission on Illumination, Vienna, Austria (2010). Kernel description
Shlayan, N., Challapali, K., Cavalcanti, D., Oliveira, T., Yang, Y.: A novel illuminance control strategy for roadway lighting based on greenshields macroscopic traffic model. IEEE Photon. J. 10(1), 1–11 (2018)
Agramelal, F., Sadik, M., El Hannani, A., Moubarak, Y.: A traffic-aware street lighting system based on fuzzy logic controller. In: 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), pp. 132–137. IEEE (2022)
Agramelal, F., Sadik, M., Sabir, E.: A dual carriageway smart street lighting controller based on multi-variate traffic forecast. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds.) AI2SD 2022. LNNS, vol. 637. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-26384-2_41
Jackett, M., Frith, W.: Quantifying the impact of road lighting on road safety-a New Zealand study. IATSS Res. 36(2), 139–145 (2013)
Lau, S.P., Merrett, G.V., Weddell, A.S., White, N.M.: A traffic-aware street lighting scheme for smart cities using autonomous networked sensors. Comput. Electr. Eng. 45, 192–207 (2015)
Mustafa, A.M., Abubakr, O.M., Derbala, A.H., Ahmed, E., Mokhtar, B.: Towards a smart highway lighting system based on road occupancy: model design and simulation. In: Sucar, E., Mayora, O., Muñoz de Cote, E. (eds.) Applications for Future Internet. LNICST, vol. 179, pp. 22–31. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49622-1_4
Petritoli, E., Leccese, F., Pizzuti, S., Pieroni, F.: Smart lighting as basic building block of smart city: an energy performance comparative case study. Measurement 136, 466–477 (2019)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
Tukymbekov, D., Saymbetov, A., Nurgaliyev, M., Kuttybay, N., Dosymbetova, G., Svanbayev, Y.: Intelligent autonomous street lighting system based on weather forecast using LSTM. Energy 231, 120902 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Agramelal, F., Sadik, M., Sabir, E., Saad, A. (2023). A Context-Aware Street Light System Based on Multi-variate Forecast and Fuzzy Logic. In: Arai, K. (eds) Intelligent Computing. SAI 2023. Lecture Notes in Networks and Systems, vol 739. Springer, Cham. https://doi.org/10.1007/978-3-031-37963-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-37963-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-37962-8
Online ISBN: 978-3-031-37963-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)