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Intelligent autonomous street lighting system based on weather forecast using LSTM

Author

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  • Tukymbekov, Didar
  • Saymbetov, Ahmet
  • Nurgaliyev, Madiyar
  • Kuttybay, Nurzhigit
  • Dosymbetova, Gulbakhar
  • Svanbayev, Yeldos

Abstract

Existing traditional street lighting systems are characterized by a high level of energy consumption compared to automated intelligent systems that offer different operating modes depending on traffic and power system load. The most promising energy sources systems are hybrid installations that switch the load to the grid in adverse weather conditions. Such systems may increase the energy efficiency of the street lighting system, but they are not completely autonomous. In this case, the most important problem is to provide the street lighting system with energy in adverse weather conditions. In this paper, an autonomous street lighting system with adaptive energy consumption based on weather forecast was shown. The proposed street lighting system is completely independent of traditional power sources and is completely powered by solar panels. The main energy consumers of a street lighting system are lamps. The consumption of lamps can be changed to the minimum brightness level required by outdoor lighting standards. Forecasts of energy generation by solar panels can be obtained using LSTM. It is based on weather and solar radiation forecasts data for the coming days. The brightness levels of lamps are calculated and changed using the methods proposed in this paper. The probability of reaching the critical level of batteries does not exceed 0.10% and fluctuates around 0.05% most of the time when simulating for 1000 days under random weather conditions. Simulation of energy consumption by the street lighting system using the proposed method shows stable and sustainable performance in Almaty, Kazakhstan. The obtained results in this work can be used for designing autonomous street lighting and outdoor lighting systems.

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  • Tukymbekov, Didar & Saymbetov, Ahmet & Nurgaliyev, Madiyar & Kuttybay, Nurzhigit & Dosymbetova, Gulbakhar & Svanbayev, Yeldos, 2021. "Intelligent autonomous street lighting system based on weather forecast using LSTM," Energy, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:energy:v:231:y:2021:i:c:s0360544221011506
    DOI: 10.1016/j.energy.2021.120902
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    as
    1. Chen, Pengzhan & Liu, Mengchao & Chen, Chuanxi & Shang, Xin, 2019. "A battery management strategy in microgrid for personalized customer requirements," Energy, Elsevier, vol. 189(C).
    2. Nurzhigit Kuttybay & Ahmet Saymbetov & Saad Mekhilef & Madiyar Nurgaliyev & Didar Tukymbekov & Gulbakhar Dosymbetova & Aibolat Meiirkhanov & Yeldos Svanbayev, 2020. "Optimized Single-Axis Schedule Solar Tracker in Different Weather Conditions," Energies, MDPI, vol. 13(19), pages 1-18, October.
    3. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    4. Alberto Gutierrez-Escolar & Ana Castillo-Martinez & Jose M. Gomez-Pulido & Jose-Maria Gutierrez-Martinez & Zlatko Stapic & Jose-Amelio Medina-Merodio, 2015. "A Study to Improve the Quality of Street Lighting in Spain," Energies, MDPI, vol. 8(2), pages 1-19, January.
    5. Kovács, András & Bátai, Roland & Csáji, Balázs Csanád & Dudás, Péter & Háy, Borbála & Pedone, Gianfranco & Révész, Tibor & Váncza, József, 2016. "Intelligent control for energy-positive street lighting," Energy, Elsevier, vol. 114(C), pages 40-51.
    6. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
    7. Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.
    8. Dewangan, Chaman Lal & Singh, S.N. & Chakrabarti, S., 2020. "Combining forecasts of day-ahead solar power," Energy, Elsevier, vol. 202(C).
    9. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    10. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    11. Djuretic, Andrej & Kostic, Miomir, 2018. "Actual energy savings when replacing high-pressure sodium with LED luminaires in street lighting," Energy, Elsevier, vol. 157(C), pages 367-378.
    12. Beccali, Marco & Bonomolo, Marina & Ciulla, Giuseppina & Galatioto, Alessandra & Lo Brano, Valerio, 2015. "Improvement of energy efficiency and quality of street lighting in South Italy as an action of Sustainable Energy Action Plans. The case study of Comiso (RG)," Energy, Elsevier, vol. 92(P3), pages 394-408.
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