Skip to main content
editor@theusajournals.com | Oscar Publishing Services Journal Home

American Journal of Applied Science and Technology

Peer Reviewed | Open Access | E-ISSN: 2771-2745
Published Article

INTELLIGENT CONTROL METHODS FOR STREET LIGHTING SYSTEMS

INTELLIGENT CONTROL METHODS FOR STREET LIGHTING SYSTEMS

  • Muratova Zulfizar
    Doctoral Student Of Andijan Machine Building Institute, Uzbekistan
Street Lighting Systems Intelligent Control Models Adaptive Lighting

This comprehensive article explores the transformative journey of street lighting systems, highlighting recent advancements in intelligent control models, methods, and algorithms. The narrative encompasses the evolution from traditional, fixed-schedule lighting to dynamic, adaptive systems that respond to real-time data, sensors, and communication technologies. The article delves into the benefits, challenges, and future outlook of these innovations, emphasizing the role of machine learning, IoT integration, and specialized algorithms. It also discusses the positive impacts on energy efficiency, safety, and the overall development of smart cities.

Zafardinov Muslimbek, & Oqilov Azizbek. (2023). ROBOTLARINI ROS TIZIMI ORQALI TASHQI QURILMALAR BILAN BOG‘LASH AFZALLIKLARI. FAN, JAMIYAT VA INNOVATSIYALAR, 1(1), 107–113. Retrieved from https://michascience.com/index.php/fji/article/view/21

Chen, P., Lin, J., & Wong, K. P. (2018). Intelligent Street Lighting System with Traffic Flow Optimization. IEEE Transactions on Industrial Informatics, 14(4), 1427-1435. doi:10.1109/TII.2017.2771502

Arif, M. E., Torabi, M., & Parlikad, A. K. (2019). Machine Learning-Based Adaptive Street Lighting Control for Smart Cities. IEEE Transactions on Industrial Informatics, 15(6), 3339-3347. doi:10.1109/TII.2018.2874983

Yang, C., & Mei, L. (2020). A Survey of Intelligent Street Lighting Systems: Challenges and Opportunities. Journal of Sensors, 2020, 1-22. doi:10.1155/2020/8861417

Zafar, F., Zafar, F., Kim, D. H., & Kim, D. Y. (2021). Smart Street Lighting: A Review of Adaptive Control Strategies and Emerging Technologies. Energies, 14(5), 1238. doi:10.3390/en14051238

Zafardinov Muslimbek, & Oqilov Azizbek. (2023). ROBOTLARINI ROS TIZIMI ORQALI TASHQI QURILMALAR BILAN BOG‘LASH AFZALLIKLARI. FAN, JAMIYAT VA INNOVATSIYALAR, 1(1), 107–113. Retrieved from https://michascience.com/index.php/fji/article/view/21

Mukhitdinov, J. P., & Safarov, E. X. (2021). Reviewing technologies and devices for drying grain and oilseeds. Chemical Technology, Control and Management, 2021(3), 05-19. URL: https://ijctcm.researchcommons.org/journal/vol2021/iss3/1/

Pakhritdinovich, M. J., & Xasanovich, S. E. (2022). Research of a combined energy-saving drum dryer for drying sunflower seeds. Harvard Educational and Scientific Review, 2(1). URL: https://journals.company/index.php/hesr/article/view/25

Mukhitdinov, J., & Safarov, E. (2022, May). Increasing the Productivity and Energy Efficiency of the Drum Grain Dryer. In International Scientific Conference on Agricultural Machinery Industry “Interagromash"” (pp. 2151-2158). Cham: Springer International Publishing. URL: https://link.springer.com/chapter/10.1007/978-3-031-21219-2_241

Xasanovich, S. E. (2023). Neural Network Model of Energy Saving of Combined Drum Dryer. Texas Journal of Engineering and Technology, 20, 45-50. URL: https://zienjournals.com/index.php/tjet/article/view/4060

Xasanovich, S. E. (2023). Neural Network Model of Sunflower Seed Drying Process in Combined Drum Dryer. Eurasian Journal of Engineering and Technology, 18, 45-49. URL: https://www.geniusjournals.org/index.php/ejet/article/view/4211

SAFAROV, E. STUDY OF THE INFLUENCE OF THE DRYING AGENT SPEED ON THE OPERATION OF A COMBINED ENERGY-SAVING DRUM DRYER. UNIVERSUM, 18-23. URL: https://7universum.com/ru/tech/archive/item/14120