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Journals / International Journal of Informatics and Applied Mathematics / 2026 / Volume: 9 Issue: 1

Comparative Study of CNN–LSTM and ConvLSTM Models for Short-Term Power Forecasting in Smart Street Lighting Systems

Mouaadh Yaichi(University of Tahri Mohamed)
Pages
—
Publication Date
2026-08-02
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Smart street lighting systems (SSLs) have emerged as an effective solution for reducing energy consumption and promoting environmental sustainability by providing adaptive lighting that responds to user demand without compromising safety or security, thereby improving overall efficiency.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Smart street lighting systems (SSLs) have emerged as an effective solution for reducing energy consumption and promoting environmental sustainability by providing adaptive lighting that responds to user demand without compromising safety or security, thereby improving overall efficiency.
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Key Findings & Data:The methodology utilized a one-step-ahead prediction strategy based on actual values.
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Conclusion & Impact:Experimental results showed that both models achieved satisfactory predictive performance.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Smart street lighting · Deep learning · CNN-LSTM

Abstract

Smart street lighting systems (SSLs) have emerged as an effective solution for reducing energy consumption and promoting environmental sustainability by providing adaptive lighting that responds to user demand without compromising safety or security, thereby improving overall efficiency. The application of computational techniques, including machine learning and deep learning, is essential for enhancing smart street lighting systems. These methods enable the prediction of power consumption, allowing dynamic adjustment of lighting levels based on real-time demand. As a result, integrating advanced algorithms improves energy efficiency, reduces operational costs, and contributes to sustainable urban lighting. This study investigates the application of deep learning models, namely Convolutional Neural Network with Long Short-Term Memory (CNN–LSTM) and Convolutional LSTM (ConvLSTM), for predicting power consumption in a smart lighting system. A univariate time-series dataset of power consumption, collected over a seven-day period, was employed. The methodology utilized a one-step-ahead prediction strategy based on actual values. Experimental results showed that both models achieved satisfactory predictive performance.

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Cite This Article

Yaichi, M. (2026). Comparative Study of CNN–LSTM and ConvLSTM Models for Short-Term Power Forecasting in Smart Street Lighting Systems. International Journal of Informatics and Applied Mathematics, Volume: 9 Issue: 1. https://doi.org/10.53508/ijiam.1842160

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References (24)

  1. Yu-Sheng Yang, Shih-Hsiung Lee, Guan-Sheng Chen, Chu-Sing Yang, Yueh-Min Huang, and Ting-Wei Hou. An implementation of high efficient smart street light management system for smart city. IEEE access, 8:38568–38585, 2020.
  2. M Beccali, M Bonomolo, F Leccese, D Lista, and G Salvadori. On the impact of safety requirements, energy prices and investment costs in street lighting refurbish ment design. Energy, 165:739–759, 2018.
  3. Didar Tukymbekov, Ahmet Saymbetov, Madiyar Nurgaliyev, Nurzhigit Kuttybay, Gulbakhar Dosymbetova, and Yeldos Svanbayev. Intelligent autonomous streetlighting system based on weather forecast using lstm. Energy, 231:120902, 2021.
  4. H Andrei, C Cepisca, V Dogaru-Ulieru, T Ivanovici, L Stancu, and PC Andrei. Measurement analysis of an advanced control system for reducing the energy consumption of public street lighting systems. In 2009 IEEE Bucharest PowerTech, pages 1–6. IEEE, 2009.
  5. Anke Blöbaum and Marcel Hunecke. Perceived danger in urban public space: The impacts of physical features and personal factors. Environment and behavior, 37(4):465–486, 2005.
  6. Marco Beccali, Valerio Lo Brano, Marina Bonomolo, Paolo Cicero, Giacomo Corvisieri, Marco Caruso, and Francesco Gamberale. A multifunctional public lighting infrastructure, design and experimental test. Journal of Sustainable Development of Energy, Water and Environment Systems, 5(4):608–625, 2017.
  7. Iván Sánchez Fernández and Jurriaan M Peters. Machine learning and deep learning in medicine and neuroimaging. Annals of the Child Neurology Society, 1(2):102 122, 2023.
  8. Maha Altalak, Mohammad Ammad uddin, Amal Alajmi, and Alwaseemah Rizg. Smart agriculture applications using deep learning technologies: A survey. Applied Sciences, 12(12):5919, 2022.
  9. Mohammad Amin Mirjalili, Alireza Aslani, Rahim Zahedi, and Mohammad Soleimani. A comparative study of machine learning and deep learning methods for energy balance prediction in a hybrid building-renewable energy system. Sustainable Energy Research, 10(1):8, 2023.
  10. Xiaoli Ren, Xiaoyong Li, Kaijun Ren, Junqiang Song, Zichen Xu, Kefeng Deng, and Xiang Wang. Deep learning-based weather prediction: a survey. Big Data Research, 23:100178, 2021.
  11. Fouad Agramelal, Mohamed Sadik, Asmaa El Hannani, and Youssef Moubarak. A traffic-aware street lighting system based on fuzzy logic controller. In 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), pages 132–137. IEEE, 2022.
  12. Fouad Agramelal, Mohamed Sadik, and Essaid Sabir. A dual carriageway smart street lighting controller based on multi-variate traffic forecast. In International Conference on Advanced Intelligent Systems for Sustainable Development, pages 476–488. Springer, 2022.
  13. Jiachen Bian and Jidong J Yang. Smart street lighting powered by renewable energy: a multi-criteria, data-driven decision framework. Sustainability, 17(13):5874, 2025.
  14. J Angela Jennifa Sujana, R Venitta Raj, and VK Raja Priya. Fog intelligence for energy efficient management in smart street lamps. Computing, 106(12):4057–4082, 2024.
  15. Safwan Nadweh, Nabil Mohammed, Charalambos Konstantinou, and Shehab Ahmed. Operational performance assessment of pv-powered street lighting: A comparative study of different machine learning prediction models. IEEE Access, 2025.
  16. Gurjeet Singh and Effariza Hanafi. Smart street lighting with prediction algorithm. In 2022 IEEE 20th Student Conference on Research and Development (SCOReD), pages 24–29. IEEE, 2022.
  17. Mouaadh Yaichi, Bousmaha Bouchiba, and Mhamed Rebhi. Iot-based smart street lighting system and enhanced energy saving using esp-now protocol. Light & Engineering, 32(6):76–84, 2024.
  18. Zexian Sun and Mingyu Zhao. Short-term wind power forecasting based on vmd decomposition, convlstm networks and error analysis. Ieee Access, 8:134422–134434, 2020.
  19. Mustaqeem and Soonil Kwon. Clstm: Deep feature-based speech emotion recognition using the hierarchical convlstm network. Mathematics, 8(12):2133, 2020.
  20. Md Zabirul Islam, Md Milon Islam, and Amanullah Asraf. A combined deep cnn lstm network for the detection of novel coronavirus (covid-19) using x-ray images. Informatics in medicine unlocked, 20:100412, 2020.
  21. Fachrizal Aksan, Yang Li, Vishnu Suresh, and Przemysław Janik. Cnn-lstm vs. lstm-cnn to predict power flow direction: A case study of the high-voltage subnet of northeast germany. Sensors, 23(2):901, 2023.
  22. Tae-Young Kim and Sung-Bae Cho. Predicting residential energy consumption using cnn-lstm neural networks. Energy, 182:72–81, 2019.
  23. Jin Wang, Liang-Chih Yu, K Robert Lai, and Xuejie Zhang. Dimensional sentiment analysis using a regional cnn-lstm model. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers), pages 225–230, 2016.
  24. Furkan Elmaz, Reinout Eyckerman, Wim Casteels, Steven Latré, and Peter Hellinckx. Cnn-lstm architecture for predictive indoor temperature modeling. Building and Environment, 206:108327, 2021