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Original Article
Solar Energy Forecasting Using LSTM and IoT-Based ESP32 Monitoring System
VM. Saravana Perumal1
Nidhi S Gowda2
Preksha K V3
Prerita H G4
1 Professor, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India. 2 3 4 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India.
Published Online: November-December 2025
Pages: 231-236
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250506031
References
1. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
2. Yadav and L. Kumar, “Solar Energy Forecasting Using Deep Learning Techniques: A Review,” Renewable and Sustainable Energy Reviews, vol. 135, pp. 110–119, 2021.
3. P. Mocanu, M. Gibescu, and W. Kling, “Deep Learning for Solar Power Forecasting—An Approach Using LSTM Networks,” IEEE PowerTech, pp. 1–6, 2017.
4. ESP32 Technical Reference Manual, Espressif Systems, 2022. [Online]. Available: https://www.espressif.com/en/products/hardware/esp32
5. Kaggle, “Solar Energy and Weather Dataset,” 2024. [Online]. Available: https://www.kaggle.com/
6. A. Mellit and S. Kalogirou, “Artificial Intelligence Techniques for Photovoltaic Applications: A Review,” Progress in Energy and Combustion Science, vol. 34, no. 5, pp. 574–632, 2008.
7. M. Bouzerdoum, A. Mellit, and A. Massi Pavan, “A Hybrid Model for Hourly Solar Radiation Forecasting Using Time-Series and Artificial Neural Networks,” Energy Conversion and Management, vol. 72, pp. 725–732, 2013.
8. J. Dudley and M. Infield, “The Effect of Weather Forecast Uncertainty on Solar Power Predictions,” IET Renewable Power Generation, vol. 7, no. 2, pp. 136–143, 2013.
9. F. Chollet, Deep Learning with Python, 2nd ed. New York: Manning Publications, 2021.
10. S. Ghosh et al., “IoT-Based Real-Time Solar Monitoring Using ESP32,” IEEE International Conference on Energy Systems, pp. 244–249, 2022.
2. Yadav and L. Kumar, “Solar Energy Forecasting Using Deep Learning Techniques: A Review,” Renewable and Sustainable Energy Reviews, vol. 135, pp. 110–119, 2021.
3. P. Mocanu, M. Gibescu, and W. Kling, “Deep Learning for Solar Power Forecasting—An Approach Using LSTM Networks,” IEEE PowerTech, pp. 1–6, 2017.
4. ESP32 Technical Reference Manual, Espressif Systems, 2022. [Online]. Available: https://www.espressif.com/en/products/hardware/esp32
5. Kaggle, “Solar Energy and Weather Dataset,” 2024. [Online]. Available: https://www.kaggle.com/
6. A. Mellit and S. Kalogirou, “Artificial Intelligence Techniques for Photovoltaic Applications: A Review,” Progress in Energy and Combustion Science, vol. 34, no. 5, pp. 574–632, 2008.
7. M. Bouzerdoum, A. Mellit, and A. Massi Pavan, “A Hybrid Model for Hourly Solar Radiation Forecasting Using Time-Series and Artificial Neural Networks,” Energy Conversion and Management, vol. 72, pp. 725–732, 2013.
8. J. Dudley and M. Infield, “The Effect of Weather Forecast Uncertainty on Solar Power Predictions,” IET Renewable Power Generation, vol. 7, no. 2, pp. 136–143, 2013.
9. F. Chollet, Deep Learning with Python, 2nd ed. New York: Manning Publications, 2021.
10. S. Ghosh et al., “IoT-Based Real-Time Solar Monitoring Using ESP32,” IEEE International Conference on Energy Systems, pp. 244–249, 2022.
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