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Al-Maqsoosi, Ahmed, Al-Budairi, Hassan, Akgündoğdu, Abdurrahim, Al Yodaoi, Hanan. (1405). Optimising Photovoltaic Power Output Using Hybrid Deep Reinforcement Learning and Real-Time Environmental Adaptation. , 11(2), 2923-2933. doi: 10.22059/jser.2026.410130.1708
Ahmed Al-Maqsoosi; Hassan Al-Budairi; Abdurrahim Akgündoğdu; Hanan Al Yodaoi. "Optimising Photovoltaic Power Output Using Hybrid Deep Reinforcement Learning and Real-Time Environmental Adaptation". , 11, 2, 1405, 2923-2933. doi: 10.22059/jser.2026.410130.1708
Al-Maqsoosi, Ahmed, Al-Budairi, Hassan, Akgündoğdu, Abdurrahim, Al Yodaoi, Hanan. (1405). 'Optimising Photovoltaic Power Output Using Hybrid Deep Reinforcement Learning and Real-Time Environmental Adaptation', , 11(2), pp. 2923-2933. doi: 10.22059/jser.2026.410130.1708
Al-Maqsoosi, Ahmed, Al-Budairi, Hassan, Akgündoğdu, Abdurrahim, Al Yodaoi, Hanan. Optimising Photovoltaic Power Output Using Hybrid Deep Reinforcement Learning and Real-Time Environmental Adaptation. , 1405; 11(2): 2923-2933. doi: 10.22059/jser.2026.410130.1708

Optimising Photovoltaic Power Output Using Hybrid Deep Reinforcement Learning and Real-Time Environmental Adaptation

Journal of Solar Energy Research
دوره 11، شماره 2، تیر 2026، صفحه 2923-2933    اصل مقاله (699.76 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22059/jser.2026.410130.1708
نویسندگان
Ahmed Al-Maqsoosi1؛ Hassan Al-Budairi* 1؛ Abdurrahim Akgündoğdu2؛ Hanan Al Yodaoi3
1Center of Computer and Informatics, Wasit University, Wasit, Iraq
2Department of Electrical and Electronics Engineering at the Faculty of Engineering, Istanbul University-Cerrahpaşa, Turkey
3Ministry of Trade, Iraq
چکیده
Development of Artificial Intelligence (AI) systems has transformed the management of renewable energy by resolving long-standing challenges in efficiency, resilience, and responsiveness. Photovoltaic (PV) power generation, highly sensitive to environmental fluctuations, can particularly benefit from AI-based control strategies. This paper proposes a hybrid AI architecture combining model-free Deep Reinforcement Learning (DRL) using Deep Q-Networks (DQN) with Long Short-Term Memory (LSTM) networks to enhance Maximum Power Point Tracking (MPPT) under dynamic conditions including rapid irradiance, temperature, humidity variations, and partial shading. The system employs real-time environmental sensor inputs, namely solar irradiance, ambient and module temperature, relative humidity, and shading indices, as the DQN state. The LSTM processes historical sequences to predict near-future power trends and enable proactive MPPT decisions. Implementation on a low-cost, energy-efficient Raspberry Pi edge computing platform enables decentralised, low-latency control without cloud dependence, suitable for remote or off-grid applications. A 180-day field validation on a rooftop 5.4 kW PV array demonstrated a 37% reduction in convergence time compared with Perturb and Observe (P&O) and 28% relative to Fuzzy Logic MPPT. The system achieved a 12.4% average increase in daily energy yield, rising to 18.7% under sporadic cloud cover and partial shading in real-world operational scenarios contexts.
کلیدواژه‌ها
Photovoltaic power generation؛ Deep reinforcement learning؛ Renewable energy؛ MPPT؛ Iraq
مراجع
  1. Salman, D., Elmi, Y. K., Isak, A. M., & Sheikh-Muse, A. (2025). Evaluation of MPPT algorithms for solar PV systems with machine learning and metaheuristic techniques. Mathematical Modelling of Engineering Problems, 12(1), 115–124. https://doi.org/10.18280/mmep.120113.
  2. Naser, A. T., Aziz, N. F. A., Mohammed, K. K., Kamil, K. binti, & Mekhilef, S. (2025). Performance assessment of meta-heuristic MPPT strategies for solar panels under complex partial shading conditions and load variation. Global Energy Interconnection, 8(4), 554–571. https://doi.org/10.1016/j.gloei.2025.03.004.
  3. Chandola, J., Pundir, S., Sharma, A., Song, Y., Fekete, G., & Singh, T. (2026). Recent advances in MPPT techniques for photovoltaic systems: A review of classical (P&O, IC), intelligent (ANN), optimization (PSO) and hybrid (ANN–PSO) methods. Results in Engineering, 29, 109395. https://doi.org/10.1016/j.rineng.2026.109395.
  4. Lyu, G., Soomar, A. M., Shah, S. H. H., Shaikh, S., & Musznicki, P. (2025). Maximum power point tracking strategies for solar PV systems: A review of current methods and future innovations. Results in Engineering, 28, 107227. https://doi.org/10.1016/j.rineng.2025.107227
  5. Yousaf, M. Z., Koondhar, M. A., Zaki, Z. A., Ahmed, E. M., Alaas, Z. M., Mahariq, I., & Guerrero, J. M. (2025). Improved MPPT of solar PV systems under different environmental conditions utilizing a novel hybrid PSO. Renewable Energy, 244, 122709. https://doi.org/10.1016/j.renene.2025.122709.
  6. Endiz, M. S., Gökkuş, G., Coşgun, A. E., & Demir, H. (2025). A review of traditional and advanced MPPT approaches for PV systems under uniformly insolation and partially shaded conditions. Applied Sciences, 15(3), 1031. https://doi.org/10.3390/app15031031.
  7. Esram, T., & Chapman, P. L. (2007). Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on Energy Conversion, 22(2), 439–449. https://doi.org/10.1109/TEC.2006.874230.
  8. Ramli, A. M., Twaha, S., Ishaque, K., & Al-Turki, Y. A. (2017). A review on maximum power point tracking for photovoltaic systems with and without shading conditions. Renewable and Sustainable Energy Reviews, 67, 144–159. https://doi.org/10.1016/j.rser.2016.09.013.
  9. Mazaheri Salehi, P., & Solyali, D. (2018). A review on maximum power point tracker methods and their applications. Journal of Solar Energy Research, 3(2), 123–133.
  10. Fapia, C. B. N., Kamtaa, M., & Wirab, P. (2019). A comprehensive assessment of MPPT algorithms to optimal power extraction of a PV panel. Journal of Solar Energy Research, 4(3), 172–179. https://doi.org/10.22059/jser.2019.287029.1126.
  11. Malobe, P. A., Djondine, P., & Ntsama Eloundou, P. (2023). Meta-heuristic optimisation of the neuro-fuzzy MPPT controller for PV systems under partial shading conditions. Journal of Solar Energy Research, 8(1), 1222–1234. https://doi.org/10.22059/jser.2022.349012.1255.
  12. Hussein, K. H., Muta, I., Hoshino, T., & Osakada, M. (1995). Maximum photovoltaic power tracking: An algorithm for rapidly changing atmospheric conditions. IEE Proceedings - Generation, Transmission and Distribution, 142(1), 59–64. https://doi.org/10.1049/ip-gtd:19951577.
  13. Samangkool, K., & Premrudeepreechacharn, S. (2005). Maximum power point tracking using neural networks for grid-connected photovoltaic system. 2005 International Conference on Future Power Systems, 1–4. https://doi.org/10.1109/FPS.2005.204215.
  14. Xie, F., Guan, X., Peng, X., Zeng, Y., Wang, Z., & Qin, T. (2024). Application of Fuzzy control and Neural Network control in the commercial development of sustainable energy system. Sustainability, 16(9), 3823. https://doi.org/10.3390/su16093823.
  15. Phan, B. C., Lai, Y.-C., & Lin, C.-L. (2020). A deep reinforcement learning-based MPPT control for PV systems under partial shading condition. Sensors, 20(11), Article 3039. https://doi.org/10.3390/s20113039.
  16. Madjoudj, N. , Hafdaoui, H. , Belhaouas, N. , Mehareb, F. and Assem, H. (2025). Detection of maximum power degradation in photovoltaic modules using support vector machines. Journal of Solar Energy Research, 10(4), 2633-2644. https://doi.org/10.22059/jser.2025.405467.1664.
  17. Rajamallaiah, A., Naresh, S. V. K., Yarlagadda, R., Manmadharao, S. (2025). Deep reinforcement learning for power converter control: A comprehensive review of applications and challenges. IEEE Open Journal of Power Electronics. 6. 1-35. https://doi.org/10.1109/OJPEL.2025.3619673.
  18. Mnih, V., Kavukcuoglu, K., Silver, D., & Rusu, A. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533. https://doi.org/10.1038/nature14236.
  19. Wadehra, A., Bhalla, S., Jaiswal, V., & Rana, K.P.S. (2024). A deep recurrent reinforcement learning approach for enhanced MPPT in PV systems. Applied Soft Computing, 162. https://doi.org/10.1016/j.asoc.2024.111728.
  20. Bollipo, R., Mikkili, S., & Bonthagorla, P. (2022). Critical review on PV MPPT techniques: Classical, intelligent and optimisation. Renewable Power Generation, 14(9), 1752–1416. https://doi.org/10.1049/iet-rpg.2019.1163.
  21. Sharma, A. K., Pachauri, R. K., Choudhury, S., & Minai, A. F. (2023). Role of metaheuristic approaches for implementation of integrated MPPT-PV systems: A comprehensive study. Mathematics, 11(2), 269. https://doi.org/10.3390/math11020269.
  22. Lei, X. (2025). Intelligent MPPT framework with reinforcement learning and dynamic search region optimization for photovoltaic systems under variable environmental conditions. Progress in Electromagnetics Research B, 112, 89–103. https://doi.org/10.2528/PIERB25041101.
  23. Djaafari, A., Ibrahim, A., Bailek, N., & Bouchouicha, K. (2022). Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions. Energy Reports, 8, 15548–15562. https://doi.org/10.1016/j.egyr.2022.10.402.
  24. Senthilvel, A., Vijeyakumar, K. N., & Vinothkumar, B. (2020). FPGA based implementation of MPPT algorithms for photovoltaic system under partial shading conditions. Microprocessors and Microsystems, 77, 103011. https://doi.org/10.1016/j.micpro.2020.103011.
  25. Elsafi, A., Almohammedi, A. A., Balfaqih, M., & Balfagih Z. (2025). Comparative analysis of maximum power point tracking methods for power optimization in grid tied photovoltaic solar systems. Discover Applied Sciences, 7(9), 976. https://doi.org/10.1007/s42452-025-07606-w.
  26. El-Telbany, M., Yousef, A., & Zekry, A. (2015). Intelligent techniques for MPPT control in photovoltaic systems: A comprehensive review. https://doi.org/10.1109/ICAIET.2014.13.
  27. Ukoba, K., Olatunji, K. O., Adeoye, E., & Jen, T. (2024). Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Energy & Environment, 35(7), 3833–3879. https://doi.org/10.1177/0958305X241256293.
  28. Kumar, P., & Ragesh, V. (2023). Performance analysis of maximum power point tracking of PV systems using artificial neural networks and support vector machines. International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, 511–515. https://doi.org/10.1109/ICCIKE58312.2023.10131783.
  29. Fang, H., Zhang, H., Wen, S., & Li, Z. (2025). A reinforcement learning based real-time energy management method for mobile microgrid considering photovoltaic uncertainty. International Journal of Electrical Power & Energy Systems, 170, 110844. https://doi.org/10.1016/j.ijepes.2025.110844.
  30. Polymeropoulos, I., Bezyrgiannidis, S., Vrochidou, E., & Papakostas, G. A. (2024). Enhancing solar plant efficiency: A review of vision-based monitoring and fault detection techniques. Technologies, 12(10), 175. https://doi.org/10.3390/technologies12100175.
  31. Abisoye, B. O., Sun, Y., & Zenghui, W. (2024). A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights. Renewable Energy Focus, 48. https://doi.org/10.1016/j.ref.2023.100529.
  32. Kirkpatrick, J., Pascanu, R., Rabinowitz, N., & Veness, J. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13), 3521–3526. 10.1073/pnas.1611835114.
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