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، تابستان 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
آمار
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