Introduction:
Optimizing solar energy systems is essential for improving efficiency and reliability in the shift toward renewable energy. Artificial Intelligence (AI) plays a key role by enabling precise forecasting, adaptive control, and real-time optimization of photovoltaic (PV) technologies. This study examines the use of AI methods, such as neural networks, hybrid deep learning, and machine learning, to enhance solar irradiance prediction, panel orientation, and maximum power point tracking (MPPT).
Methods:
Multiple AI models were examined, including Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and hybrid CNN-LSTM frameworks. These models utilized datasets containing meteorological variables such as solar irradiance, temperature, humidity, and historical PV output. Optimization tasks incorporated Random Forest Regressors with Grid Search, ANFIS for MPPT, and reinforcement learning for tilt adjustment.
Results and Discussion:
The GRU model achieved a 23.3% lower RMSE compared to standard backpropagation neural networks and 11.9% improvement over traditional RNNs. A hybrid CNN-LSTM model demonstrated superior performance, reaching an RMSE of 0.0187 and an R² of 0.9915. In large-scale deployments like the Benban Solar Park, the model yielded a MAPE of 2.04% and R² of 0.99. For MPPT enhancement, AI models using ANFIS improved thermal and electrical efficiency by up to 12.3% under dynamic irradiance conditions. AI-based orientation control using reinforcement learning improved annual energy yield by 10–15%, while supervised ML methods increased energy output by an average of 3.98% over fixed-tilt systems.
Conclusion:
The integration of AI in solar energy systems yields significant quantitative benefits. Forecasting accuracy improves with hybrid models achieving <2% MAPE and >0.99 R², while orientation and MPPT optimizations result in 10–15% higher efficiency and up to 30% energy savings. These advancements highlight AI’s role in transforming solar technologies from static, reactive systems into dynamic, intelligent energy infrastructures.
