2025 , Volume 30, № 3, p.78-96

Solar power integration into modern energy systems relies on accurate and adaptive control models, which are variable and uncertain. Traditional models such as ecurrent neural networkd and long short-term memory (LSTM) networks have problems with scalability, prediction accuracy, and computational efficiency. We present an enhanced control framework that combines three promising methods: Transformer-based models with self-attention mechanisms to understand predispositions, proximal policy optimisation for adaptive learning, and BNNs to update the probabilistic forecast. Transformer-based models use self-attention techniques to capture long-range relationships in time series, improving prediction accuracy and scalability. This model parallelizes inputs to reduce training time, making it better for large-scale applications. Empirically, this approach achieves 95 % prediction accuracy on validation data and 20 % faster training time than LSTM models. This works well with proximal policy optimization, an advanced reinforcement learning technique that allows real-time control policy adjustments based on solar power system data. Since maximum power point tracking and proportional-integral-derivative controllers optimize control actions, this actor-critic structure ensures learning stability and dependability. The model includes VARMAX for pre-emption. The system improved global efficiency by 15 % and updated the control strategy in 100 ms. BNNs are neural networks with uncertainty quantification for solar power generation variability management. BNNs provide probabilistic mean and variance predictions with uncertainty intervals, improving resilience and decision-making. The article found that the model covered the uncertainty interval at 90 %, while the typical neural network had a 10 % reduction in breadth. Using such methods, a solar power system control model would be robust, adaptive, and efficient. This comprehensive solution improves real-time accuracy, adaptiveness, and uncertainty handling, enhancing connected-solar load deployment performance and dependability.