AI Photovoltaic Energy Storage Power

Artificial intelligence-based smart grid technology and hybrid energy storage systems must be integrated to deliver an efficient, secure, and decentralized energy supply in contemporary solar power grids. Centralized inefficiencies, transmission losses, and lack of real-time optimization are features of conventional energy grids.

This study proposes a hybrid solar power system aided by AI that incorporates high-performance solar tracking, intelligent PV technologies, and blockchain-integrated smart grid integration for an efficient and scalable scheme for clean power production.

The AI-based hybrid solar energy system integrates multiple integrated modules to enhance the decentralized energy management, energy conversion, and solar tracking. The system integrates CNN-LSTM solar irradiance forecasting, RL-based dual-axis tracking, and Edge AI for real-time applications to facilitate adaptive and efficient solar tracking.

The characterization of AI-based adaptive solar tracking systems centers on evaluating photovoltaic efficiency improvements, thermal stability, and energy distribution under practical circumstances. These findings confirm the efficiency of AI-based tracking in optimizing the usage of solar energy and providing long-term operational stability.

The AI-hybrid solar energy system presented here optimizes solar energy conversion, storage, and grid integration by integrating CNN-LSTM forecasting, reinforcement learning dual-axis tracking, and Edge AI real-time control. Unlike conventional systems, it can automatically adapt to climatic variations to optimize irradiance capture.

This study proposes hybrid AI solar tracking based on CNNs, LSTMs, and RL to enhance the prediction and autonomous control of solar irradiance with dual-axis tracking. Unlike traditional methods, the system can learn by itself and adapt in real time; therefore, it can collect the maximum solar energy, even if the weather changes.