Modeling of intelligent controllers for solar photovoltaic system
Therefore, our study aimed to conduct a comprehensive comparative analysis of these intelligent controllers by applying real environment and varying weather scenarios and
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.
Termed Intelligent Solar Energy Management Technology (ISEMS), this system comprises three key components: Forecast-Based Intelligent Energy Management System: Utilizes predictive analytics to enhance energy availability forecasting, reducing uncertainty in solar power generation.
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.
These include applications such as remote monitoring and control, predictive maintenance, energy optimization, and other functionalities designed to maximize solar energy generation, enhance system reliability, and ensure efficient energy management.
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