Advanced AI Techniques for Grid-Connected Renewable Energy Systems

This research paper explores the ability to integrate innovative AI methods to optimise grid-integrated renewable energy systems. Consequently, this study assesses the benefits of energy demand forecasting, fault detection, and real-time energy management using Long Short Term Memory LSTM networks, SVM and RL models focusing on three broad areas. The LSTM model performed well in the energy demand forecast with low MAE and RMSE than other models while the SVM model performed well in fault detection with a 92% accuracy. LBC revealed that the RL-based system contributed to the reduction of energy cost while regulating the grid stability through optimizing energy in different demand situations. This paper also presents several key issues associated with data quality, model generalization, computational overhead, and interfaces to extant systems. This emphasizes the gaps on the effectiveness of data acquisition techniques, flexibility of AI mod- els and robustness of the algorithms to perform within resource constraints. There are some issues arise while integrating new AI technologies with existing traditional systems which must be considered and standardized. Possible future works are provided to overcome these issues, which include the refinement of pre processing techniques, flexi- bility of models, and solution extensibility. The studies highlight multiple possibilities of using AI in the enhancement of energy systems and their managing factor, decrease of operation costs, as well as increase of the robustness of grids, opening the way to further development of the more effective and stable energy managing solutions.