Recent Papers

The paper presents an AI-based solar energy optimization system designed to improve the efficiency, reliability, and scalability of solar power generation. The system integrates advanced machine learning techniques, including reinforcement learning (RL) for dynamic energy distribution, long short-term memory (LSTM) networks for solar power forecasting, and predictive maintenance models using support vector machines (SVM) and random forests for fault detection. The proposed approach is tested through both simulations and real-world experiments on a 50 kW solar farm, equipped with IoT-based sensors and cloud computing infrastructure. Key performance metrics, such as prediction accuracy, energy utilization, fault detection accuracy, and computational efficiency, are evaluated and compared with conventional optimization methods. The results show that the AI-driven system outperforms traditional methods in several aspects, including a 15-20% improvement in energy utilization, an 85% fault detection rate, and a 20% faster computational performance. These findings demonstrate the potential of AI in enhancing the optimization of solar energy systems, paving the way for smarter, more efficient renewable energy solutions. Further research will focus on expanding the system to integrate other renewable energy sources and explore decentralized AI models for greater scalability.

Copyright © 2025 Nikin Tharan. This is an open access article distributed under the Creative Commons Attribution License.