Artificial intelligence enabled wind energy prediction and optimization management for renewable energy systems

In recent years, artificial intelligence-based computer simulation technology has made significant progress in the field of energy management, especially in data processing and intelligent decision-making, which show great potential. Optimizing energy allocation and utilization through intelligent algorithms significantly improves the efficiency and sustainability of energy management. This paper proposes an improved Long Short Term Memory Neural Network based on Quantum Particle Swarm Optimization Algorithm (QPSO-LSTM) wind power prediction method, which empowers the renewable energy system through artificial intelligence for prediction and management. At the same time, combining the prediction results with the energy storage system containing green hydrogen, a capacity allocation method is proposed to improve the utilization rate of renewable energy through energy management and help the effective consumption of wind power. This study effectively improves the accuracy and efficiency of wind power prediction, and at the same time provides new ideas for realizing the efficient use of renewable energy and the stable consumption of wind power. This is of great significance in promoting the intelligent transformation of energy systems, and is expected to play a key role in future energy management and lead a profound change in the field of renewable energy.