Power Prediction

Power prediction research aims to accurately forecast energy consumption or generation across diverse applications, from microprocessors to renewable energy sources. Current efforts focus on developing sophisticated models, including machine learning techniques like graph neural networks and hybrid approaches combining analytical and machine learning methods, to improve prediction accuracy and efficiency, particularly in scenarios with limited data or complex spatio-temporal dependencies. These advancements are crucial for optimizing energy systems, improving the efficiency of electronic devices, and enhancing the reliability of power grids by enabling better resource allocation and management.