Abstract
With the continuous growth of global demand for renewable energy, efficiently addressing the challenges of utilizing variable energy sources such as wind and solar power has become a key research focus in the energy sector. This paper systematically reviews the progress in the application of big data and artificial intelligence (AI) in renewable energy generation forecasting and optimal scheduling. In generation forecasting, it provides a detailed analysis of the variability characteristics of wind and solar power, highlights the critical role of big data in data acquisition and processing, and discusses the advantages of machine learning and deep learning algorithms in improving forecasting accuracy. This paper summarizes the latest research advancements in dynamic scheduling strategies, AI-driven real-time optimization systems, and intelligent energy storage management, emphasizing their effectiveness in enhancing energy system flexibility and stability. Specifically, it explores the design and validation of simulation experiment frameworks, covering mathematical modeling, scenario setup, optimization algorithm evaluation, and comparative analysis. Finally, the paper outlines future research directions, proposing potential pathways to improve simulation methodologies and discussing cutting-edge trends in AI-driven smart renewable energy systems. This paper aims to provide comprehensive insights for researchers and practitioners, facilitating the efficient utilization of renewable energy and the intelligent transformation of energy systems.
