AI-Based Situational Awareness for New Energy Power Forecasting

With the increasing demand for sustainable energy and the strengthening international consensus on reducing dependence on fossil fuels, the development of new energy power generation, especially wind and solar power, has become a significant trend in scientific research and industrial development. The intermittent and unstable nature of these new energy sources poses challenges to the stable operation of power systems, making power generation forecasting a key to ensuring grid stability and the economic viability of new energy sources. This paper aims to explore a high-precision method for forecasting the power output of new energy sources using artificial intelligence and machine learning technologies to enhance the operational efficiency and reliability of power systems and to support the green transformation of the global energy structure. By integrating meteorological forecast data, historical power generation data, and real-time environmental monitoring data, this study also discusses an effective forecasting model that can provide precise power generation predictions for grid operators, thereby optimizing grid dispatch and energy configuration.