Medium and long-range meteorological forecasting technology for new energy: Challenges and opportunities

Meteorological forecasting serves as a fundamental technology for disaster prevention, mitigation, and addressing climate change. Due to the transient nature of weather conditions and the inherent uncertainty in forecasting, it plays a particularly critical role in the development and utilization of renewable energy sources such as wind and solar power. Medium- to long-term weather predictions significantly influence the planning, regulation, and layout of wind and solar energy systems. However, as the forecast range extends, accuracy declines rapidly, and uncertainty increases. Consequently, current medium and long-range meteorological forecasting is difficult to meet the demands of the renewable energy sector. Addressing the challenges of high uncertainty and low resolution in medium and long-range weather forecasting represents a major scientific issue at the intersection of meteorology and energy science. In recent years, advancements in deep learning and artificial intelligence (AI) have brought transformative changes to prediction technologies. By innovating assimilation and probabilistic forecasting theories and methods based on numerical models and AI generative models, it is possible to achieve medium and long-range forecasts of wind and solar resources with high spatiotemporal resolution, precision, and cost-effectiveness. This breakthrough could overcome the challenges posed by the variability of renewable resources and forecasting uncertainties, which currently hinder the integration of high proportions of wind and solar energy into power systems. Ultimately, such advancements will provide scientific and technological support for the stable operation and sustainable development of renewable energy power generation.