With the acceleration of global energy transition, the proportion of new energy such as wind power and photovoltaics in the power system continues to rise. However, new energy generation has natural intermittency and volatility - the wind is sometimes strong and sometimes weak, and sunlight is influenced by clouds and day and night. How to accurately predict the power generation of new energy has become the core challenge to ensure the stable operation of the power grid.
Although traditional physics model-based prediction methods, such as numerical weather forecasting (NWP), have a solid foundation, they have limitations in dealing with complex nonlinear relationships. The introduction of AI technology is fundamentally changing this situation.
Deep learning models, especially LSTM and Transformer architectures, can automatically extract features from massive historical meteorological and power generation data. Compared to traditional methods, AI models have reduced errors by about 15-30% in photovoltaic power generation prediction, and have also shown significant improvements in wind power prediction. The hybrid model (CNN+GRU) deployed by a European power grid operator recently reduced the average absolute error from 8.2% to 5.1% in power generation forecasting.
The key technological path includes three aspects: firstly, multimodal data fusion - integrating satellite cloud images, radar data, meteorological station measured data, and numerical weather forecast results into inputs, and AI models automatically learn the weight allocation of different data sources. Secondly, time series modeling optimization - utilizing attention mechanisms to capture long-range dependencies between meteorological elements and power generation, particularly suitable for strong correlation scenarios in photovoltaic power generation where the previous day is at the same time period. Thirdly, ensemble learning strategy - weighting and combining the prediction results of multiple different architecture models to further reduce single model bias.
In practical applications, AI power generation prediction has helped multiple new energy power plants reduce backup capacity configuration, directly reducing operating costs. After the deployment of an AI prediction system in a large photovoltaic base in China, the abandonment rate decreased from 12% to below 4%, and the annual increase in power generation revenue exceeded 10 million yuan.
Looking into the future, with the improvement of the resolution of meteorological satellite data and the maturity of edge computing technology, the AI prediction model will further deepen in the two directions of "ultra short term prediction" (minute level) and "extreme weather prediction". This will shift the power grid dispatch from "passive response" to "active prediction", providing solid technical support for the integration of high proportion renewable energy into the grid. The content of this article is comprehensively compiled from technical reports and industry information publicly released by IEEE Transactions on Power Systems and China Electric Power Research Institute.