With the acceleration of global energy transition, the proportion of wind and photovoltaic power generation in the power system continues to rise. However, the intermittency and volatility of renewable energy pose significant challenges to grid scheduling. Traditional meteorological forecasting methods have accuracy bottlenecks in predicting wind speed and solar radiation intensity, and the intervention of artificial intelligence technology is fundamentally changing this situation.
In recent years, deep learning models have made significant breakthroughs in the field of renewable energy prediction. A hybrid model based on Long Short Term Memory (LSTM) and Temporal Convolutional Network (TCN) can learn complex spatiotemporal dependencies from massive historical meteorological and power generation data. Research has shown that this type of model can reduce the root mean square error of wind power prediction by 30% -40%, which has significant advantages compared to traditional numerical weather forecasting methods.
In terms of photovoltaic power generation prediction, the combination of convolutional neural networks and satellite cloud image analysis has shown great potential. By analyzing the trajectory of cloud movement and the attenuation law of solar radiation, AI models can predict changes in photovoltaic output with over 90% accuracy 4-6 hours in advance. DeepMind, a subsidiary of Google, has collaborated with the UK's National Grid to optimize wind power forecasting using machine learning, resulting in a 20% increase in wind power utilization.
More importantly, AI prediction systems are evolving from single power plant prediction to regional level aggregated prediction. Graph neural networks can model the spatial correlation between multiple wind farms and photovoltaic power plants, helping dispatch centers to more accurately evaluate regional renewable energy output. The research of China Electric Power Research Institute shows that the regional prediction model based on graph neural network can reduce the overall prediction error by more than 15%.
At the practical deployment level, AI prediction systems are deeply integrated with digital twin technology. By constructing a digital twin of wind farms and photovoltaic power plants, operation and maintenance personnel can simulate the power generation performance under different meteorological conditions in a virtual environment and make scheduling decisions in advance. After deploying an AI prediction system in a certain offshore wind power project, State Power Investment Corporation increased annual power generation by about 5% and reduced operation and maintenance costs by 12%.
Looking ahead, with the enrichment of meteorological satellite data and the improvement of computing power, the application of AI in renewable energy forecasting will become more accurate and reliable. This not only helps to improve the level of clean energy consumption, but also is one of the key technological paths to achieve carbon peak and carbon neutrality goals.
[Reference source] The content of this article is comprehensively compiled from the public research reports of China Electric Power Research Institute, DeepMind's official technical blog, and industry information publicly released by State Power Investment Corporation.