In a groundbreaking development that could reshape how provincial power grids manage wind energy, researchers have introduced a novel approach to predict wind power generation intervals with unprecedented accuracy. Led by Gang Li from the Institute of Hydropower and Hydroinformatics at Dalian University of Technology, this innovative model addresses the long-standing challenges of predicting wind power fluctuations, which are crucial for the stable and economic operation of power systems.
Wind power, a cornerstone of renewable energy, has seen remarkable growth globally, with China’s wind turbine capacity reaching 441 GW by the end of 2023. However, the inherent intermittency of wind power poses significant challenges for power grid operators. “The key to integrating more wind power into the grid lies in accurately predicting its output,” Li explained. “Our model aims to provide a more comprehensive and reliable tool for system operators to manage this variability effectively.”
The research, published in the journal Energies, introduces a lightweight model that directly generates probabilistic predictions in the form of intervals. This is achieved through a fusion of geographic and meteorological information, creating feature images that enhance the model’s attention to spatial and temporal features. Unlike traditional methods that require extensive data processing, this approach streamlines the input features, making the model more efficient and easier to implement.
One of the standout features of this model is its parallel prediction network architecture, which combines a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). This design allows the model to effectively capture both the spatial meteorological distribution characteristics of regional power stations and the temporal features of historical power generation. “By integrating these elements, we can provide a more accurate and reliable prediction of wind power output, which is essential for maintaining grid stability,” Li noted.
The model also introduces an efficient channel attention (ECA) mechanism and an improved quantile regression-based loss function. These enhancements enable the model to directly generate prediction intervals, improving both the accuracy and sharpness of the forecasts. The case study, conducted in Guizhou province, Southwest China, demonstrated that the model outperforms benchmark models by at least 12.3% in interval prediction performance and reduces the deterministic prediction root mean square error (RMSE) by at least 19.4%.
The implications of this research are far-reaching. For provincial power grids, this model could significantly enhance the integration of wind power, reducing the need for costly spinning reserves and improving economic dispatch. “This model has the potential to revolutionize how we manage wind power in the grid,” Li said. “It could lead to more efficient use of renewable energy resources, reducing reliance on fossil fuels and lowering carbon emissions.”
As the energy sector continues to evolve, the ability to accurately predict wind power output will be crucial for achieving a more sustainable and resilient energy system. This research not only addresses the current challenges but also paves the way for future developments in renewable energy forecasting. With its potential for scalability and adaptability, this model could be expanded to other renewable energy applications, further enhancing the integration of clean energy into the power grid. The model’s ability to handle large datasets and complex features makes it a valuable tool for energy sector professionals, offering a more reliable and efficient way to manage wind power generation.
