In a significant advancement for the wind energy sector, researchers have unveiled an innovative method for ultra-short-term wind speed prediction that could revolutionize how wind farms operate. Led by SHAO Yixiang from the NARI Group Corporation in Nanjing, this research focuses on improving the accuracy of wind speed forecasts, which are crucial for optimizing the performance of wind turbines.
The new approach leverages a combination of two powerful neural network models: the Backpropagation (BP) neural network and the Long Short-Term Memory (LSTM) neural network. Each of these models is adept at handling time series data and possesses robust capabilities for nonlinear learning. By integrating these two models through a weighted combination, the researchers aim to mitigate the significant errors that can arise when relying on a single neural network.
“By enhancing the prediction accuracy, we not only improve the operational efficiency of wind farms but also contribute to better integration of renewable energy into the grid,” SHAO stated. This is particularly pertinent as energy providers increasingly seek reliable forecasting methods to manage the variability inherent in wind power generation.
To further refine the predictive capabilities of their model, the team employed a differential evolution (DE) algorithm to optimize the combination weights of the neural networks. This optimization step is crucial, as it fine-tunes the model to adapt to changing atmospheric conditions and improves its responsiveness to sudden fluctuations in wind speed.
The research was tested on a wind farm, where the results demonstrated a marked improvement in prediction accuracy compared to traditional single neural network models and even equal weight combined networks. This advancement has profound implications for the commercial viability of wind energy, as enhanced forecasting can lead to more effective turbine control, reduced operational costs, and increased energy output.
As renewable energy sources continue to gain traction globally, the ability to predict wind speed accurately becomes even more critical. The findings from this study not only promise to enhance the efficiency of existing wind farms but also pave the way for future developments in energy management systems, potentially leading to a more stable and reliable energy grid.
This groundbreaking research was published in ‘发电技术’, which translates to ‘Power Generation Technology’, highlighting its relevance to the ongoing evolution in energy technology. For more information about the lead author’s work, visit NARI Group Corporation.
