Two-Phase Deep Learning Model For Short-Term Wind Direction Forecasting
RENEWABLE ENERGY(2021)
摘要
Accurate and reliable wind direction prediction is important for improving wind power conversion efficiency and operation safety. In this paper, a two-phase deep learning model is proposed and constructed for high-performance short-term wind direction forecasting. In the first phase, a hybrid data processing strategy, including data reconstruction, outlier deletion, dimension reduction, and sequence decomposition, is proposed to extract the most meaningful information from practical data. Then, in the second phase, a robust echo state network is developed for wind direction forecasting. In addition, its hyper-parameters are optimized using an improved flower pollination algorithm (IFPA) to achieve high efficiency. Experiments conducted on data from real wind farms validate the proposed hybrid data processing method. Finally, comparisons with benchmark prediction models show that the proposed network achieves superior performance.
更多查看译文
关键词
Wind direction prediction, Two-phase prediction model, Improved flower pollination algorithm, Echo state network
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要