基于变分模态分解与双向长短期记忆神经网络的超短期风速预测  

Ultra-Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Bidirectional Long Short-Term Memory Neural Network

作　　者：刘宇 张雨飞[1] 陈尚巧 Liu Yu

机构地区：[1]东南大学能源与环境学院,江苏南京210096

出　　处：《工业控制计算机》2020年第9期54-57,共4页Industrial Control Computer

摘　　要：风速预测对于风电机组稳定运行,提高电力系统稳定性有着重要的意义。针对风速序列具有随机性、波动性大的特点,提出一种基于分解组合算法的风速超短期预测模型。采用变分模态分解算法将风速原始序列分解为若干平稳子序列,再利用优化的双向长短期记忆神经网络对子序列建立预测模型,最终组合得到预测结果。通过实例分析并与其他模型对比表明,该模型能够有效提高风速超短期预测精度。Wind speed prediction is of great significance for the stable operation of wind turbines and the improvement of power system stability.In view of the characteristics of randomness and large fluctuation of wind speed sequence,this paper proposes an ultra-short-term wind speed prediction model based on decomposition and combination.The variational mode decomposition algorithm is used to decompose the original wind speed sequence into several stationary subsequences,and then the optimized bidirectional long short-term memory neural network is used to establish prediction models for the subsequences.Finally,the prediction results are obtained through combination.The comparison with other models shows that the proposed model can effectively improve the accuracy of ultra-short-term wind speed prediction.

关 键 词：风速预测 变分模态分解 双向长短期记忆神经网络 

分 类 号：TM614[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]