基于多任务学习的风速实时预测方法

准确的秒级风速实时预测能够提高风电机组的运行状况和控制品质,为电网做出最优调度决策提供辅助信息.目前风速实时预测时间分辨率通常为分钟级,且在小数据集的情况下模型泛化能力弱.文章以时间分辨率为5s的风速序列为研究对象,提出了基于多任务学习的风速实时预测方法.该方法结合了变分模态分解方法和长短期记忆神经网络.首先,通过变分...

Wind speed real-time prediction method based on multi-task learning

Accurate real-time prediction of second-level wind speed can improve the operating conditions and control quality of wind turbines and provide auxiliary information for the power grid to make optimal dispatch decisions.At present,the time resolution of real-time wind speed prediction is usually on the minute level,and the model's generalization ability is weak in the case of small data sets.Therefore,this paper takes the wind speed sequence with a time resolution of 5 s as the research object and proposes a real-time prediction method of wind speed based on multi-task learning,which combines the variational modal decomposition method and long-short term memory neural network.First,the wind speed sequence is decomposed into a series of signals through the variational mode decomposition method.Then,a shared layer for multi-task learning is established,and the long-short term memory neural network is used to extract the shared parameters of each decomposition signal,which digs and shares information between sub-sequence prediction tasks.Finally,the specific task layer of multi-task learning is established,which uses multiple LSTMs to predict the decomposed wind speed sub-sequences in parallel,and the multiple prediction results are superimposed to obtain real-time wind speed prediction results.The practical example results show that the overall mean value of the root mean square error in the 10-step and 5-step prediction of the proposed multi-task learning model is 0.80 m/s and 0.71 m/s.Compared with the single-task model predictions after variational modal decomposition and without variational modal decomposition,the root mean square error of the proposed model is reduced by 35.5%and 39.8%in the 10-step prediction and is reduced by 24.5%and 45.8%in the 5-step prediction.