基于SSA-BiLSTM-AM的短期风电功率预测

Short-term wind power prediction based on SSA-BiLSTM-AM

风电功率的准确预测可以有效地减少并网波动。现有的风电功率预测模型存在输入特征过多、超参数选择难、时序过长易丢失重要信息等问题。为此,提出了一种麻雀搜索算法(SSA)优化双向长短时记忆(BiLSTM)加注意力机制(AM)的短期风电功率融合预测模型。首先,SSA对BiLSTM神经网络的节点数、学习率和训练次数等超参数进行寻优,确认最佳参数;然后,引入AM对BiLSTM的输入特征分配不同权重,强化关键特征;最后,应用所提模型对新疆210 MW风电场的风电功率进行预测,并与其他模型的预测结果对比。结果表明,SSA-BiLSTM-AM预测模型的均方根误差(RMSE)为5.411 4、平均绝对误差(MAE)为3.674 9,显著优于其他模型的预测精度,证明了SSA优化算法和AM能够有效提高风电机组的短期功率预测精度。

Accurate prediction of wind power can effectively reduce grid connection fluctuation.The existing wind power prediction models have some problems,such as too many input features,difficult to select super parameters,and easy to lose important information in long time series.To this end,a short-term wind power fusion prediction model was proposed based on the sparrow search algorithm(SSA)to optimize the bidirectional long short-term memory(BiLSTM)plus attention mechanism(AM).First,SSA optimizes the super parameters such as the number of nodes,learning rate and training times of BiLSTM neural network to confirm the best parameters.Then,AM was introduced to assign different weights to input features of BiLSTM to strengthen key features.Finally,the proposed model is used to predict the wind power of 210MW wind farm in Xinjiang Province,and the results are compared with those of other models.The results show that the root mean square error(RMSE)of the SSA-BiLSTM-AM prediction model is 5.4114,and the mean absolute error(MAE)is 3.6749.The prediction accuracy of SSA optimization algorithm and AM is significantly better than that of other models,which proves that SSA optimization algorithm and AM can effectively improve the short-term power prediction accuracy of wind turbines.