风速变化的间歇性和波动性给风功率的精准预测带来极大挑战,充分挖掘风电功率与风速等关键因素的内在规律是提高风电功率预测精度的有效途径.提出一种结合时间模式注意力(time pattern attention,TPA)机制的多层堆叠双向长短期记忆网络的超短期风电功率预测方法.首先,利用基于密度的含噪声空间聚类方法(den-sity based spatial clustering with noise,DBSCAN)和线性回归算法进行风功率数据集的异常值检测,利用k最邻近(k-nearest neighbor,KNN)插值法重构异常点数据;其次,综合考虑风电功率与各气象特征的内在关联性,在MBLSTM网络中引入TPA机制合理分配时间步长权重,捕捉风电功率时间序列潜在逻辑规律;最后,利用实验仿真数据进行分析验证本文方法的有效性,该方法能够充分挖掘风功率与风速影响因素的关系,从而提高其预测精度.
Ultra-short-term wind power prediction based on TPA-MBLSTM model
The intermittency and volatility of wind speed changes pose great challenges to the accurate prediction of wind power.Fully exploring the inherent laws of key factors such as wind power and wind speed is an effective way to improve the accuracy of wind power prediction.A method for ultra-short-term wind power prediction is proposed,which incorporates a temporal pattern attention(TPA)mechanism into a multi-layer stacked bidirectional long short-term memory network.Firstly,outlier detection for the wind power dataset is performed using a density-based noisy spatial clustering method(DBSCAN)and a linear regression algorithm,followed by data reconstruction of outlier points using k-nearest neighbor(KNN)interpolation.Next,the intrinsic correlations between wind power and various meteorological features are comprehensively considered,and the TPA mechanism is introduced into the MBLSTM network to properly allocate time step weights,capturing the underlying logical patterns of the wind power time series.Finally,the effectiveness of the proposed method is verified through experimental simulation data analysis.Results show that this method can fully explore the relationship between wind power and wind speed influencing factors,thereby improving its prediction accuracy.
wind power predictionTPA mechanismMBLSTManomaly data detectionDBSCANlinear regression
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