基于时空特征挖掘的EIF-WaveNet乡村小型分布式风电集群功率预测OA

To address the challenges of complex terrain,significant noise interference in wind speed and power data for small-scale distributed wind farms in rural areas,an improved WaveNet prediction algorithm integrating enhanced isolation forest data cleaning and spatiotemporal feature mining is proposed to enhance ultra-short-term power prediction accuracy.Firstly,an enhanced isolation forest(EIF)algorithm is employed for data cleaning.This algorithm incorporates dynamic quantile threshold optimization,path length window smoothing,and a multimodal correction strategy to effectively identify and eliminate anomalous data points.Subsequently,the maximal information coefficient(MIC)is utilized to screen highly correlated input features.A temporal attention mechanism combined with a Transformer module captures critical temporal dependencies,while a spatial attention mechanism integrated with a graph convolutional network(GCN)models spatial correlations among wind turbines for spatiotemporal feature fu-sion.Next,an ultra-short-term single-turbine power prediction model based on WaveNet architecture is constructed,which accurately captures the dynamic patterns of the fused feature sequence through its dilated convolution and skip connections,and outputs the single-turbine power prediction results.Finally,the total cluster power output is aggregated from single-turbine prediction results by considering the positional weights and spatial correlations of each turbine.A case study is conducted on three-small-turbine distributed wind farms with a single turbine capacity of 10 kW deployed in a certain rural area.The results demonstrate that the proposed method achieves high prediction accuracy.