南方电网技术2026，Vol.20Issue(8)：18-28,11.DOI:10.13648/j.cnki.issn1674-0629.2026.08.002
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
Power Prediction for Small-Scale Distributed Wind Turbine Clusters in Rural Areas Based on EIF-WaveNet with Spatiotemporal Feature Mining
摘要
Abstract
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.关键词
风功率预测/小型分布式风电/改进孤立森林算法/时空特征挖掘/WaveNetKey words
wind power prediction/small-scale distributed wind power/enhanced isolation forest algorithm/spatiotemporal feature mining/WaveNet分类
信息技术与安全科学引用本文复制引用
陈文颖,吴梦达..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[J].南方电网技术,2026,20(8):18-28,11.基金项目
中央高校基本科研基金(2023JC010) （2023JC010）
河北省省级科技计划资助项目(22567643H) （22567643H）
河北省高等学校科学技术研究项目(CXY2023001).Supported by the Fundamental Research Funds for Central Universities(2023JC010) （CXY2023001）
the Science and Technology Program of Hebei Province(22567643H) （22567643H）
the Science and Technology Research Project of Hebei Provincial Colleges and Universities(CXY2023001). （CXY2023001）
