针对乡村地区小型分布式风电面临的地形复杂、风速及功率数据噪声干扰显著等问题，为提升其超短期功率预测精度，提出了一种融合改进孤立森林数据清洗与时空特征挖掘的WaveNet预测算法。首先，采用改进孤立森林算法（enhanced isolation forest，EIF）进行数据清洗，该算法集成了动态分位数阈值优化、路径长度窗口平滑及多模态校正策略，有效识别并剔除异常数据点。随后，利用最大互信息系数（maximal information coefficient，MIC）筛选高相关特征输入并引入时间注意力机制结合Transformer模块捕获关键时间依赖性，同时应用空间注意力机制与图卷积网络（graph convolutional network，GCN）建模风机间的空间关联性，进行时空特征融合。接着，构建基于WaveNet架构的超短期单机功率预测模型，以其膨胀卷积和跳跃连接精准捕捉融合特征序列的动态模式并输出单机功率预测结果。最后，考虑各机组的位置权重与空间相关性，聚合单机预测结果输出集群总功率。以某村庄部署的3台单机容量为10 kW的小型分布式风电场为实例，验证结果表明所提方法具有较高预测精度。

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 fusion. 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.