准确的风电功率预测对于电力系统安全稳定运行具有至关重要的意义.针对当前在集群预测中存在集群划分不合理以及在短期预测中精度难以得到有效提升的问题,文中提出了一种基于模糊 C 均值(Fuzzy C-means,FCM)和ITransformer-时间卷积网络(Temporal Convolutional Network,TCN)的短期风电集群功率预测方法.首先基于FCM聚类算法划分子集群,再利用ITransformer-TCN模型双重特征提取的优势对各子集群建模预测,最后将文中方法应用于中国吉林省某风电集群,与其他方法对比RMSE平均降低了 10.8%,验证了模型的有效性.
Short-term Power Prediction of Wind Power Cluster Based on FCM and ITransformer-TCN
Accurate wind power prediction is of great significance for the safe and stable operation of the power system.In view of the problems of unreasonable cluster division in cluster prediction and difficult to effectively improve the accuracy in short-term prediction,this paper proposes a short-term wind power cluster power prediction method based on Fuzzy C-means(FCM)and ITransformer-time convolutional network(Temporal Convolutional Network,TCN).First,divide subclusters based on FCM clustering algorithm,and then use the advantages of ITransformer-TCN model dual feature extraction to model each subcluster.Finally,this method was applied to a wind power cluster in Jilin Province,China,and the RMSE decreased by 10.8%on average compared with other methods,which verified the effectiveness of this paper.
wind power predictionFCMITransformer-TCNdual feature extractionshort-term cluster forecasting
NETL
NSTL
万方数据
