节能2025，Vol.44Issue(1)：21-25,5.DOI:10.3969/j.issn.1004-7948.2025.01.006
基于持续学习的SGMD-TSNE-TCNre风电功率长期预测
SGMD-TSNE-TCNre long-term prediction of wind power based on continuous learning
杨晓华 1代盛国 1李家浩 1李佳2
作者信息
- 1. 南方电网云南电网有限责任公司,云南 昆明 650000
- 2. 西安许继电力电子技术有限公司,陕西西安 710000||昆明理工大学,云南 昆明 650000
- 折叠
摘要
Abstract
For wind power prediction,a time convolutional network(TCNre)algorithm based on continuous learning is proposed,and combined with symplectic geometric mode decomposition(SGMD),t-distribution and random neighbor embedding(TSNE)data processing methods,the prediction model SGMD-TSNE-TCNre is constructed.In order to verify the effectiveness of the continuous learning method,a continuous learning model(TCNre)based on parameter freezing is built and compared with the TCN model.On this basis,considering that the power of wind turbines is affected by many complex factors,SGMD model is introduced to reduce the non-stationarity caused by environmental factors,and TSNE is used to reduce the input dimension of the model.One year's measured data of a wind farm is used to verify the results,and compared with other common prediction models.The results show that the SGMD-TSNE-TCNre model is effective and has higher accuracy.关键词
风力发电功率预测/持续学习模型/时序卷积神经网络/模态分解/特征降维TSNEKey words
wind power forecast/TCNre/sequential convolutional neural network/modal decomposition/feature dimension reduction TSNE分类
能源科技引用本文复制引用
杨晓华,代盛国,李家浩,李佳..基于持续学习的SGMD-TSNE-TCNre风电功率长期预测[J].节能,2025,44(1):21-25,5.
