全球能源互联网2026，Vol.9Issue(3)：361-370,10.DOI:10.19705/j.cnki.issn2096-5125.20240104
基于双通道网络和迁移学习的超短期风电功率预测
Ultra-short-term Wind Power Prediction Based on Dual-channel Networks and Transfer Learning
杨海林 1李立新 2范瑞铭 1张鑫 2王渊龄 1李媛媛 2赵雪1
作者信息
- 1. 国网青海省电力公司经济技术研究院,青海省 西宁市 810000
- 2. 电网安全与节能国家重点实验室(中国电力科学研究院有限公司),北京市 海淀区 100192
- 折叠
摘要
Abstract
An ultra short term wind power prediction method based on dual channel network and transfer learning is proposed to address the problem of insufficient historical data for newly built wind farms,which makes it difficult to achieve high-precision wind power prediction.In this method,the features are first extracted from the meteorological data and power data through the dual-channel network.Among them,the meteorological channel adopts the attention mechanism to dynamically assign weights to meteorological factors.After that,the convolutional neural network is used to extract high-dimensional meteorological features in the data.The power channel uses a gated recurrent unit to extract the change trend of features and predict future power generation.Then,the features extracted by the dual-channel are then combined and regressed to the wind power via a feedforward neural network.Finally,the training strategy of transfer learning is used to pre-train the model on the wind farm data with sufficient data.After pre-training,transfer the model to a target wind farm with insufficient data volume.Simulation results show that the proposed method outperforms other predictive models.Moreover,it can effectively improve the problem of poor prediction accuracy caused by insufficient data volume of new wind farms.关键词
风电功率预测/双通道网络/迁移学习/注意力机制/卷积神经网络/门控循环单元Key words
wind power prediction/dual-channel/transfer learning/attention mechanism/convolutional neural network/gated recurrent unit分类
信息技术与安全科学引用本文复制引用
杨海林,李立新,范瑞铭,张鑫,王渊龄,李媛媛,赵雪..基于双通道网络和迁移学习的超短期风电功率预测[J].全球能源互联网,2026,9(3):361-370,10.
