发电技术2025，Vol.46Issue(6)：1123-1132,10.DOI:10.12096/j.2096-4528.pgt.24251
一种基于时序卷积网络-Transformer的海上风电功率预测方法
Power Prediction Method for Offshore Wind Farms Based on Temporal Convolutional Network-Transformer
摘要
Abstract
[Objectives]Offshore wind power is of great significance for achieving carbon peaking and carbon neutrality goals,yet its power prediction remains challenging.To address this issue,this study proposes a novel offshore wind power prediction method integrating temporal convolutional network(TCN)with the Transformer framework.[Methods]Firstly,discrete cosine transform(DCT)technology is incorporated to effectively extract frequency-domain features from time-series data,optimizing feature weight allocation by fully exploiting frequency-domain characteristics.Based on this,a hybrid TCN-Transformer model is constructed,which is capable of comprehensively integrating both time-domain and DCT-processed frequency-domain information,enabling the deep learning and accurate prediction of the data.[Results]Comparative experiments are conducted using two sets of actual operational data from an offshore wind farm.The results show that the proposed method not only performs well in feature extraction but also significantly improves the accuracy of power prediction,fully verifying its effectiveness and application value.[Conclusions]The TCN-Transformer model integrated with DCT technology demonstrates exceptional prediction performance in offshore wind power prediction.Compared to other benchmark models,it achieves a significant improvement in prediction accuracy.This approach provides robust support for the efficient operation and optimal management of offshore wind farms.关键词
海上风电/功率预测/离散余弦变换/时序卷积网络/机器学习/深度学习/可再生能源Key words
offshore wind power/power prediction/discrete cosine transform/temporal convolutional network/machine learning/deep learning/renewable energy分类
能源科技引用本文复制引用
王曦,陈心怡..一种基于时序卷积网络-Transformer的海上风电功率预测方法[J].发电技术,2025,46(6):1123-1132,10.基金项目
国家重点研发计划项目(2023YFB4403500).Project Supported by National Key Research and Development Program of China(2023YFB4403500). （2023YFB4403500）
