南方电网技术2025，Vol.19Issue(10)：47-55,110,10.DOI:10.13648/j.cnki.issn1674-0629.2025.10.005
基于信息重组和TCN-LSTM-MHSA的超短期风电功率预测
Ultra-Short-Term Wind Power Forecasting Based on Information Recombination and TCN-LSTM-MHSA
摘要
Abstract
To improve the operational efficiency of wind farms and ensure the stable operation of the power system,an ultra-short-term wind power forecasting method based on information recombination and TCN-LSTM-MHSA is proposed.Using variational mode decomposition to split the power data,the subsequences are reorganized into high entropy sequences,medium entropy sequences,and low entropy sequences with different information types based on the evaluation results of sample entropy;Extracting feature representations of data through temporal convolutional network(TCN),further processing data features using the long short-term memory network(LSTM),and parallel learning of different attention representations using multi-head self-attention mechanism(MHSA)to construct a wind power prediction model.Using two datasets with different output capacity and fan composition as benchmarks to validate the model,the results show that the model in this paper has better prediction ability.关键词
风电/功率预测/时间卷积网络/长短期记忆网络/多头自注意力机制Key words
wind power/power forecasting/temporal convolutional network/long short-term memory/multi-head self-attention分类
信息技术与安全科学引用本文复制引用
陈磊,黄凯阳,张怡,蔡坤哲,陈禹,张志瑞..基于信息重组和TCN-LSTM-MHSA的超短期风电功率预测[J].南方电网技术,2025,19(10):47-55,110,10.基金项目
国家重点研发计划资助项目(2021YFE0190900) （2021YFE0190900）
教育部产学合作协同育人项目(230802495182120). Supported by the National Key Research and Development Program of China(2021YFE0190900) （230802495182120）
Ministry of Education Collaborative Industry and Education Partnership for Holistic Development Program(230802495182120). （230802495182120）
