高技术通讯2025，Vol.35Issue(1)：102-112,11.DOI:10.3772/j.issn.1002-0470.2025.01.011
基于Word2vec-LSTM与聚类修正的海上风电出力预测方法
A method for predicting offshore wind power output based on Word2vec-LSTM and cluster correction
摘要
Abstract
Aiming at the problem that the accuracy of mainstream prediction methods is low,a prediction method of off-shore wind power output based on word to vector long short-term memory(Word2vec-LSTM)and cluster correction is proposed.The Word2vec method is improved to extract features from time series data and efficient utilization of data information is achieved.Based on the prediction model of long short-term memory neural network,a prediction result correction algorithm based on k-shape cluster results is studied.Finally,based on real data from an offshore wind farm in Jiangsu,the results showed that the average mean absolute error(MAE)and root mean square error(RMSE)of the Word2vec-LSTM and cluster correction based offshore wind power output prediction method reached 5.04 and 5.42,respectively.Compared with traditional LSTM prediction models,the average error decreases by 11.10%and 12.25%,providing technical support for offshore wind power grid connection and grid regulation.关键词
海上风电/功率预测/特征提取/聚类修正Key words
offshore wind power/power prediction/feature extraction/cluster correction引用本文复制引用
潘国兵,余方吉,陈坚,欧阳静..基于Word2vec-LSTM与聚类修正的海上风电出力预测方法[J].高技术通讯,2025,35(1):102-112,11.基金项目
国家重点研发计划(2017YFA0700301)和浙江省重点研发计划(2022C01244)资助项目. （2017YFA0700301）
