计算机应用与软件2024，Vol.41Issue(6)：305-311,349,8.DOI:10.3969/j.issn.1000-386x.2024.06.044
基于竞争学习机制的LSTM风电多目标区间预测
MULTIPLE OBJECTIVE INTERVAL PREDICTION OF LSTM WIND POWER BASED ON COMPETITIVE LEARNING MECHANISM
摘要
Abstract
In order to further improve the comprehensive effect of interval prediction,a multiple objective interval prediction method of LSTM wind power based on competitive learning mechanism is proposed.The upper and lower bounds estimation model based on LSTM was proposed to construct the multiple objective prediction model of wind power interval,and the relationship between the estimation error and the average width of prediction interval in the multiple objective system was studied.Further considering the prediction error,a new partial least squares evaluation index was introduced.In addition,by introducing competitive learning mechanism,an improved non dominated quick sort genetic algorithm was proposed,which effectively realized multiple objective optimization.Two real wind power data sets were used to verify the proposed method.The results show that the proposed method has high prediction accuracy.关键词
风电预测/长短期记忆网络/区间预测/遗传算法Key words
Wind power forecasting/Long and short-term memory network/Interval forecasting/Genetic algorithm分类
信息技术与安全科学引用本文复制引用
任鹏,付文杰,申洪涛,陶鹏,张洋瑞..基于竞争学习机制的LSTM风电多目标区间预测[J].计算机应用与软件,2024,41(6):305-311,349,8.基金项目
国网河北省电力有限公司科技项目(kj2020-088). （kj2020-088）
