基于对比学习辅助训练的超短期风功率预测方法

利用深度学习方法提高风功率超短期预测精度能够给电力系统日内机组组合、超短期经济调度、和电力备用安排提供更精确的风功率预测结果，对进一步提高电力系统运行的安全性和经济性具有重要意义。本文针对当前深度学习特征提取模块对时序曲线中的隐式特征和趋势变化的相似性提取不充分的问题，提出一种基于对比学习辅助训练的超短期风功率预测模型，主要包括输入模块、特征提取模块、对比学习辅助模块和回归模块。该模型通过自监督的对比学习算法自主生成正负样本、并以拉开正负样本的映射空间距离为目标来辅助训练特征提取模块的网络参数，使得特征提取模块的映射结果中包含了输入信息相似性的隐式特征，进而减少数据冗余信息、增强样本关联性，最终提高风功率预测精度。实验结果表明，对比学习方法的平均绝对误差比长短期记忆网络和轻量梯度提升机方法分别下降了19.9%和6.5%,有效提高了风功率预测精度。

Ultra-short-term wind power forecasting based on contrastive learning-assisted training

The accuracy improvement of ultra-short-term wind power forecast with deep learning methods is of great significance forintraday unit commitment, ultra-short-term economic dispatch, and reserve scheduling of the power systems, which can further enhancethe safety and efficiency. To address the problem in the existing feature extraction models that the similarity of implicit features andchanging trends in time series curves have not been adequately extracted, this article proposes an ultra-short-term wind power forecastmodel based on contrastive learning-assisted training, which mainly consists of an input encoding module, a feature extraction module, acontrastive learning module, and a regression module. The self-supervised contrastive learning module autonomously generates positiveand negative samples and enlarges the distance between the positive and negative samples in the projection space, which help to extractthe implicit features of the similarity of the input information. In this way, the redundant information is reduced, the sample correlationis enhanced, and the accuracy of wind power forecast is ultimately improved. Compared with LSTM and Lightgbm methods, experimentalresults show that the mean absolute error of the proposed method is decreased by 19. 9% and 6. 5% , which effectively increase the windpower prediction accuracy.