基于Stacking多模型融合的极端天气短期风电功率预测方法  

Short-term Wind Power Forecasting Method in Extreme Weather Based on Stacking Multi-model Fusion

作　　者：郑颖颖 李鑫[1] 陈延旭 赵永宁[1] ZHENG Yingying;LI Xin;CHEN Yanxu;ZHAO Yongning(College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China)

机构地区：[1]中国农业大学信息与电气工程学院,北京100083

出　　处：《高电压技术》2024年第9期3871-3882,共12页High Voltage Engineering

基　　金：国家重点研发计划(大规模风电/光伏多时间尺度供电能力预测技术)(2022YFB2403000)。

摘　　要：为了解决极端天气下样本稀缺和单一模型预测精度不高的问题,提出一种基于Stacking多模型融合的极端天气短期风电功率预测方法。首先,提取极端事件的原始数据,通过考虑格兰杰因果的最大相关-最小冗余(maximal relevance minimal redundancy, mRMR)特征选择策略降低数据特征冗余和复杂性;其次,针对极端天气数据稀缺的问题,采用捕捉数据时间动态特性的时间序列生成对抗网络(time-seriesgenerativeadversarialnetwork,TimeGAN)算法进行扩充;最后,考虑到各单一模型的差异性及优势性,构建以卷积神经网络、长短期记忆网络、极端梯度提升树、K最近邻算法、支持向量机为基学习器,以轻量梯度提升机为元学习器的Stacking集成模型对未来3d的风电功率进行预测。实验结果表明,所提方法能够有效提升极端天气下的短期风电功率预测精度,与其他预测模型相比,其归一化平均绝对误差和均方根误差分别改善了2.48%和3.47%。In order to solve the problems of sample scarcity and low forecasting accuracy of a single model in extreme weather,a short-term wind power forecasting method that integrates multiple models in extreme weather is proposed.Firstly,the original data of extreme events are extracted,and the feature redundancy and complexity are reduced by maximal relevance minimal redundancy(mRMR)feature selection strategy considering Granger causality.Secondly,to solve the problem of scarcity of extreme weather data,we use the time-series generative adversarial network(TimeGAN)algorithm to capture the dynamic characteristics of the data.Finally,the differences and advantages of each single model are taken into consideration,and an integrated model is constructed,in which the convolutional neural network,long-and short-term memory network,extreme gradient lifting tree,K-nearest neighbor algorithm,support vector machine are taken as the learning tools and lightweight gradient elevator is taken as the meta-learning tool,so as to predict the wind power in the next three days.Experimental results show that the proposed method can be adopted to effectively improve the short-term wind power prediction accuracy under extreme weather conditions,and the normalized mean absolute error and root mean square error are improved by 2.48%and 3.47%,respectively,compared with other prediction models.

关 键 词：风电功率预测 数据扩充 m RMR特征选择 Stacking集成学习 TimeGAN 极端天气 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM614[自动化与计算机技术—控制科学与工程]