首页|考虑多变量相关性改进的风电场Transformer中长期预测模型

考虑多变量相关性改进的风电场Transformer中长期预测模型

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挖掘风电场多变量相关性对提高中长期的预测精度具有积极影响.针对Transformer模型在捕获多变量间相关性方面的不足,提出考虑多变量相关性的多变量中长期预测模型.首先,采用多变量独立嵌入(MIE)对风电场多变量进行独立建模;然后,使用二维概率稀疏注意力(TPSA)提取时间和变量间的特征信息;最后,通过多层式编码器-解码器(MED)将多尺度的特征信息聚合,一次性输出预测结果.算例分析表明,所提模型与LSTM模型、Transformer模型、Informer模型相比,均方误差在各预测时长分别降低了42.58%～66.83%,32.58%～53.49%,14.38%～30.92%,并通过消融实验验证和分析了所提改进的有效性.
Improved Transformer Medium and Long Term Prediction Model of Wind Farm Considering Multivariate Correlation
Mining the multivariate correlation of wind farms has positive effect on improving the prediction accuracy in the medium and long term.In view of the shortcomings of Transformer model in capturing multi-variable correlation,a multi-variable medium and long term prediction model considering multiple correlation is proposed.Firstly,multivariate independent embedding(MIE)is used to model multiple variables of the wind farm.Then,two-dimensional probabilistic sparse attention(TPSA)is used to extract the feature information between time and variables.Finally,the multi-scale feature information is aggregated by multilayer-style encoder-decoder(MED)to output the prediction results at one time.Example analysis shows that compared with LSTM model,Transformer model and Informer model,the mean square error of the proposed model decreases by 42.58%～66.83%,32.58%～53.49%and 14.38%～30.92%respectively in each prediction time.The effectiveness of the proposed improvement is verified and analyzed by ablation experiments.

multivariate correlationtransformer modelmultivariate independent embeddingtwo-dimensional probabilistic sparse attentionmultilayer-style encoder-decoder

李士哲、王霄慧、刘帅

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华北电力大学自动化系,河北保定 071003

多变量相关性 Transformer模型 多变量独立嵌入 二维概率稀疏注意力 多层式编码器-解码器

国家自然科学基金中央高校基本科研业务专项华北电力大学项目

619731179160323007

2024

智慧电力
陕西省电力公司

智慧电力

CSTPCD北大核心
影响因子：0.831
ISSN：1673-7598
年,卷(期)：2024.52(4)
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