基于多级特征提取的BiLSTM短期光伏出力预测

林文婷; 李培强; 荆志宇; 钟吴君

doi:10.19912/j.0254-0096.tynxb.2023-0894

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (10) : 284-297. DOI: 10.19912/j.0254-0096.tynxb.2023-0894

基于多级特征提取的BiLSTM短期光伏出力预测

林文婷^1,2, 李培强³, 荆志宇^1,2, 钟吴君³

作者信息 +

SHORT-TERM PHOTOVOLTAIC OUTPUT PREDICTION BASED ON MULTI-LEVEL FEATURE EXTRACTION USING BILSTM

Lin Wenting^1,2, Li Peiqiang³, Jing Zhiyu^1,2, Zhong Wujun³

Author information +

文章历史 +

摘要

传统光伏预测模型易受气象数据波动的影响,且对气象特征不敏感。由此,提出基于多级特征提取的BiLSTM短期光伏出力预测方法,用于预测不同天气类型下的光伏出力。首先,选取与光伏出力相关性较高的气象因素作为输入特征;使用模糊C均值（FCM）聚类方法,对样本进行灵活划分,通过计算Xie-Beni指标以确定最佳聚类数,将历史数据集聚类为晴天、少云天、晴转多云、阴雨天和恶劣天气;其次,构建CNN-CBAM-TCN多级特征提取器（MFE）：利用卷积神经网络（CNN）进行初步的特征提取,结合卷积注意力块（CBAM）抑制非重要特征,之后,利用时间卷积网络（TCN）进一步捕捉日内光伏出力的时序特征;最后,借助双向长短期记忆网络（BiLSTM）进行光伏出力预测。在实例分析中,验证了使用Xie-Beni指标确定最佳聚类数的有效性,证明了该模型较其他预测模型在复杂天气类型下具有更高预测精度。

Abstract

Traditional photovoltaic （PV） prediction models are highly susceptible to fluctuations in meteorological data and exhibit low sensitivity to meteorological features. To address this, we propose a short-term PV output prediction method based on multi-level feature extraction using bi-directional long short-term memory （BiLSTM）, aimed at predicting PV output under various weather conditions. Firstly, meteorological factors with high correlation to PV output are selected as input features. The fuzzy C-means （FCM） clustering method is used for flexible sample division, and the Xie-Beni index is calculated to determine the optimal number of clusters, categorizing historical data into sunny, partly cloudy, cloudy, rainy, and severe weather conditions. Next, a multi-level feature extractor （MFE） comprising CNN-CBAM-TCN is constructed： convolutional neural networks （CNN） are employed for initial feature extraction, convolutional block attention module （CBAM） is used to suppress non-essential features, and temporal convolutional networks （TCN） are utilized to capture the temporal characteristics of intra-day PV output. Finally, BiLSTM is used for PV output prediction. Case studies validate the effectiveness of using the Xie-Beni index to determine the optimal number of clusters and demonstrate that this model achieves higher prediction accuracy compared to other prediction models under complex weather conditions.

导出引用

林文婷, 李培强, 荆志宇, 钟吴君. 基于多级特征提取的BiLSTM短期光伏出力预测[J]. 太阳能学报. 2024, 45(10): 284-297 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0894

Lin Wenting, Li Peiqiang, Jing Zhiyu, Zhong Wujun. SHORT-TERM PHOTOVOLTAIC OUTPUT PREDICTION BASED ON MULTI-LEVEL FEATURE EXTRACTION USING BILSTM[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 284-297 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0894

中图分类号： TM615

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