计及相似日与ECM的ICEEMDAN-BiGRU-XGBoost-CrossAttention超短期光伏功率预测

李练兵; 高一波; 陈业; 代亮亮; 景睿雄; 高国强

doi:doi:10.19912/j.0254-0096.tynxb.2024-1944

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计及相似日与ECM的ICEEMDAN-BiGRU-XGBoost-CrossAttention超短期光伏功率预测

更新时间：2026-04-08

- 计及相似日与ECM的ICEEMDAN-BiGRU-XGBoost-CrossAttention超短期光伏功率预测
- Vol. 47, Issue 3, Pages: 656-667(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1944
  CLC： TM615
- Online First：07 April 2026，
  
  Published：2026
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李练兵, 高一波, 陈业, et al. 计及相似日与ECM的ICEEMDAN-BiGRU-XGBoost-CrossAttention超短期光伏功率预测[J]. 2026, 47(3): 656-667.
DOI：

李练兵, 高一波, 陈业, et al. 计及相似日与ECM的ICEEMDAN-BiGRU-XGBoost-CrossAttention超短期光伏功率预测[J]. 2026, 47(3): 656-667. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1944.

摘要

为提高光伏功率的预测精度

提出一种考虑相似日选取与误差修正模型（error correction model

ECM）的超短期光伏功率预测方法。首先

利用改进自适应噪声完备集合经验分解（improved complete ensemble empirical mode decomposition with adaptive noise

ICEEMDAN）方法将数据分解并重构为高频与低频分量

输入基于交叉注意力机制（CrossAttention）的双向门控循环单元（bidirectional gated recurrent unit

BiGRU）与优化的分布式梯度提升库（extreme gradient boosting

XGBoost）组合的特征提取与预测模型;其次

利用灰色关联分析方法计算预测日与历史日间的综合相似因子

选取预测日的气象相似日

作为基于BiGRU的相似日信息增强模块的输入

并在初始预测序列基础上构造残差预测序列

构建基于BiGRU的误差修正模型;最后

融合相似日信息后的ICEEMDAN-BiGRU-XGBoost-CrossAttention模型预测结果

叠加误差修正模型的预测误差

得出最后的日内光伏功率预测结果。基于实际光伏场站气象以及光伏发电功率数据

对比不同光伏发电功率模型

验证了所提方法提高了日内超短期光伏发电功率预测精度

具有一定应用价值。

Abstract

To enhance the accuracy of photovoltaic power forecasting

this study proposes an ultra-short-term photovoltaic power prediction method incorporating similar-day selection and an Error Correction Model（ECM）. First

data is decomposed and reconstructed into high-frequency and low-frequency components using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （ICEEMDAN） method. which feeds into a feature extraction and prediction model combining CrossAttention-based Bidirectional Gated Recurrent Units（BiGRU） and eXtreme Gradient Boosting（XGBoost）. Second

the comprehensive similarity factor between the forecast day and historical days is calculated using grey correlation analysis. Meteorologically similar days for the forecast day are selected as input to the BiGRU-based similar-day information enhancement module. A residual forecast sequence is constructed based on the initial forecast sequence to build an error correction model using BiGRU. Finally

the prediction results from the ICEEMDAN-BiGRU-XGBoost-CrossAttention model

integrated with similar-day information

are combined with the prediction errors from the error correction model to derive the final intraday PV power prediction. Using actual meteorological and PV power generation data from photovoltaic stations

comparisons with different PV power generation models validate that the proposed method enhances the accuracy of intraday ultra-short-term PV power prediction and demonstrates practical application value.

关键词

Keywords

references

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