计及NWP相似日的MHA-LSTM光伏发电超短期功率预测

叶建盈; 刘昭启; 李洲辰; 林波; 刘磊; 陈颖婷

doi:10.19912/j.0254-0096.tynxb.2024-1857

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (2) : 132-139. DOI: 10.19912/j.0254-0096.tynxb.2024-1857

计及NWP相似日的MHA-LSTM光伏发电超短期功率预测

叶建盈, 刘昭启, 李洲辰, 林波, 刘磊, 陈颖婷

作者信息 +

ULTRA-SHORT-TERM PHOTOVOLTAIC POWER PREDICTION USING MHA-LSTM CONSIDERING NWP SIMILAR DAYS

Ye Jianying, Liu Zhaoqi, Li Zhouchen, Lin Bo, Liu Lei, Chen Yingting

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文章历史 +

摘要

针对光伏发电领域中使用历史数据进行超短期功率预测精度不足的问题,提出一种融合数值天气预报（NWP）相似日与多头注意力-长短期记忆网络（MHA-LSTM）的超短期光伏发电功率预测方法。采用相关系数法分析光伏发电功率与气象信息的内在联系,梳理历史数据中与光伏发电功率高度相关的气象因素,构建关键气象信息的变量矩阵并进行分段处理,以便进行更为精细的相似日分析。引入多头自注意力机制以自动捕捉NWP数据中所蕴含的复杂函数关系,并利用长短期记忆神经网络（LSTM）对经多头自注意力机制增强后的数据信息进行处理以得到更为准确的超短期预测结果。最后,选取多种不同天气类型对算法的预测精确度展开对比分析,实验结果显示所提出的预测方法相较于传统算法,在预测精度上实现显著提升,验证了该方法的有效性和准确性。

Abstract

To address the issue of insufficient accuracy in ultra-short-term power prediction using historical data in the field of photovoltaic power generation, an ultra-short-term PV power prediction method is proposed, which integrates numerical weather prediction （NWP） similarity day and multi-head attention-long and short-term memory network （MHA-LSTM）. The correlation coefficient method is used to analyze the inherent relationship between PV power and meteorological information, combing the meteorological factors that are highly correlated with PV power in the historical data, constructing the variable matrix of key meteorological information, and carrying out the segmentation process in order to carry out a more fine-grained analysis of similar days. A multi-head self-attention mechanism is introduced to automatically capture the complex functional relationships embedded in the NWP data, and the data information enhanced by the multi-head self-attention mechanism is processed by using a long- and short-term memory neural network （LSTM） to obtain more accurate ultrashort-term prediction results. Finally, a number of different weather types are selected to compare and analyze the prediction accuracy of the algorithm, and the experimental results show that the prediction method proposed in this paper achieves a significant improvement in prediction accuracy compared with the traditional algorithm, which verifies the effectiveness and accuracy of the method.

导出引用

叶建盈, 刘昭启, 李洲辰, 林波, 刘磊, 陈颖婷. 计及NWP相似日的MHA-LSTM光伏发电超短期功率预测[J]. 太阳能学报. 2026, 47(2): 132-139 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1857

Ye Jianying, Liu Zhaoqi, Li Zhouchen, Lin Bo, Liu Lei, Chen Yingting. ULTRA-SHORT-TERM PHOTOVOLTAIC POWER PREDICTION USING MHA-LSTM CONSIDERING NWP SIMILAR DAYS[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 132-139 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1857

中图分类号： TM615

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