考虑相似日选取与PVformer模型的短期光伏功率预测

李练兵; 代亮亮; 李新达; 杨鹏伟; 杨少波; 高国强

doi:10.19912/j.0254-0096.tynxb.2024-0552

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (8) : 341-351. DOI: 10.19912/j.0254-0096.tynxb.2024-0552

考虑相似日选取与PVformer模型的短期光伏功率预测

李练兵¹, 代亮亮¹, 李新达¹, 杨鹏伟², 杨少波³, 高国强¹

作者信息 +

SHORT TERM PHOTOVOLTAIC POWER PREDICTION CONSIDER SIMILAR DAYS SELECTION AND PVFORMER MODEL

Li Lianbing¹, Dai Liangliang¹, Li Xinda¹, Yang Pengwei², Yang Shaobo³, Gao Guoqiang¹

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文章历史 +

摘要

为提高光伏功率预测的准确性,提出一种基于混合蛙跳算法（SFLA）相似日选取和改进Transformer模型的短期光伏功率预测模型——PVformer模型。基于灰色关联度计算相似因子,基于SFLA优化综合相似因子,实现对光伏相似日的选取;PVformer模型基于卷积神经网络（CNN）对光伏数据进行特征获取并降低数据维度,基于双向门控循环单元（BiGRU）提取光伏数据时序特征并对数据进行位置嵌入,基于多头自相关注意力机制寻找序列间关系,并打破信息利用瓶颈。综合相似因子最大的历史日作为预测日的相似日,选择相关性较高的特征作为模型输入,构建历史特征向量和未来气象向量输入到PVformer模型中。对比实验结果显示,PVformer模型可提高日前光伏功率预测的精度,E_MAPE、E_MAE 、E_MSE分别达到1.526%、0.274 MW、0.134 MW²。最后通过消融实验证明模型改进的有效性和必要性,具有一定的实用价值。

Abstract

In order to improve the accuracy of PV power prediction, this paper proposes a short-term PV power prediction model PVformer model based on SFLA similar day selection and improved Transformer model. This article calculates similarity factors based on grey correlation degree, optimizes comprehensive similarity factors based on SFLA, and realizes the selection of photovoltaic similarity days. The PVformer model is based on CNN networks to obtain features from photovoltaic data and reduce data dimensions. The BiGRU network is used to extract temporal features from photovoltaic data and embed data positions. The multi-head autocorrelation attention mechanism is used to find inter sequence relationships and break the bottleneck of information utilization. This article selects the historical day with the highest comprehensive similarity factor as the similarity day for the predicted day, selects features with high correlation as the model input, constructs historical feature vectors and future meteorological vectors to input into the PVformer model. The results of the control experiments show that the PVformer model improves the accuracy of day-ahead PV power prediction,E_MAPE,E_MAE, and E_MSE reach 1.526%, 0.274 MW, and 0.134 MW², respectively. Finally, the effectiveness and necessity of model improvement are demonstrated through ablation experiments, which have certain practical value.

导出引用

李练兵, 代亮亮, 李新达, 杨鹏伟, 杨少波, 高国强. 考虑相似日选取与PVformer模型的短期光伏功率预测[J]. 太阳能学报. 2025, 46(8): 341-351 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0552

Li Lianbing, Dai Liangliang, Li Xinda, Yang Pengwei, Yang Shaobo, Gao Guoqiang. SHORT TERM PHOTOVOLTAIC POWER PREDICTION CONSIDER SIMILAR DAYS SELECTION AND PVFORMER MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 341-351 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0552

中图分类号： TM615

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