基于相似日与ISC-BiLSTM的短期光伏功率预测方法

杨轶航; 韩璐; 史华勃; 邓鑫隆; 陈梓桐; 孙如田

doi:10.19912/j.0254-0096.tynxb.2023-1548

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (1) : 676-685. DOI: 10.19912/j.0254-0096.tynxb.2023-1548

基于相似日与ISC-BiLSTM的短期光伏功率预测方法

杨轶航¹, 韩璐¹, 史华勃², 邓鑫隆¹, 陈梓桐¹, 孙如田³

作者信息 +

SHORT-TERM PHOTOVOLTAIC POWER FORECAST METHOD BASED ON SIMILAR DAYS AND ISC-BiLSTM

Yang Yihang¹, Han Lu¹, Shi Huabo², Deng Xinlong¹, Chen Zitong¹, Sun Rutian³

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文章历史 +

摘要

针对传统光伏功率预测方法的精度和鲁棒性难以兼顾的不足,提出一种结合相似日理论、改进麻雀算法（ISSA）与SE通道注意力机制的卷积（CNN）双向长短期记忆（BiLSTM）神经网络模型（简写为ISC-BiLSTM）,能实现短期光伏功率的准确预测。该方法首先通过相关性计算,筛选出影响光伏功率的主要气象因子;再使用模糊C均值聚类（FCM）方法对存在相似天气特征的相似日进行聚类;然后通过加入SE的CNN对主要气象参数与历史功率的时空特征进行充分提取;接着利用BiLSTM对数据序列间的依赖关系进行捕捉;最后通过ISSA对模型的超参数进行寻优,并选择超参数最优的模型进行功率预测。对比实验与仿真结果表明,该方法预测误差较低,能实现日前分钟级短期光伏功率的准确预测。

Abstract

Aiming at the problem that the accuracy and robustness of traditional photovoltaic power prediction methods are difficult to balance, an improved convolutional （CNN） bidirectional long-term and short-term memory（BiLSTM） neural network model （ISC-BiLSTM） is proposed by combining similar days theory, improved sparrow algorithm （ISSA） and（SE） channel attention mechanism, which can achieve accurate prediction of short-term photovoltaic power. Firstly, the main meteorological factors affecting photovoltaic power are screened out by correlation calculation. Then the fuzzy C-means clustering （FCM） method is used to cluster similar days with similar weather characteristics. Then, the spatial and temporal characteristics of the main meteorological parameters and historical power are fully extracted by SE-CNN; then, BiLSTM is used to capture the dependencies between data sequences. Finally, the hyper-parameters of the model are optimized by ISSA, and the model with the optimal hyper-parameters is selected for power prediction. The comparative experiment and simulation results show that the prediction error of this method is low, and the accurate prediction of short-term photovoltaic power at the minute level before the day can be realized.

导出引用

杨轶航, 韩璐, 史华勃, 邓鑫隆, 陈梓桐, 孙如田. 基于相似日与ISC-BiLSTM的短期光伏功率预测方法[J]. 太阳能学报. 2025, 46(1): 676-685 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1548

Yang Yihang, Han Lu, Shi Huabo, Deng Xinlong, Chen Zitong, Sun Rutian. SHORT-TERM PHOTOVOLTAIC POWER FORECAST METHOD BASED ON SIMILAR DAYS AND ISC-BiLSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 676-685 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1548

中图分类号： TM615

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