基于LSTM-Attention和CNN-BiGRU误差修正的光伏功率预测

吐松江&#x000b7;卡日; 雷柯松; 马小晶; 吴现; 余凯峰

doi:10.19912/j.0254-0096.tynxb.2023-1136

PDF(1166 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (12) : 85-93. DOI: 10.19912/j.0254-0096.tynxb.2023-1136

基于LSTM-Attention和CNN-BiGRU误差修正的光伏功率预测

吐松江·卡日¹, 雷柯松², 马小晶¹, 吴现¹, 余凯峰¹

作者信息 +

PHOTOVOLTAIC POWER PREDICTION BASED ON LSTM-ATTENTION AND CNN-BiGRU ERROR CORRECTION

Kari·Tusongjiang¹, Lei Kesong², Ma Xiaojing¹, Wu Xian¹, Yu Kaifeng¹

Author information +

文章历史 +

摘要

为有效分析与利用光伏功率预测模型中以特定规律分布的预测误差,提出基于LSTM-Attention和CNN-BiGRU误差修正的光伏功率预测模型。首先,引入注意力机制（Attention）弥补输入序列长时长短期记忆网络（LSTM）难以保留关键信息的不足,建立LSTM-Attention的预测模型对光伏功率进行初步预测。其次,将卷积神经网络（CNN）在非线性特征提取上的优势与双向门控循环单元（BiGRU）在防止多种特征相互干扰的优势相结合,搭建CNN-BiGRU误差预测模型对可能产生的误差进行预测,从而对初步预测结果进行修正。经过实例分析表明：与未经误差修正的预测结果进行对比,经CNN-BiGRU误差预测模型进行误差修正后在不同天气类型中均能有效提高预测精度。

Abstract

To effectively analyze and utilize the prediction errors distributed in a specific pattern in the photovoltaic power prediction model, a photovoltaic power prediction model based on LSTM-Attention and CNN-BiGRU error correction is proposed. Firstly, the LSTM-Attention mechanism is introduced to compensate for the shortcomings of the long short-term memory （LSTM） network, which is difficult to retain key information in the input sequence. Secondly, the advantages of convolutional neural network （CNN） in non-linear feature extraction are combined with the advantages of Bidirectional gated recurrent unit （BiGRU） in preventing multiple features from interfering with each other to build a CNN-BiGRU error prediction model is used to predict the possible errors and to correct the initial prediction results. The experimental results indicates that the CNN-BiGRU error prediction model can effectively improve the prediction accuracy in different weather types when compared with the prediction results without error correction.

导出引用

吐松江·卡日, 雷柯松, 马小晶, 吴现, 余凯峰. 基于LSTM-Attention和CNN-BiGRU误差修正的光伏功率预测[J]. 太阳能学报. 2024, 45(12): 85-93 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1136

Kari·Tusongjiang, Lei Kesong, Ma Xiaojing, Wu Xian, Yu Kaifeng. PHOTOVOLTAIC POWER PREDICTION BASED ON LSTM-ATTENTION AND CNN-BiGRU ERROR CORRECTION[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 85-93 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1136

中图分类号： M615

参考文献

[1] 赖昌伟, 黎静华, 陈博, 等. 光伏发电出力预测技术研究综述[J]. 电工技术学报, 2019, 34(6): 1201-1217.
LAI C W, LI J H, CHEN B, et al.Review of photovoltaic power output prediction technology[J]. Transactions of China Electrotechnical Society, 2019, 34(6): 1201-1217.
[2] 杨国清, 李建基, 王德意, 等. 基于信息熵变权区间组合和边界逼近的短期光伏功率区间预测[J]. 太阳能学报, 2023, 44(2): 381-390.
YANG G Q, LI J J, WANG D Y, et al.Short-term photovoltaic power interval prediction based on information entropy variable weight interval combination and boundary approximation[J]. Acta energiae solaris sinica, 2023, 44(2): 381-390.
[3] 张家安, 郝峰, 董存, 等. 基于两阶段不确定性量化的光伏发电超短期功率预测[J]. 太阳能学报, 2023, 44(1): 69-77.
ZHANG J A, HAO F, DONG C, et al.Ultra-short-term power forecasting of photovoltaic power generation based on two-stage uncertainty quantization[J]. Acta energiae solaris sinica, 2023, 44(1): 69-77.
[4] 刘洁, 林舜江, 梁炜焜, 等. 基于高阶马尔可夫链和高斯混合模型的光伏出力短期概率预测[J]. 电网技术, 2023, 47(1): 266-275.
LIU J, LIN S J, LIANG W K, et al.Short-term probabilistic forecast for power output of photovoltaic station based on high order Markov chain and Gaussian mixture model[J]. Power system technology, 2023, 47(1): 266-275.
[5] 董雪, 赵宏伟, 赵生校, 等. 基于SOM聚类和二次分解的BiGRU超短期光伏功率预测[J]. 太阳能学报, 2022, 43(11): 85-93.
DONG X, ZHAO H W, ZHAO S X, et al.Ultra-short-term forecasting method of photovoltaic power based on SOM clustering, secondary decomposition and BiGRU[J]. Acta energiae solaris sinica, 2022, 43(11): 85-93.
[6] 吉锌格, 李慧, 叶林, 等. 基于波动特性挖掘的短期光伏功率预测[J]. 太阳能学报, 2022, 43(5): 146-155.
JI X G, LI H, YE L, et al.Short-term photovoltaic power forecasting based on fluctuation characteristics mining[J]. Acta energiae solaris sinica, 2022, 43(5): 146-155.
[7] YANG M, ZHAO M, HUANG D W, et al.A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder[J]. Renewable energy, 2022, 194: 659-673.
[8] 刘倩, 胡强, 杨凌帆, 等. 基于时间序列的深度学习光伏发电模型研究[J]. 电力系统保护与控制, 2021, 49(19): 87-98.
LIU Q, HU Q, YANG L F, et al.Deep learning photovoltaic power generation model based on time series[J]. Power system protection and control, 2021, 49(19): 87-98.
[9] YAN J C, HU L, ZHEN Z, et al.Frequency-domain decomposition and deep learning based solar PV power ultra-short-term forecasting model[J]. IEEE transactions on industry applications, 2021, 57(4): 3282-3295.
[10] 张雨金, 杨凌帆, 葛双冶, 等. 基于Kmeans-SVM的短期光伏发电功率预测[J]. 电力系统保护与控制, 2018, 46(21): 118-124.
ZHANG Y J, YANG L F, GE S Y, et al.Short-term photovoltaic power forecasting based on Kmeans algorithm and support vector machine[J]. Power system protection and control, 2018, 46(21): 118-124.
[11] 谭海旺, 杨启亮, 邢建春, 等. 基于XGBoost-LSTM组合模型的光伏发电功率预测[J]. 太阳能学报, 2022, 43(8): 75-81.
TAN H W, YANG Q L, XING J C, et al.Photovoltaic power prediction based on combined XGBoost-LSTM model[J]. Acta energiae solaris sinica, 2022, 43(8): 75-81.
[12] ALRASHIDI M, RAHMAN S.Short-term photovoltaic power production forecasting based on novel hybrid data-driven models[J]. Journal of big data, 2023, 10: 26.
[13] 马磊, 黄伟, 李克成, 等. 基于Attention-LSTM的光伏超短期功率预测模型[J]. 电测与仪表, 2021, 58(2): 146-152.
MA L, HUANG W, LI K C, et al.Photovoltaic ultra-short-term power prediction model based on Attention-LSTM[J]. Electrical measurement & instrumentation, 2021, 58(2): 146-152.
[14] 雷柯松, 吐松江·卡日, 伊力哈木·亚尔买买提, 等. 基于WGAN-GP和CNN-LSTM-Attention的短期光伏功率预测[J]. 电力系统保护与控制, 2023, 51(9): 108-118.
LEI K S, TUSONGJIANG K, YILIHAMU Y, et al.Prediction of short-term photovoltaic power based on WGAN-GP and CNN-LSTM-Attention[J]. Power system protection and control, 2023, 51(9): 108-118.
[15] 余向阳, 赵怡茗, 杨宁宁, 等. 基于VMD-SE-LSSVM和迭代误差修正的光伏发电功率预测[J]. 太阳能学报, 2020, 41(2): 310-318.
YU X Y, ZHAO Y M, YANG N N, et al.Photovoltaic power generation forecasting based on VMD-SE-LSSVM and iterative error correction[J]. Acta energiae solaris sinica, 2020, 41(2): 310-318.
[16] 刘帅, 朱永利, 张科, 等. 基于误差修正ARMA-GARCH模型的短期风电功率预测[J]. 太阳能学报, 2020, 41(10): 268-275.
LIU S, ZHU Y L, ZHANG K, et al.Short-term wind power forecasting based on error correction ARMA-GARCH model[J]. Acta energiae solaris sinica, 2020, 41(10): 268-275.
[17] 丁婷婷, 杨明, 于一潇, 等. 基于误差修正的短期风电功率集成预测方法[J]. 高电压技术, 2022, 48(2): 488-496.
DING T T, YANG M, YU Y X, et al.Short-term wind power integration prediction method based on error correction[J]. High voltage engineering, 2022, 48(2): 488-496.
[18] 丁明, 王磊, 毕锐. 基于改进BP神经网络的光伏发电系统输出功率短期预测模型[J]. 电力系统保护与控制, 2012, 40(11): 93-99, 148.
DING M, WANG L, BI R.A short-term prediction model to forecast output power of photovoltaic system based on improved BP neural network[J]. Power system protection and control, 2012, 40(11): 93-99, 148.
[19] 孙国良, 伊力哈木·亚尔买买提, 张宽, 等. 基于小波变换与IAGA-BP神经网络的短期风电功率预测[J]. 电测与仪表, 2024, 61(5): 126-134, 145.
SUN G L, YILIHAMU Y, ZHANG K, et al.Short-term prediction of wind power based on wavelet transform and IAGA-BP neural network[J]. Electrical measurement & instrumentation, 2024, 61(5): 126-134, 145.
[20] QU J Q, QIAN Z, PEI Y.Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern[J]. Energy, 2021, 232: 120996.
[21] 邢红涛, 郭江龙, 刘书安, 等. 基于CNN-LSTM混合神经网络模型的NO_x排放预测[J]. 电子测量技术, 2022, 45(2): 98-103.
XING H T, GUO J L, LIU S A, et al.NO_x emission forecasting based on CNN-LSTM hybrid neural network[J]. Electronic measurement technology, 2022, 45(2): 98-103.
[22] 邹智, 吴铁洲, 张晓星, 等. 基于贝叶斯优化CNN-BiGRU混合神经网络的短期负荷预测[J]. 高电压技术, 2022, 48(10): 3935-3945.
ZOU Z, WU T Z, ZHANG X X, et al.Short-term load forecast based on Bayesian optimized CNN-BiGRU hybrid neural networks[J]. High voltage engineering, 2022, 48(10): 3935-3945.