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

Kari&#x000b7;Tusongjiang; Lei Kesong; Ma Xiaojing; Wu Xian; Yu Kaifeng

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

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (12) : 85-93. DOI: 10.19912/j.0254-0096.tynxb.2023-1136

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

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

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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.

Key words

photovoltaic power prediction / deep learning / error correction / attention mechanism / long-short term memory network / bidirectional gated recurrent unit

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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

References

[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.