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

Yang Yihang; Han Lu; Shi Huabo; Deng Xinlong; Chen Zitong; Sun Rutian

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

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 676-685. DOI: 10.19912/j.0254-0096.tynxb.2023-1548

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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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.

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photovoltaic power / forecasting / neural network / attention mechanism / improved sparrow search algorithm / fuzzy clustering

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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

References

[1] 王晓霞, 俞敏, 冀明, 等. 基于气候相似性与SSA-CNN-LSTM的光伏功率组合预测[J]. 太阳能学报, 2023, 44(6): 275-283.
WANG X X, YU M, JI M, et al.Photovoltaic power combination forecasting based on climate similarity and ssa-cnn-lstm[J]. Acta energiae solaris sinica, 2023, 44(6): 275-283.
[2] LI Y T, SU Y, SHU L J.An ARMAX model for forecasting the power output of a grid connected photovoltaic system[J]. Renewable energy, 2014, 66: 78-89.
[3] OGLIARI E, DOLARA A, MANZOLINI G, et al.Physical and hybrid methods comparison for the day ahead PV output power forecast[J]. Renewable energy, 2017, 113: 11-21.
[4] MASSERAN N, RAZALI A M, IBRAHIM K, et al.Fitting a mixture of von Mises distributions in order to model data on wind direction in Peninsular Malaysia[J]. Energy conversion and management, 2013, 72: 94-102.
[5] 李丰君, 王磊, 赵健, 等. 基于天气融合和LSTM网络的分布式光伏短期功率预测方法[J]. 中国电力, 2022, 55(11): 149-154.
LI F J, WANG L, ZHAO J, et al.Research on distributed photovoltaic short-term power prediction method based on weather fusion and LSTM-Net[J]. Electric power, 2022, 55(11): 149-154.
[6] 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.
[7] 王福忠, 王帅峰, 张丽. 基于VMD-LSTM与误差补偿的光伏发电超短期功率预测[J]. 太阳能学报, 2022, 43(8): 96-103.
WANG F Z, WANG S F, ZHANG L.Ultra short term power prediction of photovoltaic power generation based on VMD-LSTM and error compensation[J]. Acta energiae solaris sinica, 2022, 43(8): 96-103.
[8] 张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.
ZHANG Y Q, CHENG Q Z, JIANG W J, et al.Photovoltaic power prediction model based on EMD-PCA-LSTM[J]. Acta energiae solaris sinica, 2021, 42(9): 62-69.
[9] WANG K J, QI X X, LIU H D.A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network[J]. Applied energy, 2019, 251: 113315.
[10] 谢小瑜, 周俊煌, 张勇军, 等. 基于W-BiLSTM的可再生能源超短期发电功率预测方法[J]. 电力系统自动化, 2021, 45(8): 175-184.
XIE X Y, ZHOU J H, ZHANG Y J, et al.W-BiLSTM based ultra-short-term generation power prediction method of renewable energy[J]. Automation of electric power systems, 2021, 45(8): 175-184.
[11] 王开艳, 杜浩东, 贾嵘, 等. 基于相似日聚类和QR-CNN-BiLSTM模型的光伏功率短期区间概率预测[J]. 高电压技术, 2022, 48(11): 4372-4388.
WANG K Y, DU H D, JIA R, et al.Short-term interval probability prediction of photovoltaic power based on similar daily clustering and QR-CNN-BiLSTM model[J]. High voltage engineering, 2022, 48(11): 4372-4388.
[12] 戚晓侠. 基于深度学习的光伏/风电功率预测研究[D]. 哈尔滨: 哈尔滨工程大学, 2020.
QI X X.Research on photovoltaic/wind power prediction based on deep learning[D]. Harbin: Harbin Engineering University, 2020.
[13] 张姗, 冬雷, 纪德洋, 等. 基于NWP相似性分析的超短期光伏发电功率预测[J]. 太阳能学报, 2022, 43(4): 142-147.
ZHANG S, DONG L, JI D Y, et al.Power forecasting of ultra-short-term photovoltaic station based on nwp similarity analysis[J]. Acta energiae solaris sinica, 2022, 43(4): 142-147.
[14] 吕伟杰, 方一帆, 程泽. 基于模糊C均值聚类和样本加权卷积神经网络的日前光伏出力预测研究[J]. 电网技术, 2022, 46(1): 231-238.
LYU W J, FANG Y F, CHENG Z.Prediction of day-ahead photovoltaic output based on FCM-WS-CNN[J]. Power system technology, 2022, 46(1): 231-238.
[15] 夏睿, 高云鹏, 朱彦卿, 等. 基于SE-CNN模型的窃电检测方法研究[J]. 电力系统保护与控制, 2022, 50(20): 117-126.
XIA R, GAO Y P, ZHU Y Q, et al.A detection method of electricity theft behavior based on an SE-CNN model[J]. Power system protection and control, 2022, 50(20): 117-126.
[16] 吉兴全, 曾若梅, 张玉敏, 等. 基于注意力机制的CNN-LSTM短期电价预测[J]. 电力系统保护与控制, 2022, 50(17): 125-132.
JI X Q, ZENG R M, ZHANG Y M, et al.CNN-LSTM short-term electricity price prediction based on an attention mechanism[J]. Power system protection and control, 2022, 50(17): 125-132.
[17] 毕贵红, 赵鑫, 陈臣鹏, 等. 基于多通道输入和PCNN-BiLSTM的光伏发电功率超短期预测[J]. 电网技术, 2022, 46(9): 3463-3476.
BI G H, ZHAO X, CHEN C P, et al.Ultra-short-term prediction of photovoltaic power generation based on multi-channel input and PCNN-BiLSTM[J]. Power system technology, 2022, 46(9): 3463-3476.
[18] SONG D L, YU M, WANG Z F, et al.Wind and wave energy prediction using an AT-BiLSTM model[J]. Ocean engineering, 2023, 281: 115008.
[19] 李争, 罗晓瑞, 张杰, 等. 基于改进麻雀搜索算法的光伏功率短期预测[J]. 太阳能学报, 2023, 44(6): 284-289.
LI Z, LUO X R, ZHANG J, et al.Short term prediction of photovoltaic power based on improved sparrow search algorithm[J]. Acta energiae solaris sinica, 2023, 44(6): 284-289.
[20] 冯增喜, 李诗妍, 赵锦彤, 等. 基于精英反向学习策略的麻雀搜索算法[J]. 计算机仿真, 2023, 40(1): 378-381, 393.
FENG Z X, LI S Y, ZHAO J T, et al.Sparrow search algorithm based on elite reverse learning strategy[J]. Computer simulation, 2023, 40(1): 378-381, 393.
[21] 宋绍剑, 罗世坚, 李国进, 等. 基于MPA-LSTM模型和Bootstrap方法的短期光伏功率区间预测[J]. 广西大学学报(自然科学版), 2022, 47(4): 986-997.
SONG S J, LUO S J, LI G J, et al.A short-term PV power interval forecasting based on MPA-LSTM network model and Bootstrap method[J]. Journal of Guangxi University (natural science edition), 2022, 47(4): 986-997.
[22] 杨锡运, 张璜, 关文渊, 等. 基于滑动分块百分位数Bootstrap法的风电功率概率区间预测[J]. 太阳能学报, 2019, 40(2): 430-437.
YANG X Y, ZHANG H, GUAN W Y, et al.Probabilistic intervals forecasting of wind power based on moving block percentile bootstrap method[J]. Acta energiae solaris sinica, 2019, 40(2): 430-437.