RESEARCH ON SHORT-TERM PHOTOVOLTAIC POWER PROBABILITY PREDICTION METHOD BASED ON IMPROVED TRANSFORMER MODEL

Liu Yifeng; Zhao Lei; Li Jiangpeng; Meng Fei; Xu Hengshan; Liu Chunyan

doi:10.19912/j.0254-0096.tynxb.2024-2161

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 653-662. DOI: 10.19912/j.0254-0096.tynxb.2024-2161

RESEARCH ON SHORT-TERM PHOTOVOLTAIC POWER PROBABILITY PREDICTION METHOD BASED ON IMPROVED TRANSFORMER MODEL

Liu Yifeng¹, Zhao Lei¹, Li Jiangpeng¹, Meng Fei¹, Xu Hengshan², Liu Chunyan²

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Abstract

To improve the accuracy of photovoltaic power prediction, an improved Transformer probability prediction method is proposed, including data preprocessing, prediction model, and post-processing process. Given the problem of missing photovoltaic power data, this paper proposes a random forest interpolation method guided by the predicted mean to interpolate the missing data and improve the data integrity. Then, a probability prediction method based on the enhanced Transformer model is introduced. The model uses a multi-head attention mechanism combined with a normalization layer and a residual connection to strengthen the robustness of the model and its ability to handle long sequence dependency problems. In the post-processing stage, a fourth-order polynomial and LSTM are combined to correct the prediction error. Finally, historical data are used for experimental verification, and the results show that the proposed model has high prediction accuracy and reliability.

Key words

PV power / deep learning / modelling / probability / interpolation / forecasting / LSTM

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Liu Yifeng, Zhao Lei, Li Jiangpeng, Meng Fei, Xu Hengshan, Liu Chunyan. RESEARCH ON SHORT-TERM PHOTOVOLTAIC POWER PROBABILITY PREDICTION METHOD BASED ON IMPROVED TRANSFORMER MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 653-662 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2161

References

[1] 李芬, 于淏, 孙改平, 等. 基于优化变分模态分解的光伏功率超短期区间预测方法[J]. 太阳能学报, 2024, 45(8): 367-376.
LI F, YU H, SUN G P, et al.Ultra short term interval prediction method of photovoltaic power based on optimal variational mode decomposition[J]. Acta energiae solaris sinica, 2024, 45(8): 367-376.
[2] PERSSON C, BACHER P, SHIGA T, et al.Multi-site solar power forecasting using gradient boosted regression trees[J]. Solar energy, 2017, 150: 423-436.
[3] LIU D, SUN K.Random forest solar power forecast based on classification optimization[J]. Energy, 2019, 187: 115940.
[4] SHI J, LEE W J, LIU Y Q, et al.Forecasting power output of photovoltaic systems based on weather classification and support vector machines[J]. IEEE transactions on industry applications, 2012, 48(3): 1064-1069.
[5] BABALHAVAEJI A, RADMANESH M, JALILI M, et al.Photovoltaic generation forecasting using convolutional and recurrent neural networks[J]. Energy reports, 2023, 9: 119-123.
[6] 秦宇, 许野, 王鑫鹏, 等. 基于改进FCM-LSTM的光伏出力短期预测研究[J]. 太阳能学报, 2024, 45(8): 304-313.
QIN Y, XU Y, WANG X P, et al.Study on short-term photovoltaic output prediction based on improved FCM-LSTM[J]. Acta energiae solaris sinica, 2024, 45(8): 304-313.
[7] ABDEL-NASSER M, MAHMOUD K.Accurate photovoltaic power forecasting models using deep LSTM-RNN[J]. Neural computing and applications, 2019, 31(7): 2727-2740.
[8] 陈庆明, 廖鸿飞, 孙颖楷, 等. 基于GWO-GRU的光伏发电功率预测[J]. 太阳能学报, 2024, 45(7): 438-444.
CHEN Q M, LIAO H F, SUN Y K, et al.Photovoltaic power prediction model based on GWO-GRU[J]. Acta energiae solaris sinica, 2024, 45(7): 438-444.
[9] 吴凡曈, 杨俊华, 杨梦丽, 等. 基于卷积门控循环单元的波浪发电系统输出功率预测[J]. 太阳能学报, 2024, 45(8): 682-688.
WU F T, YANG J H, YANG M L, et al.Output power prediction of wave power generation system based on convolutional gated cyclic unit[J]. Acta energiae solaris sinica, 2024, 45(8): 682-688.
[10] JURADO M, SAMPER M, ROSÉS R. An improved encoder-decoder-based CNN model for probabilistic short-term load and PV forecasting[J]. Electric power systems research, 2023, 217: 109153.
[11] 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.
[12] LI Y Y, SONG L D, ZHANG S, et al.A TCN-based hybrid forecasting framework for hours-ahead utility-scale PV forecasting[J]. IEEE transactions on smart grid, 2023, 14(5): 4073-4085.
[13] ZENG A L, CHEN M X, ZHANG L, et al.Are transformers effective for time series forecasting?[J]. Proceedings of the AAAI conference on artificial intelligence, 2023, 37(9): 11121-11128.
[14] KIM T, KO W, KIM J.Analysis and impact evaluation of missing data imputation in day-ahead PV generation forecasting[J]. Applied sciences, 2019, 9(1): 204.
[15] KIM J, OBREGON J, PARK H, et al.Multi-step photovoltaic power forecasting using transformer and recurrent neural networks[J]. Renewable and sustainable energy reviews, 2024, 200: 114479.
[16] CHEN B, LI J H.Combined probabilistic forecasting method for photovoltaic power using an improved Markov chain[J]. IET generation, transmission & distribution, 2019, 13(19): 4364-4373.
[17] RAMAKRISHNA R, SCAGLIONE A, VITTAL V, et al.A model for joint probabilistic forecast of solar photovoltaic power and outdoor temperature[J]. IEEE transactions on signal processing, 2019, 67(24): 6368-6383.
[18] GNEITING T, LERCH S, SCHULZ B.Probabilistic solar forecasting: benchmarks, post-processing, verification[J]. Solar energy, 2023, 252: 72-80.