RESEARCH ON BI-LSTM ULTRA-SHORT-TERM PHOTOVOLTAIC POWER PREDICTION METHOD BASED ON ATTENTION MECHANISM

Gui Yishu; Huo Yong; Xu Yichun; Li Chenxi

doi:10.19912/j.0254-0096.tynxb.2024-0564

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 437-444. DOI: 10.19912/j.0254-0096.tynxb.2024-0564

RESEARCH ON BI-LSTM ULTRA-SHORT-TERM PHOTOVOLTAIC POWER PREDICTION METHOD BASED ON ATTENTION MECHANISM

Gui Yishu¹, Huo Yong², Xu Yichun², Li Chenxi¹

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Abstract

Aiming at the problems of declining power prediction performance and high appraisal cost of photovoltaic （PV） power plants year by year, this paper proposes an optimization method integrating bidirectional long short-term memory network （Bi-LSTM） and attention mechanism. First, the time dependence of power data is captured by applying Bi-LSTM to improve the understanding of time series changes; second, the combined attention mechanism enables the model to focus on the features that have the greatest impact on the prediction results to further improve the prediction accuracy; and finally, the accuracy is verified by using field historical data modeling. The field model deployment shows that the model can better capture the rapid power changes compared with the traditional single model and integrated learning model, and the ultra-short-term prediction accuracy of the field station can be improved by more than 10%.

Key words

photovoltaic power generation / prediction / long short-term memory / attention mechanism / deployment

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Gui Yishu, Huo Yong, Xu Yichun, Li Chenxi. RESEARCH ON BI-LSTM ULTRA-SHORT-TERM PHOTOVOLTAIC POWER PREDICTION METHOD BASED ON ATTENTION MECHANISM[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 437-444 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0564

References

[1] 万灿, 宋永华. 新能源电力系统概率预测理论与方法及其应用[J]. 电力系统自动化, 2021, 45(1): 2-16.
WAN C, SONG Y H.Theories, methodologies and applications of probabilistic forecasting for power systems with renewable energy sources[J]. Automation of electric power systems, 2021, 45(1): 2-16.
[2] MARKOVICS D, MAYER M J.Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction[J]. Renewable and sustainable energy reviews, 2022, 161: 112364.
[3] 陈元峰, 马溪原, 程凯, 等. 基于气象特征量选取与SVM模型参数优化的新能源超短期功率预测[J]. 太阳能学报, 2023, 44(12): 568-576.
CHEN Y F, MA X Y, CHENG K, et al.Ultra-short-term power forecast of new energy based on meteorological feature selection and SVM model parameter optimization[J]. Acta energiae solaris sinica, 2023, 44(12): 568-576.
[4] 王东风, 刘婧, 黄宇, 等. 结合太阳辐射量计算与CNN-LSTM组合的光伏功率预测方法研究[J]. 太阳能学报, 2024, 45(2): 443-450.
WANG D F, LIU J, HUANG Y, et al.Photovoltaic power prediction method combinating solar radiation calculation and CNN-LSTM[J]. Acta energiae solaris sinica, 2024, 45(2): 443-450.
[5] 林兴宇, 肖迎群, 张苏. 基于极限学习机分位回归的光伏出力区间预测方法[J]. 机械与电子, 2023, 41(6): 3-9, 14.
LIN X Y, XIAO Y Q, ZHANG S.PV output interval forecasting method based on QRELM algorithm[J]. Machinery & electronics, 2023, 41(6): 3-9, 14.
[6] 林帆, 张耀, 东琦, 等. 基于分位数插值和深度自回归网络的光伏出力概率预测[J]. 电力系统自动化, 2023, 47(9): 79-87.
LIN F, ZHANG Y, DONG Q, et al.Probability prediction of photovoltaic output based on quantile interpolation and deep autoregressive network[J]. Automation of electric power systems, 2023, 47(9): 79-87.
[7] 王倩, 张智晟, 王帅, 等. 基于PSO-RNN的光伏发电功率预测研究[J]. 青岛大学学报(工程技术版), 2014, 29(4): 40-43.
WANG Q, ZHANG Z S, WANG S, et al.Study of power forecasting of photovoltaic based on PSO-RNN[J]. Journal of Qingdao University (engineering & technology edition), 2014, 29(4): 40-43.
[8] 蔡源, 吴浩, 唐丹. 光伏发电功率预测方法综述[J]. 四川电力技术, 2024, 47(2): 25-31.
CAI Y, WU H, TANG D.Overview of photovoltaic power generation prediction methods[J]. Sichuan electric power technology, 2024, 47(2): 25-31.
[9] YU M, NIU D X, WANG K K, et al.Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification[J]. Energy, 2023, 275: 127348.
[10] 杨春熙, 韩威, 高志球. 基于GRU神经网络的太阳辐照度短期预测[J]. 中国科技论文, 2020, 15(1): 8-14.
YANG C X, HAN W, GAO Z Q.Short-term forecasting for solar irradiance using GRU neural network[J]. China sciencepaper, 2020, 15(1): 8-14.
[11] 官松泽, 唐钰本, 蔡争, 等. 基于K-means++-Bi-LSTM的太阳辐照度超短期预测[J]. 太阳能学报, 2023, 44(12): 170-174.
GUAN S Z, TANG Y B, CAI Z, et al.Ultra-short-term forecast of solar irradiance based on K-means++-Bi-LSTM[J]. Acta energiae solaris sinica, 2023, 44(12): 170-174.
[12] 姜景芮, 苗田, 彭婧, 等. 基于深度学习算法的光伏发电功率预测方法研究[J]. 自动化应用, 2023(13): 102-104.
JIANG J R, MIAO T, PENG J, et al.Research on photovoltaic power prediction method based on deep learning algorithm[J]. Automation application, 2023(13): 102-104.
[13] ZHANG Y G, PAN Z Y, WANG H, et al.Achieving wind power and photovoltaic power prediction: an intelligent prediction system based on a deep learning approach[J]. Energy, 2023, 283: 129005.
[14] 叶林, 裴铭, 路朋, 等. 基于天气分型的短期光伏功率组合预测方法[J]. 电力系统自动化, 2021, 45(1): 44-54.
YE L, PEI M, LU P, et al.Combination forecasting method of short-term photovoltaic power based on weather classification[J]. Automation of electric power systems, 2021, 45(1): 44-54.
[15] FENG X, WEI Y J, PAN X L, et al.Academic emotion classification and recognition method for large-scale online learning environment-based on A-CNN and LSTM-ATT deep learning pipeline method[J]. International journal of environmental research and public health, 2020, 17(6): 1941.
[16] PREMALATHA M, VISWANATHAN V, ČEPOVÁ L.Application of semantic analysis and LSTM-GRU in developing a personalized course recommendation system[J]. Applied sciences, 2022, 12(21): 10792.
[17] 顾国庆, 李晓辉. 基于箱线图异常检测的指数加权平滑预测模型[J]. 计算机与现代化, 2021(1): 28-33.
GU G Q, LI X H.Exponential weighted smoothing prediction model based on abnormal detection of box-plot[J]. Computer and modernization, 2021(1): 28-33.
[18] DHARMA RAJ T, KUMAR C, SUBRAMANIAM G, et al.A novel ROA optimized Bi-LSTM based MPPT controller for grid connected hybrid solar-wind system[J]. COMPEL-the international journal for computation and mathematics in electrical and electronic engineering, 2023, 42(2): 378-401.