PHOTOVOLTAIC POWER PREDICTION BASED ON SPATIO-TEMPORAL DUAL-STREAM NETWORK AND MULTI-ATTENTION

Li Hengjie; Long Xianhua; Zhou Yun; Feng Donghan; Ma Xiping

doi:10.19912/j.0254-0096.tynxb.2024-1741

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 51-59. DOI: 10.19912/j.0254-0096.tynxb.2024-1741

PHOTOVOLTAIC POWER PREDICTION BASED ON SPATIO-TEMPORAL DUAL-STREAM NETWORK AND MULTI-ATTENTION

Li Hengjie¹, Long Xianhua¹, Zhou Yun², Feng Donghan², Ma Xiping³

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Abstract

For accuracy limitations in photovoltaic （PV） power prediction due to the intermittent and stochastic nature of PV power generation, this study proposes a short-term PV power prediction model based on spatio-temporal dual-stream networks and multi-attention. The model integrates networks ModernTCN, ITransformer, GRU, and TimesNet networks, effectively exploring the spatial distribution characteristics and temporal dynamics of PV power generation. In addition, the model fuses extracted features, and generates feature vectors rich in Spatio-Temporal information by an iterative cross-attention feature mechanism. Experimental results demonstrate that the model is superior to the current mainstream timing series models in terms of prediction accuracy.

Key words

photovoltaic power prediction / consensus clustering / feature fusion / attention mechanism / dual-stream network

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Li Hengjie, Long Xianhua, Zhou Yun, Feng Donghan, Ma Xiping. PHOTOVOLTAIC POWER PREDICTION BASED ON SPATIO-TEMPORAL DUAL-STREAM NETWORK AND MULTI-ATTENTION[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 51-59 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1741

References

[1] KABIR E, KUMAR P, KUMAR S, et al.Solar energy: potential and future prospects[J]. Renewable and sustainable energy reviews, 2018, 82: 894-900.
[2] 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.
[3] 董存, 王铮, 白捷予, 等. 光伏发电功率超短期预测方法综述[J]. 高电压技术, 2023, 49(7): 2938-2951.
DONG C, WANG Z, BAI J Y, et al.Review of ultra-short-term forecasting methods for photovoltaic power generation[J]. High voltage engineering, 2023, 49(7): 2938-2951.
[4] VOYANT C, NOTTON G, KALOGIROU S, et al.Machine learning methods for solar radiation forecasting: a review[J]. Renewable energy, 2017, 105: 569-582.
[5] 马原, 张雪敏, 甄钊, 等. 基于修正晴空模型的超短期光伏功率预测方法[J]. 电力系统自动化, 2021, 45(11): 44-51.
MA Y, ZHANG X M, ZHEN Z, et al.Ultra-short-term photovoltaic power prediction method based on modified clear-sky model[J]. Automation of electric power systems, 2021, 45(11): 44-51.
[6] KRISHNAN N, KUMAR K R, INDA C S.How solar radiation forecasting impacts the utilization of solar energy: a critical review[J]. Journal of cleaner production, 2023, 388: 135860.
[7] 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.
[8] RAZA M Q, NADARAJAH M, EKANAYAKE C.On recent advances in PV output power forecast[J]. Solar energy, 2016, 136: 125-144.
[9] 刘大贵, 王维庆, 张慧娥, 等. 基于隐马尔科夫修正的光伏中长期电量预测及调度计划应用[J]. 高电压技术, 2023, 49(2): 840-848.
LIU D G, WANG W Q, ZHANG H E, et al.Mid-long term available quantity of electricity forecasting with error calibration by hidden Markov model in photovoltaic and application of dispatching plan[J]. High voltage engineering, 2023, 49(2): 840-848.
[10] 李宏扬, 高丙朋. 基于改进VMD和SNS-Attention-GRU的短期光伏发电功率预测[J]. 太阳能学报, 2023, 44(8): 292-300.
LI H Y, GAO B P.Short-term PV power forecasting based on improved VMD and SNS-Attention-GRU[J]. Acta energiae solaris sinica, 2023, 44(8): 292-300.
[11] 王粟, 江鑫, 曾亮, 等. 基于VMD-DESN-MSGP模型的超短期光伏功率预测[J]. 电网技术, 2020, 44(3): 917-926.
WANG S, JIANG X, ZENG L, et al.Ultra-short-term photovoltaic power prediction based on VMD-DESN-MSGP model[J]. Power system technology, 2020, 44(3): 917-926.
[12] 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.
[13] 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.
[14] 王剑斌, 傅金波, 陈博. 基于强化学习的多模型融合光伏发电功率预测方法[J]. 太阳能学报, 2024, 45(6): 382-388.
WANG J B, FU J B, CHEN B.Multi-model fusion photovoltaic power generation prediction method based on reinforcement learning[J]. Acta energiae solaris sinica, 2024, 45(6): 382-388.
[15] 吴珺玥, 赵二刚, 郭增良, 等. 基于Spearman系数和TCN的光伏出力超短期多步预测[J]. 太阳能学报, 2023, 44(9): 180-186.
WU J Y, ZHAO E G, GUO Z L, et al.Ultra-short-term photovoltaic power multi-step prediction based on Spearman coefficient and TCN[J]. Acta energiae solaris sinica, 2023, 44(9), 180-186.
[16] 武新章, 王泽宇, 代伟, 等. 基于异质聚类与Stacking的双集成光伏发电功率预测[J]. 电网技术, 2023, 47(1): 275-284.
WU X Z, WANG Z Y, DAI W, et al.Bi-ensembled photovoltaic (PV) power prediction based on heterogeneous clustering and Stacking[J]. Power system technology, 2023, 47(1): 275-284.
[17] 唐雅洁, 林达, 倪筹帷, 等. 基于XGBoost的双层协同实时校正超短期光伏预测[J]. 电力系统自动化, 2021, 45(7): 18-27.
TANG Y J, NI C W, et al.XGBoost based bi-layer collaborative real-time calibration for ultra-short-term photovoltaic prediction[J]. Automation of electric power systems, 2021, 45(7): 18-27.
[18] 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 quantification[J]. Acta energiae solaris sinica, 2023, 44(1): 69-77.
[19] 武宇翔, 韩肖清, 牛哲文, 等. 融合多注意力深度神经网络的可解释光伏功率区间预测[J]. 电网技术, 2024, 48(7): 2928-2939.
WU Y X, HAN X Q, NIU Z W, et al.Interpretable photovoltaic power interval prediction using multi-attention deep neural networks[J]. Power system technology, 2024. 48(7): 2928-2939.
[20] 董俊, 刘瑞, 束洪春, 等. 基于BIRCH聚类的L-Transformer分布式光伏短期发电功率预测[J]. 高电压技术, 2024, 50(9): 3883-3893.
DONG J, LIU R, SHU H C, et al.Short-term distributed photovoltaic power generation prediction based on BIRCH clustering and L-Transformer[J]. High voltage engineering, 2024, 50(9): 3883-3893.
[21] CHUNG J Y, GULCEHER C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL]. arXiv:1412.3555, 2014.
[22] 牛甲俊, 张薇, 许达明. 基于FCM和ITransformer-TCN的短期风电集群功率预测[J].东北电力大学学报, 2024, 44(1):9-16.
NIU J J,ZHANG W,XU D M.Short-term power prediction of wind power cluster based on FCM and ITransformer-TCN[J]. Journal of Northeast Electric Power University, 2024, 44(1): 9-16.
[23] FANG B L, XU L, LUO Y J, et al.a method for short-term electric load forecasting based on the FMLP-iTransformer model[J]. Energy reports, 2024, 12: 3405-3411.
[24] ZUO C H, WANG J L, LIU M P, et al.An ensemble framework for short-term load forecasting based on TimesNet and TCN[J]. Energies, 2023, 16(14): 5330.
[25] LUO D H, WANG X. ModernTCN: a modern pure convolution structure for general time series analysis[C]//International Conference on Learning Representations (2024).
[26] YAO D C, TANG B, YANG J W, et al.Patch ModernTCN-Mixer: a dual-task temporal convolutional network framework for hybrid implementation of first prediction time detection and remaining useful life prognosis[J]. Measurement science and technology, 2024, 35(11): 116011.
[27] 张童彦, 廖清芬, 唐飞, 等. 基于气象资源插值与迁移学习的广域分布式光伏功率预测方法[J]. 中国电机工程学报, 2023, 43(20): 7929-7940.
ZHANG T Y, LIAO Q F, TANG F, et al.Wide-area distributed photovoltaic power forecast method based on meteorological resource interpolation and transfer learning[J]. Proceedings of the CSEE, 2023, 43(20): 7929-7940.
[28] 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.
[29] SHEN J F, CHEN Y F, LIU Y, et al.ICAFusion: Iterative cross-attention guided feature fusion for multispectral object detection[J]. Pattern recognition, 2024, 145: 109913.
[30] XIAO D, MENG Q Y, LI S P, et al.Improving transformers with dynamically composable multi-head attention[C]//International Conference on Machine Learning. 2024.
[31] KHAN Z A, HUSSAIN T, BAIK S W.Dual stream network with attention mechanism for photovoltaic power forecasting[J]. Applied energy, 2023, 338: 120916.