SPATIO-TEMPORAL WIND SPEED FORECASTING BASED ON HYBRID DEEP LEARNING ALGORITHM

Gui Xiangquan; Meng Panlong; Sun Linhua; Qin Sanjie; Liu Jinghong

doi:10.19912/j.0254-0096.tynxb.2023-1954

PDF(2634 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (3) : 668-678. DOI: 10.19912/j.0254-0096.tynxb.2023-1954

SPATIO-TEMPORAL WIND SPEED FORECASTING BASED ON HYBRID DEEP LEARNING ALGORITHM

Gui Xiangquan¹, Meng Panlong¹, Sun Linhua², Qin Sanjie³, Liu Jinghong¹

Author information +

History +

Abstract

The accuracy of wind speed forecasting is crucial for the efficient operation and dispatch of power grid systems. To tackle the complex spatio-temporal correlations and nonlinearity of wind speed, a novel hybrid deep learning model is proposed. Firstly, a secondary decomposition method is employed to decompose the input sequence into intrinsic mode functions （IMFs） with different frequency vibration modes. Graph convolutional network （GCN） and a bidirectional long short-term memory （BiLSTM） network are used to predict the high-frequency components. An adaptive graph spatio-temporal transformer network （ASTTN） is employed to predict the low-frequency components, fully considering the spatio-temporal correlation of the input sequence. Finally, the high-frequency and low-frequency components are combined and superimposed to obtain the ultimate prediction results. The model is applied to wind speed forecasting in a wind farm in Gansu Province, and experimental results demonstrate that the proposed hybrid deep learning model effectively enhances the accuracy of wind speed forecasting.

Key words

wind speed / forecasting / deep learning / graph convolutional network / bidirectional long short-term memory network / adaptive graph spatio-temporal transformer

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Gui Xiangquan, Meng Panlong, Sun Linhua, Qin Sanjie, Liu Jinghong. SPATIO-TEMPORAL WIND SPEED FORECASTING BASED ON HYBRID DEEP LEARNING ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(3): 668-678 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1954

References

[1] ZHANG S, CHEN Y, XIAO J, et al.Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism[J]. Renewable energy, 2021, 174: 688-704.
[2] XU X, NIU D, XIAO B, et al.Policy analysis for grid parity of wind power generation in China[J]. Energy policy, 2020, 138: 111225.
[3] 王俊, 李霞, 周昔东, 等. 基于VMD和LSTM的超短期风速预测[J]. 电力系统保护与控制, 2020, 48(11): 45-52.
WANG J, LI X, ZHOU X D, et al.Ultra-short-term wind speed prediction based on VMD-LSTM[J]. Power system protection and control, 2020, 48(11): 45-52.
[4] WANG C, LIU Z, WEI H, et al.Hybrid deep learning model for short-term wind speed forecasting based on time series decomposition and gated recurrent unit[J]. Complex system modeling and simulation, 2021, 1(4): 308-321.
[5] 向玲, 刘佳宁, 苏浩, 等. 基于CEEMDAN二次分解和LSTM的风速多步预测研究[J]. 太阳能学报, 2022, 43(8): 334-339.
XIANG L, LIU J N, SU H, et al.Research on multi-step wind speed forecast based on CEEMDAN secondary decomposition and LSTM[J]. Acta energiae solaris sinica, 2022, 43(8): 334-339.
[6] LI Z L, YU J, ZHANG X L, et al.A Multi-Hierarchical attention-based prediction method on time series with spatio-temporal context among variables[J]. Physica A: statistical mechanics and its applications, 2022, 602: 127664.
[7] NGUYEN T H T, PHAN Q B. Hourly day ahead wind speed forecasting based on a hybrid model of EEMD, CNN-Bi-LSTM embedded with GA optimization[J]. Energy reports, 2022, 8: 53-60.
[8] HAN Y, MI L, SHEN L, et al.A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting[J]. Applied energy, 2022, 312: 118777.
[9] 臧海祥, 张越, 程礼临, 等. 基于ICEEMDAN-LSTM和残差注意力的短期太阳辐照度预测[J]. 太阳能学报, 2023, 44(12): 175-181.
ZANG H X, ZHANG Y, CHENG L L, et al.Short term solar radiation forecasting based on ICEEMDAN-LSTM and residual attention[J]. Acta energiae solaris sinica, 2023, 44(12): 175-181.
[10] ZHOU F, HUANG Z, ZHANG C.Carbon price forecasting based on CEEMDAN and LSTM[J]. Applied energy, 2022, 311: 118601.
[11] 王维高, 魏云冰, 滕旭东. 基于VMD-SSA-LSSVM的短期风电预测[J]. 太阳能学报, 2023, 44(3): 204-211.
WANG W G, WEI Y B, TENG X D.Short-term wind power forecasting based on VMD-SSA-LSSVM[J]. Acta energiae solaris sinica, 2023, 44(3): 204-211.
[12] 蒋玉英, 陈心雨, 李广明, 等. 图神经网络及其在图像处理领域的研究进展[J]. 计算机工程与应用, 2023, 59(7): 15-30.
JIANG Y Y, CHEN X Y, LI G M, et al.Graph neural network and its research progress in field of image processing[J]. Computer engineering and applications, 2023, 59(7): 15-30.
[13] 万升, 杨健, 宫辰. 基于图神经网络的高光谱图像分类研究进展[J]. 电子学报, 2023, 51(6): 1687-1709.
WAN S, YANG J, GONG C.Advances of hyperspectral image classification based on graph neural networks[J]. Acta electronica sinica, 2023, 51(6): 1687-1709.
[14] 陈晋音, 熊海洋, 马浩男, 等. 基于对比学习的图神经网络后门攻击防御方法[J]. 通信学报, 2023, 44(4): 154-166.
CHEN J Y, XIONG H Y, MA H N, et al.CLB-Defense: based on contrastive learning defense for graph neural network against backdoor attack[J]. Journal on communications, 2023, 44(4): 154-166.
[15] KIM T, VECCHIETTI L F, CHOI K, et al.Machine learning for advanced wireless sensor networks: a review[J]. IEEE sensors journal, 2021, 21(11): 12379-12397.
[16] 张程, 林谷青, 匡宇. 基于MEEMD-QUATRE-BILSTM的短期光伏出力区间预测[J]. 太阳能学报, 2023, 44(11): 40-54.
ZHANG C, LIN G Q, KUANG Y.Short-term PV output interval prediction based on MEEMD-QUATRE-BILSTM[J]. Acta energiae solaris sinica, 2023, 44(11): 40-54.
[17] JOSEPH L P, DEO R C, PRASAD R, et al.Near real-time wind speed forecast model with bidirectional LSTM networks[J]. Renewable energy, 2023, 204: 39-58.
[18] FENG A, TASSIULAS L.Adaptive graph spatial-temporal transformer network for traffic forecasting[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Atlanta, USA, 2022: 3933-3937.
[19] WU Z H, PAN S R, LONG G D, et al.Graph WaveNet for deep spatial-temporal graph modeling[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao, China, 2019: 1907-1913.