ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON INGO-SWGMN HYBRID MODEL

Fu Wenlong, Zhang Xuanrui, Zhang Hairong, Fu Yuchen, Liu Xingtao

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (5) : 133-143.

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (5) : 133-143. DOI: 10.19912/j.0254-0096.tynxb.2022-1975

ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON INGO-SWGMN HYBRID MODEL

  • Fu Wenlong1,2, Zhang Xuanrui1, Zhang Hairong3, Fu Yuchen1, Liu Xingtao1
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Abstract

In order to improve the accuracy of ultra-short-term wind speed forecasting, a hybrid ultra-short-term wind speed prediction model that incorporates variational modal decomposition (VMD), phase space reconstruction, improved northern goshawk optimization algorithm (INGO) and shared weight gated memory network (SWGMN) is proposed. First, considering that the strong volatility of wind speed can adversely affect the prediction, the wind speed time series are decomposed by VMD to obtain a series of relatively smooth subseries. Then the phase space reconstruction is performed for each subsequence component to obtain the corresponding phase space matrix. Subsequently, a shared weight gated memory network (SWGMN) is proposed for the problems of long training time and many weight parameters of long short-term memory network (LSTM), and the SWGMN is used to build a prediction model for each subseries component. Meanwhile, to improve the prediction performance of the model, an improved northern goshawk optimization algorithm (INGO) is proposed to find the optimal combination of the two hyperparameters of the SWGMN model. Finally, the predicted values of each subseries are superimposed to obtain the final wind speed prediction results. The experimental results show that the proposed method has higher prediction accuracy and efficiency compared with the traditional methods.

Key words

wind speed / forecasting / deep learning / variational mode decomposition / shared weight gated memory network / improved northern goshawk optimization algorithm

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Fu Wenlong, Zhang Xuanrui, Zhang Hairong, Fu Yuchen, Liu Xingtao. ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON INGO-SWGMN HYBRID MODEL[J]. Acta Energiae Solaris Sinica. 2024, 45(5): 133-143 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1975

References

[1] 张家安, 刘东, 刘辉, 等. 基于风速波动特征提取的超短期风速预测[J]. 太阳能学报, 2022, 43(9): 308-313.
ZHANG J A, LIU D, LIU H, et al.Ultra short term wind speed prediction based on wind speed fluctuation feature extraction[J]. Acta energiae solaris sinica, 2022, 43(9): 308-313.
[2] 赵泽妮, 云斯宁, 贾凌云, 等. 基于统计模型的短期风能预测方法研究进展[J]. 太阳能学报, 2022, 43(11): 224-234.
ZHAO Z N, YUN S N, JIA L Y, et al.Recent progress in short-term forecasting of wind energy based on statistical models[J]. Acta energiae solaris sinica, 2022, 43(11): 224-234.
[3] LIU H, CHEN C.Data processing strategies in wind energy forecasting models and applications: a comprehensive review[J]. Applied energy, 2019, 249: 392-408.
[4] 曾亮, 雷舒敏, 王珊珊, 等. 基于OVMD-SSA-DELM-GM模型的超短期风电功率预测方法[J]. 电网技术, 2021, 45(12): 4701-4712.
ZENG L, LEI S M, WANG S S, et al.Ultra-short-term wind power prediction based on OVMD-SSA-DELM-GM model[J]. Power system technology, 2021, 45(12): 4701-4712.
[5] 向玲, 邓泽奇. 基于改进经验小波变换和最小二乘支持向量机的短期风速预测[J]. 太阳能学报, 2021, 42(2): 97-103.
XIANG L, DENG Z Q.Short-term wind speed forecasting based on improved empirical wavelet transform and least squares support vector machines[J]. Acta energiae solaris sinica, 2021, 42(2): 97-103.
[6] 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.
[7] 张冬雪. 基于LSTM和ARIMA的风速时间序列预测研究[D]. 兰州: 兰州大学, 2020.
ZHANG D X.Prediction of wind speed time series based on LSTM and ARIMA[D]. Lanzhou: Lanzhou University, 2020.
[8] 唐振浩, 赵赓楠, 曹生现, 等. 基于SWLSTM算法的超短期风向预测[J]. 中国电机工程学报, 2019, 39(15): 4459-4468.
TANG Z H, ZHAO G N, CAO S X, et al.Very short-term wind direction prediction via self-tuning wavelet long-short term memory neural network[J]. Proceedings of the CSEE, 2019, 39(15): 4459-4468.
[9] 向玲, 李京蓄, 王朋鹤, 等. 基于VMD-FIG和参数优化GRU的风速多步区间预测[J]. 太阳能学报, 2021, 42(10): 237-242.
XIANG L, LI J X, WANG P H, et al.Wind speed multistep interval forecasting based on VMD-FIG and parameter-optimized GRU[J]. Acta energiae solaris sinica, 2021, 42(10): 237-242.
[10] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544.
[11] PACKARD N H, CRUTCHFIELD J P, FARMER J D, et al.Geometry from a time series[J]. Physical review letters, 1980, 45(9): 712-716.
[12] DEHGHANI M, HUBÁLOVSKÝ Š, TROJOVSKÝ P. Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems[J]. IEEE access, 2021, 9: 162059-162080.
[13] 方军强. 基于变分模态分解的船用齿轮箱故障诊断研究[D]. 武汉: 武汉理工大学, 2017.
FANG J Q.Research on fault diagnosis of marine gearbox based on variational mode decomposition[D]. Wuhan: Wuhan University of Technology, 2017.
[14] 陆振波, 蔡志明, 姜可宇. 基于改进的C-C方法的相空间重构参数选择[J]. 系统仿真学报, 2007, 19(11): 2527-2529, 2538.
LU Z B, CAI Z M, JIANG K Y.Determination of embedding parameters for phase space reconstruction based on improved C-C method[J]. Journal of system simulation, 2007, 19(11): 2527-2529, 2538.
[15] 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.
[16] FANTA H, SHAO Z W, MA L Z.SiTGRU: Single-tunnelled gated recurrent unit for abnormality detection[J]. Information sciences, 2020, 524: 15-32.
[17] ZHANG Z D, YE L, QIN H, et al.Wind speed prediction method using shared weight long short-term memory network and gaussian process regression[J]. Applied energy, 2019, 247: 270-284.
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