基于VMD-TCN-GRU-AM的超短期风电预测

范竞敏; 贺广林; 王新刚; 张奎; 李佩怡; 何梓秋

doi:10.19912/j.0254-0096.tynxb.2024-0291

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (6) : 538-547. DOI: 10.19912/j.0254-0096.tynxb.2024-0291

基于VMD-TCN-GRU-AM的超短期风电预测

范竞敏¹, 贺广林^1,2, 王新刚^2,3, 张奎², 李佩怡², 何梓秋²

作者信息 +

ULTRA SHORT TERM WIND POWER PREDICTION BASED ON VMD-TCN-GRU-AM

Fan Jingmin¹, He Guanglin^1,2, Wang Xin'gang^2,3, Zhang Kui², Li Peiyi², He Ziqiu²

Author information +

文章历史 +

摘要

为提升风电功率预测精度并解决单一神经网络模型在预测波动性和间歇性风电数据时的滞后性问题,提出一种融合变分模态分解（VMD）、时间卷积网络（TCN）、门控循环单元（GRU）和注意力机制（AM）的组合预测模型。模型利用VMD将原始风电数据分解为具有不同中心频率的本征模态函数（IMF）,以降低数据的随机性和波动性。然后通过TCN-GRU-AM模型对这些IMF子序列进行独立预测,TCN和GRU的组合能更好地捕捉各子序列的复杂特征和时间依赖性,AM则可增强模型对时间序列中关键时间步的识别能力。最终通过叠加预测分量重构出风电功率预测结果。实验验证显示,该模型能显著提高预测精度且有效缓解了滞后现象。

Abstract

To enhance the accuracy of wind power forecasting and solve the lag in single neural network models when predicting fluctuating and intermittent wind power data, this paper proposes a hybrid forecasting model integrating Variational Mode Decomposition （VMD）, Temporal Convolutional Networks （TCN）, Gated Recurrent Units （GRU）, and Attention Mechanism （AM）. The model employs VMD to decompose raw wind power data into Intrinsic Mode Functions （IMFs） with different central frequencies, decreasing the data's stochasticity and volatility. Subsequently, the TCN-GRU-AM model independently predicts these IMF subsequence. The combination of TCN and GRU effectively captures the complex features and temporal dependencies within each subsequence, while AM boosts the model's capacity to recognize crucial time steps in time series data. Ultimately, the predicted components are superimposed and reconstructed to yield the final wind power prediction outcome. Experimental results demonstrate that this model significantly enhances forecasting precision and effectively mitigates the lag phenomenon.

导出引用

范竞敏, 贺广林, 王新刚, 张奎, 李佩怡, 何梓秋. 基于VMD-TCN-GRU-AM的超短期风电预测[J]. 太阳能学报. 2025, 46(6): 538-547 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0291

Fan Jingmin, He Guanglin, Wang Xin'gang, Zhang Kui, Li Peiyi, He Ziqiu. ULTRA SHORT TERM WIND POWER PREDICTION BASED ON VMD-TCN-GRU-AM[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 538-547 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0291

中图分类号： TM614 TP183

参考文献

[1] 赵冬梅, 徐辰宇, 陶然, 等. 多元分布式储能在新型电力系统配电侧的灵活调控研究综述[J]. 中国电机工程学报, 2023, 43(5): 1776-1798.
ZHAO D M, XU C Y, TAO R, et al.Review on flexible regulation of multiple distributed energy storage in distribution side of new power system[J]. Proceedings of the CSEE, 2023, 43(5): 1776-1798.
[2] TEFERRA D M, NGOO L M H, NYAKOE G N. Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization[J]. Heliyon, 2023, 9(1): e12802.
[3] 韩自奋, 景乾明, 张彦凯, 等. 风电预测方法与新趋势综述[J]. 电力系统保护与控制, 2019, 47(24): 178-187.
HAN Z F, JING Q M, ZHANG Y K, et al.Review of wind power forecasting methods and new trends[J]. Power system protection and control, 2019, 47(24): 178-187.
[4] 李远征, 倪质先, 段钧韬, 等. 面向高比例新能源电网的重大耗能企业需求响应调度[J]. 自动化学报, 2023, 49(4): 754-768.
LI Y Z, NI Z X, DUAN J T, et al.Demand response scheduling of major energy-consuming enterprises based on a high proportion of renewable energy power grid[J]. Acta automatica sinica, 2023, 49(4): 754-768.
[5] LIU C Y, ZHANG X M, MEI S W, et al.Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction[J]. Applied energy, 2023, 336: 120815.
[6] 刘帅, 朱永利, 张科, 等. 基于误差修正ARMA-GARCH模型的短期风电功率预测[J]. 太阳能学报, 2020, 41(10): 268-275.
LIU S, ZHU Y L, ZHANG K, et al.Short-term wind power forecasting based on error correction ARMA-GARCH model[J]. Acta energiae solaris sinica, 2020, 41(10): 268-275.
[7] 曹俊波, 周任军, 邓学华, 等. 考虑优化ARIMA模型差分次数的风功率预测[J]. 电力系统及其自动化学报, 2019, 31(1): 105-111.
CAO J B, ZHOU R J, DENG X H, et al.Wind power forecast considering differential times of optimal ARIMA model[J]. Proceedings of the CSU-EPSA, 2019, 31(1): 105-111.
[8] 王丽婕, 刘田梦, 王勃, 等. 基于奇异值分解与卡尔曼滤波修正多位置NWP的短期风电功率预测[J]. 太阳能学报, 2022, 43(12): 392-398.
WANG L J, LIU T M, WANG B, et al.Short-term wind power prediction based on SVD and Kalman filter correction of multi-position NWP[J]. Acta energiae solaris sinica, 2022, 43(12): 392-398.
[9] 普智勇, 夏攀, 张璐, 等. 机器学习与统计方法在太阳能预报中的比较性分析[J]. 太阳能学报, 2023, 44(7): 162-167.
PU Z Y, XIA P, ZHANG L, et al.Comparative analysis of machine learning and statistical methods in solar energy prediction[J]. Acta energiae solaris sinica, 2023, 44(7): 162-167.
[10] SHARIFZADEH M, SIKINIOTI-LOCK A, SHAH N.Machine-learning methods for integrated renewable power generation: a comparative study of artificial neural networks, support vector regression, and Gaussian process regression[J]. Renewable and sustainable energy reviews, 2019, 108: 513-538.
[11] CHANDEL S S, GUPTA A, CHANDEL R, et al.Review of deep learning techniques for power generation prediction of industrial solar photovoltaic plants[J]. Solar compass, 2023, 8: 100061.
[12] 武煜昊, 王永生, 徐昊, 等. 风电输出功率预测技术研究综述[J]. 计算机科学与探索, 2022, 16(12): 2653-2677.
WU Y H, WANG Y S, XU H, et al.Survey of wind power output power forecasting technology[J]. Journal of frontiers of computer science and technology, 2022, 16(12): 2653-2677.
[13] GUO H T, PAN L, WANG J, et al.Short-term wind power prediction method based on TCN-GRU combined model[C]//2021 IEEE Sustainable Power and Energy Conference (iSPEC). Nanjing, China, 2021: 3764-3769.
[14] DONG Y C, ZHANG H L, WANG C, et al.Wind power forecasting based on stacking ensemble model, decomposition and intelligent optimization algorithm[J]. Neurocomputing, 2021, 462: 169-184.
[15] 张家军, 陈杰, 常喜强, 等. 基于EMD与模型预测控制算法的风电功率平抑[J]. 四川电力技术, 2021, 44(2): 38-42.
ZHANG J J, CHEN J, CHANG X Q, et al.Wind power fluctuation stabilization based on EMD and model predictive control algorithm[J]. Sichuan electric power technology, 2021, 44(2): 38-42.
[16] 程启明, 陈路, 程尹曼, 等. 基于EEMD和LS-SVM模型的风电功率短期预测方法[J]. 电力自动化设备, 2018, 38(5): 27-35.
CHENG Q M, CHEN L, CHENG Y M, et al.Short-term wind power forecasting method based on EEMD and LS-SVM model[J]. Electric power automation equipment, 2018, 38(5): 27-35.
[17] CHEN H P, WU H, KAN T Y, et al.Low-carbon economic dispatch of integrated energy system containing electric hydrogen production based on VMD-GRU short-term wind power prediction[J]. International journal of electrical power & energy systems, 2023, 154: 109420.
[18] NAZARI M, SAKHAEI S M.Successive variational mode decomposition[J]. Signal processing, 2020, 174: 107610.
[19] ZHANG G, XU B B, LIU H C, et al.Wind power prediction based on variational mode decomposition and feature selection[J]. Journal of modern power systems and clean energy, 2021, 9(6): 1520-1529.
[20] 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.
[21] BAI S J, KOLTER J Z, KOLTUN V, et al. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[EB/OL].2018: 1803.01271. https://arxiv.org/abs/1803.01271v2.
[22] 符杨, 任子旭, 魏书荣, 等. 基于改进LSTM-TCN模型的海上风电超短期功率预测[J]. 中国电机工程学报, 2022, 42(12): 4292-4302.
FU Y, REN Z X, WEI S R, et al.Ultra-short-term power prediction of offshore wind power based on improved LSTM-TCN model[J]. Proceedings of the CSEE, 2022, 42(12): 4292-4302.
[23] LI C S, TANG G, XUE X M, et al.Short-term wind speed interval prediction based on ensemble GRU model[J]. IEEE transactions on sustainable energy, 2020, 11(3): 1370-1380.
[24] MENG A B, CHEN S, OU Z H, et al.A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization[J]. Energy, 2022, 238: 121795.