RESEARCH ON ULTRA SHORT TERM WIND POWER FORECASTING BASED ON VMD-LSTM-MULTICONV-SELFATTENTION

Ren Haoqin; Lian Weichang; Qi Fengwu; Wang Limin; Zhao Shuhan; Liu Guangchen

doi:10.19912/j.0254-0096.tynxb.2025-0170

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (6) : 394-402. DOI: 10.19912/j.0254-0096.tynxb.2025-0170

RESEARCH ON ULTRA SHORT TERM WIND POWER FORECASTING BASED ON VMD-LSTM-MULTICONV-SELFATTENTION

Ren Haoqin¹, Lian Weichang², Qi Fengwu³, Wang Limin¹, Zhao Shuhan¹, Liu Guangchen¹

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Abstract

To enhance the accuracy of ultra-short-term wind power forecasting and support efficient power system dispatch, this study proposes an integrated multi-algorithm forecasting model. The variational mode decomposition （VMD） is firstly applied to suppress noise and reconstruct the original power sequence. In the modeling stage, a long short-term memory （LSTM） network is adopted to capture temporal dependencies, followed by a multi-layer convolutional neural network （CNN） to extract local features. A Self Attention mechanism is further incorporated to dynamically focus on critical time steps, resulting in a collaborative multi-module forecasting framework. To evaluate the performance of the proposed model, ablation studies, comparative experiments, and seasonal transfer tests were conducted using data from a wind farm in Shandong Province, China. The results show that, compared to baseline models, the proposed model reduces the mean absolute error （MAE） by 23.5% and the root mean square error （RMSE） by 20%, highlighting the advantages of each module in modeling complex temporal patterns. Additional validations in cross-regional （wind farms in Central and Western China） and cross-energy （photovoltaic plants） scenarios further demonstrate the model's strong generalization capability.

Key words

convolutional neural network / Self-Attention mechanism / wind power prediction / gradient boosting regression algorithm / variational mode decomposition / wind power

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Ren Haoqin, Lian Weichang, Qi Fengwu, Wang Limin, Zhao Shuhan, Liu Guangchen. RESEARCH ON ULTRA SHORT TERM WIND POWER FORECASTING BASED ON VMD-LSTM-MULTICONV-SELFATTENTION[J]. Acta Energiae Solaris Sinica. 2026, 47(6): 394-402 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0170

References

[1] 刘德澳, 董明刚, 叶威, 等. 基于图神经网络的非均匀风电场功率预测方法[J]. 太阳能学报, 2023, 44(11): 279-286.
LIU D A, DONG M G, YE W, et al.Power prediction method of non-uniform wind farm layout based on graph neural network[J]. Acta energiae solaris sinica, 2023, 44(11): 279-286.
[2] 荆志宇, 李培强, 林文婷. 结合贝叶斯优化及通道注意力的双端优化时序式风电功率预测模型[J]. 电力系统及其自动化学报, 2024, 36(8): 39-47, 59.
JING Z Y, LI P Q, LIN W T.Double-side optimized time-series wind power prediction model combining Bayesian optimization and channel attention[J]. Proceedings of the CSU-EPSA, 2024, 36(8): 39-47, 59.
[3] 唐清苇, 向月, 代佳琨, 等. 基于CNN-LSTM的风电场发电功率迁移预测方法[J]. 工程科学与技术, 2024, 56(2): 91-99.
TANG Q W, XIANG Y, DAI J K, et al.Wind farm power transfer forecasting method based on CNN-LSTM[J]. Advanced engineering sciences, 2024, 56(2): 91-99.
[4] 琚垚, 祁林, 刘帅. 基于改进乌鸦算法和ESN神经网络的短期风电功率预测[J]. 电力系统保护与控制, 2019, 47(4): 58-64.
JU Y, QI L, LIU S.Short-term wind power forecasting based on improved crow search algorithm and ESN neural network[J]. Power system protection and control, 2019, 47(4): 58-64.
[5] 李润金, 李丽霞. 基于LSTM神经网络的风电功率预测研究[J]. 沈阳工程学院学报(自然科学版), 2023, 19(3): 14-18.
LI R J, LI L X.Wind power prediction based on LSTM neural network[J]. Journal of Shenyang Institute of Engineering (natural science), 2023, 19(3): 14-18.
[6] 薛阳, 王琳, 王舒, 等. 一种结合CNN和GRU网络的超短期风电预测模型[J]. 可再生能源, 2019, 37(3): 456-462.
XUE Y, WANG L, WANG S, et al.An ultra-short-term wind power forecasting model combined with CNN and GRU networks[J]. Renewable energy resources, 2019, 37(3): 456-462.
[7] 郭凯杰, 耿光超, 江全元, 等. 计及超短期预测误差的风储系统跟踪计划出力控制策略[J]. 太阳能学报, 2023, 44(2): 326-333.
GUO K J, GENG G C, JIANG Q Y, et al.Output control strategy of wind storage system tracking plan considering ultra short term prediction error[J]. Acta energiae solaris sinica, 2023, 44(2): 326-333.
[8] 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.
[9] 王献志, 曾四鸣, 周雪青, 等. 基于XGBoost联合模型的光伏发电功率预测[J]. 太阳能学报, 2022, 43(4): 236-242.
WANG X Z, ZENG S M, ZHOU X Q, et al.Power forecast of photovoltaic generation based on XGBoost combined model[J]. Acta energiae solaris sinica, 2022, 43(4): 236-242.
[10] 胡云峰. 基于短期风电功率预测的桨距角优化研究[D].吉林:东北电力大学,2023,1-64.
HU Y F.Research on pitch angle optimization based on short-term wind power prediction[D]. Jilin: Northeast Electric Power University, 2023,1-64.
[11] 钱勇生, 邵洁, 季欣欣, 等. 基于LSTM-Attention网络的短期风电功率预测[J]. 电机与控制应用, 2019, 46(9): 95-100.
QIAN Y S, SHAO J, JI X X, et al.Short-term wind power forecasting based on LSTM-attention network[J]. Electric machines & control application, 2019, 46(9): 95-100.
[12] 杨茂, 黄宾阳, 江博, 等. 基于卡尔曼滤波和支持向量机的风电功率实时预测研究[J]. 东北电力大学学报, 2017, 37(2): 45-51.
YANG M, HUANG B Y, JIANG B, et al.Real-time prediction for wind power based on Kalman filter and suport vector mahines[J]. Journal of Northeast Dianli University, 2017, 37(2): 45-51.
[13] 黄万超. 机器学习技术在风力与光伏发电系统短期功率预测中的应用与比较研究[J]. 现代工业经济和信息化, 2024, 14(10): 137-138.
HUANG W C.Application and comparative study of machine learning techniques in short-term power prediction of wind and photovoltaic power generation systems[J]. Modern industrial economy and informationization, 2024, 14(10): 137-138.
[14] ZHOU Y, RAVEY A, PERA M C.Real-time predictive energy management for fuel cell electric vehicles[C]//2021 IEEE Transportation Electrification Conference & Expo (ITEC). Chicago, IL, USA, 2021: 142-147.
[15] SHANG Z H, CHEN Y H, LAI D K, et al.A novel interpretability machine learning model for wind speed forecasting based on feature and sub-model selection[J]. Expert systems with applications, 2024, 255: 124560.
[16] XU H Y, CHANG Y Q, ZHAO Y, et al.A hybrid model for multi-step wind speed forecasting based on secondary decomposition, deep learning, and error correction algorithms[J]. Journal of intelligent & fuzzy systems, 2021, 41(2): 3443-3462.
[17] BLFGEH A, ALKHUDHAYR H.A machine learning-based sustainable energy management of wind farms using Bayesian recurrent neural network[J]. Sustainability, 2024, 16(19): 8426.
[18] KHODAYAR M, KAYNAK O, KHODAYAR M E.Rough deep neural architecture for short-term wind speed forecasting[J]. IEEE transactions on industrial informatics, 2017, 13(6): 2770-2779.
[19] NIKSA-RYNKIEWICZ T, STOMMA P, WITKOWSKA A, et al.An intelligent approach to short-term wind power prediction using deep neural networks[J]. Journal of artificial intelligence and soft computing research, 2023, 13(3): 197-210.
[20] CHEN Y B, XU J J.Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition[J]. Scientific data, 2022, 9: 577.