太阳能学报

基于VMD-LSTM-MultiConv-SelfAttention的超短期风电功率预测研究

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太阳能学报 | 2026,47(6): 394-402

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太阳能学报 2026 , 47 (6) : 394 -402

基于VMD-LSTM-MultiConv-SelfAttention的超短期风电功率预测研究

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任浩琴¹, 连维畅², 亓凤梧³, 王立敏¹, 赵树涵¹, 刘广臣¹

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

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摘要

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为提高风电功率预测精度同时确保电力系统的高效调度,提出一种多算法集成的超短期风电功率预测模型,通过变分模态分解（VMD）对功率序列进行噪声抑制与重构。在模型构建阶段,利用LSTM捕捉时序依赖,其后连接多层卷积神经网络提取局部特征并引入自注意力机制动态聚焦关键时间步,形成多模块协同的预测框架。为检验模型的实效,以山东省某风电场为核心开展消融、对比及季节迁移实验。结果显示：相较于基准模型,该模型的平均绝对误差MAE降低23.5%,均方根误差RMSE减少20%,可凸显各模块在捕捉复杂时序特征中的优势。进一步验证表明,模型在跨区域（中西部风电场）与跨能源（光伏电站）场景中均表现优异,凸显其强泛化能力。

关键词

卷积神经网络 / 自注意力机制 / 风电功率 / 梯度提升回归算法 / 变分模态分解 / 风电

Abstract

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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

引用本文

任浩琴, 连维畅, 亓凤梧, 王立敏, 赵树涵, 刘广臣. 基于VMD-LSTM-MultiConv-SelfAttention的超短期风电功率预测研究. 太阳能学报, 2026 , 47 (6) : 394 -402 . DOI: 10.19912/j.0254-0096.tynxb.2025-0170

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

基金

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山东省重点研发计划（2024LZGCQY012）; 国家级大学生创新创业训练项目（202410451008）

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2026年第47卷第6期

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

接收时间：2025-01-24
首发时间：2026-07-17

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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