<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">SSSD</journal-id><journal-title-group><journal-title>Scientific and Social Sustainable Development</journal-title></journal-title-group><issn>3066-8964</issn><eissn>3066-8980</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/SSSD.2026090027</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于物理特征门控注意力的长短期记忆网络的风电功率预测</title><url>https://artdesignp.com/journal/SSSD/2/9/10.61369/SSSD.2026090027</url><author>滕菲</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>2</volume><issue>9</issue><history><date date-type="pub"><published-time>2026-05-14</published-time></date></history><abstract>风力发电的波动性对电力系统的安全稳定运行构成挑战。但现有预测方法忽略物理规律，导致其在极端场景下的精度较差。因此，本文提出了一种基于物理特征门控注意力的长短期记忆网络预测方法。该方法首先通过相关性分析筛选出风速作为核心物理特征，并利用该物理特征向量融合局部时序模式与全局依赖关系。实验结果表明，所提方法在多种指标上展现出优秀的性能。</abstract><keywords>新能源预测,风力出力预测,深度学习,长短期记忆网络,注意力机制</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] 张懿夫, 张海锋, 崔运海, 等. 考虑多源数据融合的风力发电机组功率预测模型[J]. 自动化与仪器仪表,2026,(02):166-170.DOI:10.14016/j.cnki.1001-9227.2026.02.166.[2] 夏卫平, 邓艾东, 薛原, 等. 基于VMD 和多时间尺度分类预测的风电单机短期功率预测研究[J]. 太阳能学报,2025,46(12):554-563.DOI:10.19912/j.0254-0096.tynxb.2024-1466.[3] 韩健. 基于LSTM-CNN 混合预测的风电功率预测技术研究[J]. 石油石化节能与计量,2025,15(12):14-18.[4] 米云宝. 时序数据挖掘下风力发电机组输出功率预测方法[J]. 技术与市场,2026,33(03):101-104.[5] 李传栋, 张明慧, 张逸, 等. 考虑风力波动相关性的风电场超短期出力预测[J]. 太阳能学报,2025,46(11):754-763.DOI:10.19912/j.0254-0096.tynxb.2024-1299.[6] 马昕远, 伊海港. 基于CNN-LSTM-Attention 的风力发电功率预测与可解释性分析[J]. 长江信息通信,2025,38(11):17-21.DOI:10.20153/j.issn.2096-9759.2025.11.005.[7]G.Wang, L.Jia and Q.Xiao. A Hybrid Approach Based on Unequal Span Segmentation-Clustering forShort-Term Wind Power Forecasting[J]. IEEE Transactions on Power Systems, 2024, vol.39, no.1, pp.203-216.[8]Y.Wang, Q.Hu, D.Srinivasan and Z.Wang. Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data[J]. IEEE Transactions on Sustainable Energy, 2019, vol.10, no.1, pp.16-25.[9]J.Sathyaraj and V.Sankardoss. Enhancing Short-Term Wind Speed Prediction Based on Deep Learning With Ensemble Learning Model for Small Wind Turbine Applications[J]. IEEE Access, 2025, vol.13, pp.82759-82782.[10]S.Wang, J.Li, Z.Hou, Q.Meng and M.Li. Composite Model-free Adaptive Predictive Control for Wind Power Generation Based on Full Wind Speed[J]. CSEE Journal of Power and Energy Systems, 2022, vol.8, no.6, pp.1659-1669.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
