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基于流匹配模型的海上风能资源时序超分辨率评估方法OA

Temporal Super-resolution Offshore Wind Energy Resources Assessment Method Based on Flow Matching Model

中文摘要英文摘要

海上风电作为清洁能源的重要组成部分发展迅速,但在海上风能资源的开发过程中,受限于海洋气象监测手段不足及地球系统模式(earth system model,ESM)的计算复杂度,实现高时间分辨率、长时间尺度的海上风能资源评估存在挑战.针对该问题,该文提出一种时序流匹配模型,融合历史气象监测数据对ESM模拟数据进行偏差修正与时序分辨率提升.该方法采用微分方程建模低分辨率数据至高分辨率数据的连续变换路径,并拟合概率分布变换过程中的速度场,实现对目标数据分布的建模.算例选用ERA5再分析数据,结合海上气象浮标观测数据开展仿真验证.结果表明,所提方法能有效模拟亚小时级的风速波动特性,且能够刻画风能资源的不确定性,可为海上风能资源评估提供新的技术路径.

Offshore wind power,as a key component of clean energy,has experienced rapid development in recent years.However,achieving high-temporal resolution and long-term assessments of offshore wind resources remains challenging due to limitations in marine meteorological monitoring capabilities and the high computational complexity of Earth System Model(ESM).To address this issue,this paper proposes a temporal flow matching model that integrates historical meteorological monitoring data to perform bias correction and temporal resolution enhancement on ESM simulations.The method employs differential equations to model the continuous transformation path from low-resolution to high-resolution data and fits the velocity field during the probability distribution transformation to characterize the target data distribution.Using ERA5 reanalysis data combined with offshore meteorological buoy observations for simulation and validation,the results demonstrate that the proposed method can effectively simulate sub-hourly wind speed fluctuations and capture the uncertainty in wind energy resources.This work provides a novel technical pathway for offshore wind resource assessment.

刘楠;董骁翀;宋赟祯;陈来军;梅生伟

新型电力系统运行与控制全国重点实验室(清华大学),北京市 海淀区 100084新型电力系统运行与控制全国重点实验室(清华大学),北京市 海淀区 100084青海大学土木水利学院,青海省 西宁市 810016青海大学能源与电气工程学院,青海省 西宁市 810016新型电力系统运行与控制全国重点实验室(清华大学),北京市 海淀区 100084

信息技术与安全科学

海上风电流匹配模型风能资源超分辨率偏差校正

offshore wind powerflow matching modelwind energy resourcesuper-resolutionbias correct

《中国电机工程学报》 2026 (18)

6597-6606,中插2,11

国家自然科学基金项目(U22A20224)国家资助博士后研究人员计划(BX20250414). Project Supported by National Natural Science Foundation of China(U22A20224)China Postdoctoral Science Foundation(BX20250414).

10.13334/j.0258-8013.pcsee.252015

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