多点数值天气预报风速和辐照度集中式修正方法研究
摘要：数值天气预报(numerical weather prediction，NWP)修正是提升风光功率预测精度的关键技术之一，但目前鲜有对NWP辐照度修正的研究，同时现有的NWP风速修正方法大多只考虑单一位置，忽略了风速间的时空耦合特性，影响修正效果。针对这一问题，提出了考虑区域风光资源时空相关性的多点NWP风速和辐照度集中式修正方法。以区域内多个风电场和光伏电站的实测风速、辐照度数据为修正模型的学习目标，建立基于注意力神经网络的多点NWP集中修正模型，同时修正多个具有一定相关性的场站级NWP数据。结合某区域8个风电场和7个光伏电站的NWP数据和历史风速/辐照度数据，对所提方法进行验证，结果表明，相比于传统的单点NWP修正方法，所提方法能够有效提高NWP精度。
Abstract:Numerical weather prediction (NWP) correction is one of the key technologies to improve the accuracy of wind and solar power forecasting. However, there are few researches on the correction of NWP irradiance. At the same time, most of the existing NWP wind speed correction methods only consider a single location, ignoring the spatio-temporal coupling of wind speed, which affects the correction effect. Aiming at this problem, this paper proposed an integrated correction method for multi-point NWP wind speed and irradiance considering the spatial and temporal correlation of regional wind and solar resources. Taking the measured wind speed and irradiance data of multiple wind farms and photovoltaic power plants in the region as the learning objective of the correction model, an integrated NWP correction model based on attentional neural network was established to simultaneously correct multiple station-level NWP data with certain correlation. The proposed method was verified by the NWP data and historical wind speed / irradiance data of 8 wind farms and 7 photovoltaic power plants in a region. The calculation results show that compared with the traditional single-point NWP correction method, the proposed method can effectively improve the accuracy of NWP.
标题：多点数值天气预报风速和辐照度集中式修正方法研究
title:Integrated Correction Method of Multi-point Numerical Weather Prediction Wind Speed and Irradiance
作者：张永蕊, 阎洁, 林爱美, 韩爽, 刘永前
authors:Yongrui ZHANG, Jie YAN, Aimei LIN, Shuang HAN, Yongqian LIU
关键词：风电场,光伏电站,时空相关性,数值天气预报(NWP),功率预测,注意力神经网络,
keywords:wind farm,photovoltaic power plant,spatial and temporal correlation,numerical weather prediction (NWP),power forecasting,attentional neural network,
发表日期：2022-04-30
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