利用空间相关性的超短期风速预测

CHEN Ning

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利用空间相关性的超短期风速预测

更新时间：2025-08-13

- 利用空间相关性的超短期风速预测
- Automation of Electric Power Systems Vol. 41, Issue 12, Pages: 124-130(2017)
- 作者机构：
  
  东南大学电气工程学院,江苏省,南京市,210096
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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CHEN Ning. [J]. Automation of electric power systems, 2017, 41(12): 124-130.
DOI：

CHEN Ning. [J]. Automation of electric power systems, 2017, 41(12): 124-130. DOI：

摘要

风速的空间相关性有助于提高其预测质量

特别是在风速突变的情况下。将“离线分类建模

在线匹配模型”的预测思路应用到利用空间相关性的超短期风速预测之中:通过历史数据的时序分析

识别其中各风电场风速存在空间相关性的时段;按其时序特征及其他的条件特征

将观察时窗内的风速序列划分为不同演化形态的样本子集;在离线环境下

分别根据各类形态的训练样本子集优化其专用的预测模型及参数;在线应用时

则根据当下窗口内风速序列的演化形态及相关的条件特征

按匹配所得模型及参数

根据参考风电场的实测数据预测目标风电场的风速。以实际的历史数据验证了所述方法的有效性。

Abstract

Spatial correlation of wind speed is helpful to improving its prediction quality

especially when there are sudden changes of wind speed. A new method for ultra-short term wind speed prediction based on the idea of “offline modeling by classification

and online feature matching for model selection” is proposed. By analyzing time series among historical data

the time segments having spatial correlation in different wind farms are identified. The time segments of wind speed in the current time window are divided into sample subsets with different evolution patterns according to the features of time series and other external conditions. Prediction models and corresponding parameters for different patterns are optimized offline based on their sample subsets

respectively. While for online application

prediction models and the corresponding parameters are selected by feature matching

according to evolution patterns and other external conditions in the current time window. Finally

a case study using actual historical data is presented to validate the effectiveness of the proposed method. This work is supported by the State Key Program of National Natural Science Foundation of China(No. 61533010)

NSFC-NRCT(Sino-Thai)Cooperation Research Project(No. 51561145011)and State Grid Corporation of China.

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