Method and system for predicting a day-ahead wind power of wind farms

Patent metadata

Field	Value
Publication number	US-2023136352-A1
Application number	US-202217652681-A
Country	US
Kind code	A1
Filing date	Feb 26, 2022
Priority date	Oct 29, 2021
Publication date	May 4, 2023
Grant date	—

Abstract

A method for predicting a day-ahead wind power of wind farms, comprising: constructing a raw data set based on a correlation between the to-be-predicted daily wind power, the numerical weather forecast meteorological feature and a historical daily wind power; obtaining a clustered data set and performing k-means clustering, obtaining a raw data set with cluster labels, and generating massive labeled scenes based on robust auxiliary classifier generative adversarial networks; determining the cluster label category of the to-be-predicted day based on the known historical daily wind power and numerical weather forecast meteorological feature, and screening out multiple scenes with high similarity to the to-be-predicted daily wind power based on the cluster label category; and obtaining the prediction results of the to-be-predicted daily wind power at a plurality of set times based on an average value, an upper limit value and a lower limit value of the to-be-predicted daily wind power.

First claim

What is claimed is:

1 . A method for predicting a day-ahead wind power of wind farms, comprising:
constructing a raw data set containing a numerical weather forecast meteorological feature and a to-be-predicted daily wind power based on a correlation between the to-be-predicted daily wind power, the numerical weather forecast meteorological feature, and a historical daily wind power; removing the to-be-predicted daily wind power in the raw data set, obtaining a clustered data set and performing k-means clustering, obtaining a raw data set with cluster labels, and generating massive labelled scenes based on robust auxiliary classifier generative adversarial networks; determining the cluster label category of the to-be-predicted day based on the known historical daily wind power and numerical weather forecast meteorological feature, and screening out multiple scenes with high similarity to the to-be-predicted daily wind power from the massive labelled scenes based on the cluster label category, forming a similar scene set; and obtaining the point prediction and interval prediction results of the to-be-predicted daily wind power at a plurality of set times based on an average value, an upper limit value and a lower limit value of the to-be-predicted daily wind power in the similar scene set.

2 . The method for predicting a day-ahead wind power of wind farms of claim 1 , wherein the step of based on a correlation between the to-be-predicted daily wind power, the numerical weather forecast meteorological feature and a historical daily wind power comprises:
selecting a wind speed, a wind direction, a temperature, a humidity and a pressure as a first primary selection feature of the to-be-predicted daily wind power; selecting a historical daily wind power with an absolute value of the Pearson correlation coefficient greater than a preset threshold as a second primary selection feature of the to-be-predicted daily wind power based on the correlation between each historical daily wind power and the to-be-predicted daily wind power; calculating the Pearson correlation coefficient between the to-be-predicted daily wind power and the first primary selection feature and the second primary selection feature; and comparing the absolute value of the Pearson correlation coefficient with the preset threshold to determine the correlation between the to-be-predicted daily wind power and the first primary selection feature and the second primary selection feature.

3 . The method for predicting a day-ahead wind power of wind farms of claim 2 , wherein the calculation formula of the Pearson correlation coefficient is as follows:

P
C
C

x
,

y
i

=
1
−

∑

k
=
1

n

x
k

−

x
¯

y

i
k

−

y
i

¯

∑

k
=
1

n

x
k

−

x
¯

∑

k
=
1

n

y

i
k

−

y
i

¯

wherein, x is the to-be-predicted daily wind power, y i is an impact feature of the to-be-predicted daily wind power; x k and y ik are the k-th data in x and y i ; x̅ and y̅ i are the average values of x and y i ; k and n are positive integers.

4 . The method for predicting a day-ahead wind power of wind farms of claim 3 , wherein the step of constructing a raw data set containing a numerical weather forecast meteorological feature and a to-be-predicted daily wind power comprises:
removing the primary selection features with very weak correlation or without correlation based on the correlation determination result between the to-be-predicted daily wind power and the first primary selection feature and the second primary selection feature; taking remaining primary selection features as the impact features of the to-be-predicted daily wind power, and normalizing the features predicted by the data set corresponding to the impact features; integrating the normalized features with the to-be-predicted daily wind power in turn based on a preset arrangement sequence, and forming a to-be-predicted daily raw data; and constructing an annual raw data sample based on each to-be-predicted daily raw data and selecting several raw data from the annual raw data sample randomly to construct a raw data set.

5 . The method for predic

Assignees

• Economic And Tech Research Institute Of State Grid Liaoning Electric Power Co Ltd
• State Grid Corp China
• Univ Northeast Electric Power

Inventors

• Pan Xiao
• Zhang Mingli
• Zhao Lin
• Zhang Na
• Song Zhuoran
• Huang nantian
• Gao Jing
• Lv Xuming
• Li Hua
• Cheng Mengzeng
• Ji Xing
• Shang Wenying
• Hou Yixin
• Yang Suo
• Yang Bo
• Liu Yutong
• Man Linkun
• Xu Xilin
• Yang Haifeng
• Yang Fangyuan
• Liu Kai
• Li Jinqi
• Wang Zongyuan

Classifications

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• F05B2260/8211

  of the weather · CPC title

• F05B2270/335

  Output power or torque · CPC title

• F03D17/00 Primary

  Monitoring or testing of wind motors, e.g. diagnostics (testing during commissioning of wind motors F03D13/30) · CPC title

• G06Q50/06

  Energy or water supply · CPC title