Wind power non-parametric interval prediction method based on self-adaptive double-layer optimization

A wind power, two-layer optimization technology, applied in forecasting, complex mathematical operations, data processing applications, etc., can solve problems such as improving the robust operation of power systems and controlling potential costs

Active Publication Date: 2019-11-08
ZHEJIANG UNIV
View PDF2 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For the probability distribution of wind power with asymmetrical and heavy-tailed characteristics, the central prediction interval is usually conservative in the width of the interval, which increases the potential cost of robust operation and control of the power system

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Wind power non-parametric interval prediction method based on self-adaptive double-layer optimization
  • Wind power non-parametric interval prediction method based on self-adaptive double-layer optimization
  • Wind power non-parametric interval prediction method based on self-adaptive double-layer optimization

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0053] The present invention will be further described below in conjunction with the accompanying drawings and implementation examples.

[0054] (1) First obtain the training set data where x t is the explanatory variable, y t is the target variable, and for the short-term prediction of wind power within 3 hours, the historical power data can be used as the explanatory variable;

[0055] (2) Set the nominal confidence of the prediction interval to 100(1-β)%; for example figure 1 As shown, the weight vector and bias from the input layer to the hidden layer of the extreme learning machine are randomly given I and H are the number of neurons in the input layer and hidden layer of the extreme learning machine, respectively;

[0056] (3) Establish a non-parametric interval prediction model of wind power based on adaptive double-layer optimization:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064] where h t =[ψ(1 ,x 1 >+b 1 ) … ψ(1...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a wind power non-parametric interval prediction method based on self-adaptive double-layer optimization, and belongs to the field of renewable energy probability prediction. According to the method, an extreme learning machine and quantile regression are combined to carry out modeling on a prediction interval to form a lower-layer optimization problem; and the quantile level corresponding to the prediction interval is adaptively adjusted by taking the interval sharpness as a target to form an upper-layer optimization problem. Efficient and reliable training of the prediction model is realized by using a primal-dual interior point algorithm. The method does not need to depend on the priori hypothesis of wind power probability distribution, breaks through the centralsymmetry limitation of the traditional probability prediction on the interval quantile level, remarkably improves the reliability and sharpness of the prediction interval, and provides important reference for the operation and control of a high-proportion wind power system.

Description

technical field [0001] The invention relates to a non-parametric interval prediction method of wind power based on self-adaptive double-layer optimization, which belongs to the field of probability prediction of renewable energy. Background technique [0002] At present, the energy structure with fossil fuels as the main primary energy has caused problems such as climate warming, air pollution, and overexploitation. Because wind energy is clean, economical, renewable, and suitable for large-scale development, countries around the world are vigorously developing wind power The total installed capacity of wind power has increased year by year. However, with the continuous improvement of wind power penetration, the randomness, volatility and intermittent characteristics of wind power are difficult to accurately quantify, and the impact on the power system is becoming more and more significant, threatening the safe and stable operation of the power system. Therefore, accurate w...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06F17/11G06N20/00
CPCG06Q10/04G06Q50/06G06F17/11G06N20/00Y04S10/50Y02E40/70
Inventor 万灿赵长飞宋永华
Owner ZHEJIANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap