Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Wind power generation output power prediction method based on similarity theory

A technology of output power and prediction method, which is applied in the direction of prediction, data processing applications, instruments, etc., can solve problems such as poor prediction accuracy, and achieve the effect of simple and easy method, improved prediction accuracy and high prediction accuracy

Inactive Publication Date: 2016-02-03
STATE GRID CORP OF CHINA +2
View PDF1 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method requires a wealth of meteorological knowledge and physical characteristics. If the model is rough, the prediction accuracy will be poor

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 generation output power prediction method based on similarity theory
  • Wind power generation output power prediction method based on similarity theory
  • Wind power generation output power prediction method based on similarity theory

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] Such as figure 1 As shown, the steps of the method for predicting the output power of wind power generation based on the similarity theory in this embodiment include:

[0037] 1) For the historical monitoring data of the wind power generation system and the historical meteorological data provided by the meteorological department, select the specified climate type and weather type as the feature vectors of similar time periods, and normalize the climate types respectively, and at the same time Weather type feature vectors are mapped to numeric values;

[0038] 2) Perform pattern recognition analysis on the normalized climate type eigenvectors and the weather type eigenvectors mapped to numerical values;

[0039] 3) Obtain the validity of all data classifications of climate type feature vectors according to the classification evaluation index function, and then use the data classification with the smallest validity as the classification result to form a sample set of sim...

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 generation output power prediction method based on the similarity theory. The method comprises steps of: using an appointed climate type and an appointed weather type as the characteristic vectors of similar time segments, normalizing the climate type, and mapping the characteristic vectors of the weather type into numerical value; performing mode identification analysis; acquiring the validities of all data classifications of the characteristic vectors of the climate type, using the data classification with the lowest validity as a classification result to form a sample set of the similar time segments; constructing a factor vector sample set influencing the wind power generation system output power and acquiring the similarity of different time segments; establishing a machine learning model and training the machine learning model by using the output power of the similar time segments and the sample set of the similar time segments; and predicting the output power of the time segment by using the machine learning model. The wind power generation output power prediction method is simple and feasible, and accurate in prediction.

Description

technical field [0001] The invention relates to the technical field of wind power generation, in particular to a method for predicting output power of wind power generation based on similarity theory. Background technique [0002] With the rapid development of global industrialization and the continuous growth of the world population, conventional energy sources such as oil, natural gas and coal are increasingly exhausted. The development and utilization of clean, safe and environmentally friendly renewable energy has become a common goal for human society to alleviate the growing energy shortage. Select and control the effective force of severe environmental pollution. Wind energy, as a renewable energy source, has received widespread attention in recent years. Its development speed is the fastest, its technology is relatively mature, and it is an industry with the greatest potential for large-scale development and commercialization. [0003] For the power grid, the output...

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): G06Q10/04G06Q50/06
CPCY04S10/50
Inventor 唐福顺粟时平刘桂英邓宇恩罗国才唐谟懿周臣帅
Owner STATE GRID CORP OF CHINA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com