Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

An ultra-short-term wind power forecasting method based on small wavelength short-term memory network

A wind power prediction and short-term memory technology, applied in prediction, instrument, character and pattern recognition, etc., can solve the problems of reduced training efficiency, complicated model structure, etc., and achieve good prediction results

Active Publication Date: 2019-01-15
HOHAI UNIV
View PDF5 Cites 41 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in actual operation, if there are too many selected features or influencing factors, the structure of the predicted model may be complicated and the training efficiency will be reduced. The prediction accuracy is very important

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
  • An ultra-short-term wind power forecasting method based on small wavelength short-term memory network
  • An ultra-short-term wind power forecasting method based on small wavelength short-term memory network
  • An ultra-short-term wind power forecasting method based on small wavelength short-term memory network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0017] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0018] Such as figure 1 As shown, the ultra-short-term wind power prediction method based on the small-wavelength short-term memory network of the present invention comprises the following steps:

[0019] 1) Analyze and study wind power data, extract features closely related to wind power data, collect historical data of wind farms, and obtain a training sample set; wherein, the extracted feature information includes: 20 wind power values ​​x before the current moment 1 ,x 2 ...,x 20 As the inp...

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 an ultra-short-term wind power prediction method based on a small wavelength short-term memory network, which generates input variables according to historical data and takes the corresponding wind power historical data as output to obtain a training sample. Using wavelet analysis method to decompose the training samples into four wavelet samples, and using short-term and long-term memory network model to train the four wavelet samples respectively, the small-wavelength short-term memory network prediction model after training is obtained. According to the actual data of the four wavelet samples at the time to be predicted, the test input data are generated and input to the prediction model, and the output is the ultra-short-term wind power prediction value at the time to be predicted. The invention combines wavelet analysis method with long-term and short-term memory depth network, can realize data stabilization processing and depth learning at the same time, improves prediction accuracy and enhances model generalization ability.

Description

technical field [0001] The invention belongs to the technical field of new energy consumption, and in particular relates to an ultra-short-term wind power prediction method for predicting wind power. Background technique [0002] Nowadays, the decrease of fossil energy and the gradual increase of human demand for energy have become the main contradiction in the energy field. In order to solve the energy problem, human beings need to vigorously develop renewable energy, such as clean wind resources and solar energy resources. Due to the strong randomness, intermittency and volatility of resources, wind and light curtailment of renewable energy is still very serious. The forecasting research of renewable energy has become one of the key technologies for new energy consumption. With the advent of the era of artificial intelligence, more and more fields have successfully introduced advanced intelligent algorithms. For new energy forecasting research, it is even more necessary t...

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): G06Q10/04G06Q50/06G06K9/62
CPCG06Q10/04G06Q50/06G06F18/214
Inventor 孙永辉王朋翟苏巍候栋宸武小鹏王义吕欣欣周衍张宇航钟永洁陈凯夏响张闪铭
Owner HOHAI 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
Eureka Blog
Learn More
PatSnap group products