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

A Multi-step Wind Speed ​​Forecasting Method Based on Bayesian Robust Function Regression

A wind speed and function technology, applied in the field of new energy and statistical learning, can solve the problems of large error and low precision

Active Publication Date: 2020-07-31
TIANJIN UNIV +1
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to solve the two major defects existing in the existing wind speed forecasting method, which leads to the technical problem of low accuracy and large error. The present invention provides a multi-step wind speed forecasting method based on Bayesian robust function regression

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
  • A Multi-step Wind Speed ​​Forecasting Method Based on Bayesian Robust Function Regression
  • A Multi-step Wind Speed ​​Forecasting Method Based on Bayesian Robust Function Regression
  • A Multi-step Wind Speed ​​Forecasting Method Based on Bayesian Robust Function Regression

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach

[0050] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS This embodiment is a multi-step wind speed forecasting method based on Bayesian robust function regression, such as figure 1 shown, the specific steps are as follows:

[0051] 1) Data preprocessing:

[0052] The 120 5-second wind speed points in every 10 minutes are regarded as a unit and stored in MATLAB, and then all the data in each unit are averaged to obtain a 10-minute average wind speed time series, and then the multi-step is determined according to the actual situation. The predicted prediction step size, the number of low-resolution prediction inputs, and the corresponding number of high-resolution wind speed inputs;

[0053] 2) Construct a multi-step wind speed prediction model of robust function regression:

[0054] Integrate the traditional regression model and the functional regression model to construct a functional regression model that can process multi-resolution data The x, y represent the input and...

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 multi-step wind speed forecasting method based on Bayes robust function regression. The method comprises the following steps that: carrying out data preprocessing, constructing a multi-step wind speed forecasting model based on robust function regression, utilizing a variational Bayes optimization model parameter, and calculating a forecasting value according to an estimation parameter and a test set. By use of the multi-step wind speed forecasting method, various types of resolution data can be processed, a robustness effect can be performed for different practical forecasting tasks, in addition, an influence of a redundant function type variable on a final result can be lowered, accuracy is high, errors are small, and wind speed forecasting accuracy can be further improved.

Description

technical field [0001] The invention relates to the field of new energy and statistical learning, in particular to a multi-step wind speed forecasting method based on Bayesian robust function regression. Background technique [0002] At present, wind power has received more and more attention as a clean and renewable energy. The integration of large-scale wind power into the grid will alleviate the energy crisis to a certain extent, and can bring economic benefits and reduce environmental pollution. From the perspective of wind power grid integration, accurate wind speed and power forecasts are important factors to maintain the stability and safety of wind power systems. [0003] At present, there are a variety of wind speed and power forecasting methods. According to the modeling theory, these methods can be roughly divided into five categories: physical models, traditional statistical models, artificial intelligence-based forecasting methods, spatial correlation models, a...

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 Patents(China)
IPC IPC(8): G06F30/20
CPCG06F30/20
Inventor 胡清华汪运王铮
Owner TIANJIN UNIV
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