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

Improved-support-vector-machine-based power load prediction correction method

A support vector machine and power load technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as inability to accurately reflect current and future changes in power demand, power demand development trends, and low accuracy of power load

Inactive Publication Date: 2017-02-01
STATE GRID CORP OF CHINA +2
View PDF0 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problems that the current and future changes in power demand and the development trend of power demand cannot be accurately reflected and the accuracy of forecasting power load is low

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
  • Improved-support-vector-machine-based power load prediction correction method
  • Improved-support-vector-machine-based power load prediction correction method
  • Improved-support-vector-machine-based power load prediction correction method

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0016] Specific implementation mode one: combine figure 1 The electric load forecast correction method based on the improved support vector machine of this embodiment is specifically prepared according to the following steps:

[0017] Step 1. Select sample data from the database or on-site;

[0018] Step 2: Encoding the sample data;

[0019] Step 3, preprocessing the encoded sample data;

[0020] Step 4, initialization kernel function; described initialization kernel function, because selection of different kernel functions can constitute different predictive models, so the selection of kernel function is also crucial; Select Gaussian radial basis kernel function to preprocessed Sample data for processing;

[0021] Step five, using the improved support vector machine to predict the electric load.

[0022] The effect of this implementation mode:

[0023] This embodiment relates to an electric load forecast correction method based on an improved support vector machine. The...

specific Embodiment approach 2

[0024] Embodiment 2: This embodiment differs from Embodiment 1 in that: the sample data is specifically the power grid operation competition data provided by the European Intelligent Technology Network. Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0025] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that the training samples for encoding the sample data are as follows: short-term power load forecasting is not just a matter of using a single factor to predict future loads, Its training samples are composed of multiple factors and are a multi-dimensional non-linear sample set; including historical load, temperature and holiday information. Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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 relates to a power load prediction correction method, especially to an improved-support-vector-machine-based power load prediction correction method. With the method, problems that the current and future power demand changes and the power demand development trend can not be reflected accurately and the power load accuracy prediction is low can be solved. Therefore, the invention puts forward an improved-support-vector-machine-based power load prediction correction method. The method comprises: step one, sample data are selected from a database or a field; step two, the sample data are coded; step three, pretreatment is carried out on the coded sample data; step four, a kernel function is initialized; and the sample data after pretreatment are processed by selecting a Gaussian radial basis function; and step five, power load prediction is carried out by using an improved support vector machine. The method is applied to the power load prediction correction field.

Description

technical field [0001] The invention relates to a power load forecasting and correction method, in particular to a power load forecasting and correction method based on an improved support vector machine. Background technique [0002] The electric power sector must not only meet the normal electricity consumption of users, but also take into account not to waste national resources, especially the demand for short-term loads. For example, during peak electricity consumption periods such as summer or holidays, correct power load forecasting is necessary. Failure to predict or inaccurate prediction will result in insufficient power during peak hours, and unnecessary waste when the demand for power is not large. At present, the short-term load forecasting of electric power is generally to predict the power load within one month in the future, and the power load forecast has continuity, that is, the previous forecast results will be used as part of the input parameter variables 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
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 陈洪涛吴刚单小东孟祥辰陈艳孙振胜张海明李伟李军韩显华李冬梅黄树春赵强李一凡韩兆婷
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