Real-time electricity price-based chaotic support vector machine load prediction method

A support vector machine, load forecasting technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as failure to consider the internal evolution of electrical load characteristics

Pending Publication Date: 2021-01-01
SOUTH CHINA UNIV OF TECH
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the above load forecasting models fail to take into ac

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
  • Real-time electricity price-based chaotic support vector machine load prediction method
  • Real-time electricity price-based chaotic support vector machine load prediction method
  • Real-time electricity price-based chaotic support vector machine load prediction method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0084] The present invention will be further described below in conjunction with specific examples.

[0085] Such as figure 1 , figure 2 , image 3As shown, the real-time electricity price-based chaotic support vector machine load forecasting method provided in this embodiment includes the following steps:

[0086] 1) The improved C-C method is used to reconstruct the multivariate phase space of the load and electricity price time series to obtain high-dimensional phase points. The specific steps are as follows:

[0087] 1.1) For load or electricity price time series X={x 1 , x 2 ,...x i ..., x N}, where x i is the load or electricity value at the i-th moment, with the delay time τ, embedding dimension m, and reconstructing the phase space Y={Y i}, Y i is the i-th phase point in the phase space, and the correlation integral is calculated to get:

[0088]

[0089] Among them, N m is the number of reconstructed phase points, d ij =||Y i -Y j || ∞ Indicates the...

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 real-time electricity price-based chaotic support vector machine load prediction method, which comprises the following steps: 1) performing multivariable phase space reconstruction on a load and electricity price time sequence by adopting an improved CC method to obtain a high-dimensional phase point; 2) calculating a Lyapunov index through a small data volume method, calculating a correlation dimension by using a GP method, and quantitatively analyzing the chaotic characteristics of the time sequence; and 3) establishing a model by using a least square support vectormachine improved by an adaptive particle swarm algorithm, and carrying out load prediction. According to the invention, a multivariable phase space reconstruction method is introduced, the influenceof real-time electricity price factors can be effectively considered in load prediction, a least square support vector machine algorithm is used, a regression function is searched in a high-dimensional phase space, the calculation efficiency and precision can be effectively improved, a load prediction technology considering demand side response is realized, and the user load participating in demand side response can be accurately predicted.

Description

technical field [0001] The invention relates to the technical field of demand side response and time series chaotic characteristic analysis and prediction, in particular to a chaotic support vector machine load forecasting method based on real-time electricity price. Background technique [0002] The power industry is an important basic energy industry. In recent years, due to the increase in electricity demand, the scale and level of the power grid have been continuously improved, and users' requirements for the stability, safety and power quality of the power system have also been further improved. . [0003] Load forecasting is the basis for stable, reliable and economical operation of the power grid. It is of great significance in grid construction planning, power deployment planning, and start-stop and maintenance arrangements for generating units. Therefore, accurate load forecasting is of great significance. [0004] Traditional load forecasting methods mainly includ...

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): G06Q50/06G06Q10/04
CPCG06Q10/04G06Q50/06
Inventor 季天瑶王诗雨李志刚
Owner SOUTH CHINA UNIV OF TECH
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