A power grid investment prediction method based on an AdaBoost regression tree model

A forecasting method and regression tree technology, applied in forecasting, character and pattern recognition, instruments, etc., can solve problems such as the inability to accurately obtain the relationship between investment and operating data indicators, affect the investment budget, and fail to obtain operating data indicators.

Inactive Publication Date: 2019-06-18
UNIV OF ELECTRONIC SCI & TECH OF CHINA
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The above-mentioned investment decision-making method has the following problems: the relationship between the investment amount and the operating data indicato

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 power grid investment prediction method based on an AdaBoost regression tree model
  • A power grid investment prediction method based on an AdaBoost regression tree model
  • A power grid investment prediction method based on an AdaBoost regression tree model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0031] figure 1 It is a flow chart of a specific embodiment of the grid investment prediction method based on the AdaBoost regression tree model of the present invention. like figure 1 As shown, the concrete steps of the grid investment prediction method based on the AdaBoost regression tree model of the present invention include:

[0032] S101: Obtain historical power grid investment data:

[0033] Determine N technical indicators related to grid investment according to needs, and obtain the value x′ of the N grid investment related technical indicators at M time points m (n) and the corresponding grid investment Y m , n=1,2,...,N, m=1,2,...,M. Record the grid investment-related technical index vector at the mth time point as X′ m ={x' m (1), x′ m (2),...,x' m (N)}, for each power grid investment-related technical index vector X′ m Perform dimensionless processing to obtain a dimensionless grid investment-related technical index vector X m ={x m (1), x m (2),...,x...

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 power grid investment prediction method based on an AdaBoost regression tree model. The method comprises steps of determining N power grid investment related technical indexes according to requirements, acquiring numerical values of the N power grid investment related technical indexes at M time points and corresponding power grid investments, constructing a power grid investment related technical index vector at each time point; carrying out dimensionless processing; forming a training sample set by the dimensionless power grid investment related technical index vectors and the power grid investments; and training the AdaBoost regression tree by adopting the training sample set, performing K times of iteration to obtain K AdaBoost regression trees as weak learners, selecting the weak learners from the K AdaBoost regression trees to obtain a strong learner, and performing power grid investment prediction by utilizing the strong learners. According to the method, the training process of the AdaBoost regression tree model is improved, the training process is introduced into power grid investment prediction, and the power grid investment prediction accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of grid investment forecasting, and more specifically relates to a grid investment forecasting method based on an AdaBoost regression tree model. Background technique [0002] In recent years, social demand for electricity has been increasing with economic development, and the investment scale of power grid enterprises has also gradually increased. However, affected by the adjustment of the national industrial structure, power grid enterprises face greater uncertainty in investment management, greater fluctuations in corporate profits, new asset performance has not played an effective role, and the contradiction between input and output structures is more prominent. [0003] On the specific issue of optimizing investment structure allocation, how to obtain objective investment rules by analyzing past investment structures and corresponding operating data, and make investment budgets according to the rules ha...

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): G06Q10/04G06K9/62G06Q40/06G06Q50/06
Inventor 凡时财王强邹见效徐红兵
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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