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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
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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 indicators cannot be accurately obtained according to the empirical formula, and how the operating data indicators specifically affect the investment budget cannot be obtained

Method used

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  • 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

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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...

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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

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Application Information

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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
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