Improved firefly algorithm-based power transformation engineering cost prediction method for SVM optimization

A technology of firefly algorithm and firefly optimization, which is applied in the field of substation project cost prediction based on improved firefly algorithm optimization SVM, can solve problems such as overfitting and easy to fall into local optimum, and achieve high-precision prediction, fast convergence speed, and search ability strong effect

Inactive Publication Date: 2018-01-12
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

Among them, SVM is a machine learning method based on the principle of structural risk minimization. It shows many unique advantages in solving small sample and nonlinear problems, but it has disadvantages such as overfitting and easy to fall into local optimum.

Method used

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  • Improved firefly algorithm-based power transformation engineering cost prediction method for SVM optimization
  • Improved firefly algorithm-based power transformation engineering cost prediction method for SVM optimization
  • Improved firefly algorithm-based power transformation engineering cost prediction method for SVM optimization

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Embodiment 1

[0084] In this embodiment, the Schaffer test function is used to test the optimization capabilities of FA and GDFA, and a comparative analysis is performed.

[0085] The Schaffer function formula is:

[0086]

[0087] The Schaffer function obtains the global optimal solution 1 at (0,0). There is a circular ridge around the maximum point, and the difference between the local extremum and the maximum point at the circular ridge is small, and it is easier to fall into a local optimum. For this reason, this embodiment uses the Schaffer function to better test the optimization capability between FA and GDFA. Assuming the search range is [-5,5,-5,5], 100 fireflies, the maximum number of iterations is 200, the step size factor α=0.2, and the light intensity absorption coefficient γ=1.0, then use the Schaffer function to test the FA algorithm and GDFA The comparison results of the algorithm are as follows Figure 3a with Figure 3b shown. Depend on Figure 3a with Figure 3b...

Embodiment 2

[0089] In this embodiment, the GDFA algorithm is used as the SVM system parameter optimization algorithm to predict the cost level of the substation project.

[0090] The following is an example of the cost level of 72 new 220kV outdoor substation projects in a province in 2014. Starting from the geographical environment, project management, social environment, etc., it will include soil quality, power distribution device planning, substation type, Seven indicators, including equipment and material prices, project progress, technical level of designers, and project quality, are the main factors affecting the cost of 220kV new substation projects. The substation project cost prediction in this embodiment is based on the above 7 influencing factors, and the cost level and influencing factors data of the first 54 historical substation projects are selected as the training set, and the cost level and influencing factors of the last 18 projects are the test set. Detailed data are s...

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Abstract

The invention belongs to the field of power transformation engineering cost prediction, and particularly relates to an improved firefly algorithm-based power transformation engineering cost predictionmethod for SVM optimization. For improving optimization performance of an FA to optimize parameters of an SVM prediction model, the invention provides the improved firefly algorithm-based power transformation engineering cost prediction method for the SVM optimization. The method mainly comprises three parts including data processing, parameter determination and cost prediction; specially, in theparameter determination part, a position updating formula of the FA is improved by adopting a Gauss disturbance technology based on a conventional FA to search for optimal parameters; and the methodenhances the capability of fireflies in escaping from local optimum and improves the optimization performance of the FA so as to optimize the parameters of the SVM prediction model. Through Schaffer function testing, the proposed Gauss disturbance FA has the advantages of high convergence speed, good search capability and the like, and can realize high-precision prediction of power transformationengineering cost level.

Description

technical field [0001] The invention belongs to the field of substation project cost forecasting, in particular to a substation project cost forecasting method based on an improved firefly algorithm to optimize SVM. Background technique [0002] Transformation engineering and transmission engineering are two important components of power grid engineering. For substation engineering, its cost level varies with various technical conditions such as voltage level, construction type, and substation type. The cost level prediction of substation projects is an important means to control the cost and improve the rationality of the cost. It is related to the overall economics of the power grid project and has important guiding significance for the cost saving of the power grid project. However, the cost level of power grid projects is affected by regional economic development, natural environment and management level, showing a strong nonlinearity. How to accurately predict the cos...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06K9/62G06N3/00
Inventor 牛东晓戴舒羽宋宗耘康辉
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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