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Electric load forecasting method based on optimized least squares support vector machine

A technology of support vector machine and power load, applied in forecasting, computer parts, instruments, etc., and can solve problems such as slow convergence speed

Active Publication Date: 2022-03-08
JIANGXI UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

To a certain extent, it overcomes the disadvantage of slow convergence speed when the traditional artificial bee colony algorithm is applied to power load forecasting, and the invention can improve the accuracy of power load forecasting

Method used

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  • Electric load forecasting method based on optimized least squares support vector machine
  • Electric load forecasting method based on optimized least squares support vector machine
  • Electric load forecasting method based on optimized least squares support vector machine

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Embodiment

[0038] Present embodiment in conjunction with accompanying drawing, the specific implementation steps of the present invention are as follows:

[0039] Step 1, collect the power load data set from the power information system, then preprocess the collected power load data set, and divide the power load data set into two parts: power load training data set and power load test data set; where , preprocessing the power load data set includes but is not limited to deleting redundant power load data, filling missing power load data, eliminating outlier power load data, and normalizing power load data;

[0040] Step 2, determine that the training parameters of the optimal design required by the least squares support vector machine are the penalty coefficient C and the kernel function parameter σ, and determine the number TD=2 of the training parameters of the optimal design required by the least squares support vector machine;

[0041] Step 3, setting the number of nectar sources of...

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Abstract

The invention discloses an electric load forecasting method based on an optimized least square support vector machine. The invention uses the elite center artificial bee colony algorithm to optimize and design the training parameters of the least squares support vector machine, and then uses the optimized least squares support vector machine to construct a prediction model of electric load. In the elite-centered artificial bee colony algorithm, the distribution weight of each nectar source in the high-quality nectar source set is calculated first, and then the elite-centered nectar source is generated, and the elite-centered nectar source is used to guide the search direction of the evolution operation, so as to improve the convergence speed of the algorithm. The present invention can improve the accuracy of power load forecasting.

Description

technical field [0001] The invention relates to the field of electric load forecasting, in particular to an electric load forecasting method based on an optimized least square support vector machine. Background technique [0002] Power systems play an important role in modern industrial production. In order to improve the operating efficiency of the power system, power-related departments often need to forecast the power load. In recent years, many researchers have established a mathematical model of electric load modulus based on the least squares support vector machine. Existing studies have shown that the setting of training parameters will greatly affect the power load forecasting accuracy of the least squares support vector machine [Pan Lei, Li Lijuan, Ding Tingting, Liu Dui. Short-term power load forecasting based on improved PSO algorithm and LS-SVM [J]. Industrial and Mine Automation, 2012,38(09):55-59]. However, there is no systematic theoretical guidance on how ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/00G06K9/62
CPCG06Q10/04G06Q50/06G06N3/006G06F18/2411G06F18/214
Inventor 彭静郭肇禄李群芳石涛张文生
Owner JIANGXI UNIV OF SCI & TECH
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