Power load prediction method based on grey correlation degree and support vector machine

A technology of support vector machine and gray relational degree, which is applied in forecasting, data processing applications, instruments, etc., can solve the problems of insufficient application and low accuracy, and achieve the effect of simple division method, excellent effect, simplified classification and regression

Pending Publication Date: 2019-10-08
GUANGDONG POWER GRID CO LTD +1
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Problems solved by technology

[0004] However, the accuracy of existing power load forecasting methods is still relatively low, resulting in their not being widely used

Method used

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  • Power load prediction method based on grey correlation degree and support vector machine
  • Power load prediction method based on grey correlation degree and support vector machine
  • Power load prediction method based on grey correlation degree and support vector machine

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Embodiment Construction

[0062] In order to make the purpose, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the following description The embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0063] The embodiment of the present invention provides a short-term power load forecasting method based on gray relational degree and support vector machine. In order to improve the accuracy of support vector machine, redundant data is eliminated, key samples are seized, and the gray scale of date type and meteorologic...

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Abstract

The invention discloses a power load prediction method based on a grey correlation degree and a support vector machine. The method comprises the following steps: firstly, screening out historical daysconsistent with the date type of a prediction day; according to the meteorological factors, calculating gray correlation degrees between the prediction days and the historical days; and selecting a historical day with a high correlation degree as a sample of the support vector machine, and establishing a support vector machine model. The historical day most similar to the prediction day is selected according to the date type and the meteorological factors to establish the support vector machine model, and the effectiveness and accuracy of the prediction method can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of electric load forecasting, in particular to an electric load forecasting method based on gray relational degree and support vector machine. Background technique [0002] Due to the imbalance between the supply and consumption of electricity, electricity load forecasting has always been an important research topic. The development of smart grids has higher and higher requirements for the accuracy of power load forecasting. The small deviation of the forecast results can plan power supply in advance and reduce unnecessary waste; the large deviation of the forecast results may cause waste of resources, and even lead to grid failure in serious cases. System paralysis. [0003] The power load has the characteristics of instability, and the use of machine learning algorithms can effectively solve the problem of power load instability. Support vector machine is one of the most commonly used machine learning al...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 赖伟坚张鑫陈威洪李敬光林泽宏周娟袁文伟尹健锋邱泽坚郭清元
Owner GUANGDONG POWER GRID CO LTD
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