Gray prediction and support vector machine-based classification type electric vehicle demand temporal-spatial distribution dynamic prediction method

Abstract

The present invention is applied to the electric power system field, in particular relates to a gray prediction and support vector machine-based classification type electric vehicle demand temporal-spatial distribution dynamic prediction method. The method comprises the steps of firstly utilizing a high-accuracy improved gray model to predict the numbers of the vehicles of different types; then based on the proportions of the electric vehicles of different types and the nonlinear characteristic of the influence factors, utilizing a support vector machine regression method and the prediction samples to obtain a classification type electric vehicle substitution ratio prediction result, and utilizing an iterative method to correct the prediction result continuously; and finally matching the two prediction results according to the vehicle types, establishing a classification type electric vehicle demand growth prediction model, and combining the user travel rule research to realize the accurate dynamic temporal-spatial prediction of the classification type electric vehicle demands. Therefore, the method of the present invention has the following advantages of fully considering the characteristic that the historical data is insufficient and the influence of different factors on the development of the electric vehicles and combing the user travel rule research to realize the more accurate dynamic prediction.

Description

technical field [0001] The invention is applied in the field of electric power system, and is used for accurately and reasonably predicting the quantity demand of classified electric vehicles, so as to facilitate the accurate analysis of the charging load of electric vehicles and the reasonable planning and construction of charging stations, and specifically relates to a method based on gray prediction and A Categorical Approach to Electric Vehicle Demand Dynamics Forecasting with Support Vector Machines. Background technique [0002] The energy industry is the basic industry of the national economy, a necessary prerequisite for ensuring national strategic security, and an important guarantee for sustainable economic development. However, with the continuous expansion of my country's economic scale, my country's demand for traditional energy such as oil has not only increased, but also more and more carbon content has been discharged into the atmosphere, which has increased ...

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Problems solved by technology

[0005] At present, domestic scholars have conducted a lot of research on the prediction of electric vehicle ownership, but the consideration factors are relatively single, and they rarely conduct classification research. They mainly use a single method such as linear regression to estimate, which is accurate in predicting the classification of electric vehicle ownership. There is a certain influence on the characteristics of electric vehicles, and the research on the dynamic prediction of the temporal and spatial distribution of electric vehicle demand by type is still blank.

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