A spatio-temporal dynamic load forecasting method for electric vehicles

A space-time dynamic and load forecasting technology, applied in forecasting, data processing applications, instruments, etc., can solve problems such as the inability to predict the space-time dynamic load of electric vehicles

Active Publication Date: 2019-03-22
YUNNAN POWER GRID
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Problems solved by technology

[0005] This application provides a method for forecasting the space-time dynamic load of electric vehicles to solve the technical problem in the prior art that the space-time dynamic load of electric vehicles cannot be accurately and effectively predicted

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  • A spatio-temporal dynamic load forecasting method for electric vehicles
  • A spatio-temporal dynamic load forecasting method for electric vehicles
  • A spatio-temporal dynamic load forecasting method for electric vehicles

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[0046] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described The embodiments are only some of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0047] see figure 1 , is a schematic flowchart of a method for spatiotemporal dynamic load forecasting of an electric vehicle provided by an embodiment of the present invention. combine figure 1 It can be obtained that the method of electric vehicle spatiotemporal dynamic load forecasting in this application includes:

[0048] Step S101: Pre...

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Abstract

The invention discloses a spatio-temporal dynamic load forecasting method of an electric vehicle, which comprises the following steps of: preprocessing the load data of a charging pile; constructing Aspatio-temporal dynamic load matrix according to the charging load data and the charging pile position information. Normalizing The spatio-temporal dynamic load matrix and acquiring the spatio-temporal dynamic load normalized matrix. Dividing The spatio-temporal dynamic load normalization matrix into training set and test set. According to the training set, acquiring a two-dimensional causal convolution neural network model. If the parameters in the two-dimensional cavity causal convolution neural network model make the objective function minimum on the test set, predicting the test model according to the model and denormalized. Otherwise, adjusting the super parameters of the two-dimensional cavity causal convolution neural network model to obtain the two-dimensional cavity causal convolution neural network model again. The two-dimensional cavity causal convolution neural network model in the present application can fully consider time and space, and realize accurate spatio-temporaldynamic load forecasting of electric vehicles.

Description

technical field [0001] The present application relates to the technical field of power system operation and load forecasting, and in particular to a method for spatiotemporal dynamic load forecasting of electric vehicles. Background technique [0002] Due to its energy-saving, emission-reducing, and green environmental protection features, electric vehicles are considered to be one of the beneficial ways to solve today's energy shortage and environmental problems, and thus are strongly supported and promoted by governments and enterprises of various countries. Due to the uncertainty and mutual differences in the needs and behaviors of electric vehicle users, the future large-scale electric vehicle charging load will have uncertain characteristics such as randomness, intermittency and volatility in time and space, which will affect the safe operation and optimal dispatching of the power grid. Therefore, it is necessary to effectively predict the charging load of electric vehi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04
CPCG06Q10/04G06Q50/06G06N3/045Y04S30/12
Inventor 张秀钊王志敏钱纹赵爽刘娟陈宇赵岳恒
Owner YUNNAN POWER GRID
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