A Charging Spatiotemporal Prediction Method for Hybrid Isomorphic and Heterogeneous Deep Graph Neural Networks

Abstract

The present invention proposes a charging space-time prediction method for a hybrid isomorphic and heterogeneous depth graph neural network, which includes a deep neural network module, a multi-step adaptive dynamic programming module, a graph neural network module and a hybrid isomorphic and heterogeneous module; depth The neural network module realizes the functions from automatic language translation to image recognition, and models high-complexity data through multi-layer nonlinear transformation; the multi-step adaptive dynamic programming module includes adaptive dynamic programming and adaptive multi-step correction methods for Solve the optimization control problem of large-scale complex nonlinear systems; the graph neural network module constructs the electric vehicle charging space-time prediction model; the hybrid isomorphic and heterogeneous module includes homogeneous networks and heterogeneous networks, and its function is to process similar and different types of information; the method It can effectively solve the problem that the electric vehicle charging load is random and difficult to predict in time and space.

Description

technical field [0001] The invention belongs to the technical field of electric vehicles, and relates to a charging space-time prediction method based on an artificial intelligence method, which is suitable for the distribution of charging piles of electric vehicles. Background technique [0002] Nowadays, problems such as energy shortage, climate warming and environmental pollution are becoming more and more serious. Motor vehicle exhaust pollution is one of the main sources of air pollution. Many countries and regions have formulated a timetable for banning the sale of fuel vehicles. For example, in March 2019, China's Hainan Province issued the "Development Plan for Clean Energy Vehicles in Hainan Province", which was the first in China to propose that the sale of fuel vehicles in the province be banned in 2030. Driven by policies and markets, the scale of electric vehicles and charging demand will usher in a new round of growth, and large-scale charging load access will...

AI Technical Summary

Problems solved by technology

The charging spatio-temporal prediction method of hybrid isomorphic and heterogeneous deep graph neural network can be applied to complex road conditions and the distribution of charging piles with a large flow of people

The difference between this method and the traditional space-time prediction method of electric vehicle load based on dynamic traffic flow: (1) the multi-step adaptive dynamic programming can solve the real-time control problem that the traditional dynamic programming is not suitable for complex nonlinear systems; (2) the hybrid The charging space-time prediction method of the isomorphic and heterogeneous deep graph neural network is not only a simple prediction of the charging space-time, but also introduces a graph neural network to improve the accuracy of the method

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