Abstract:

This paper proposes an electric vehicle charging demand prediction model based on mobile operator location intelligence, and designs a dynamic matching strategy oriented toward renewable energy. By integrating operator location data, renewable energy generation data, and charging stations data, this paper constructs a long short-term memory network model that can accurately predict regional charging demands. The test results show that this model significantly outperforms traditional methods in prediction accuracy. And the dynamic matching strategy effectively increases the local consumption ratio of renewable energy and optimizes users' charging waiting time to a certain extent. This research provides new insights for refined planning and operation of charging stations, and offers technical support for promoting clean energy applications in the transportation field.

Key words: charging stations, charging demand prediction, operator location data, renewable energy integration, dynamic matching strategy