Real Time Scheduling and Intelligent Prediction Algorithm for Electric Vehicle Charging Station Based on Big Data Analysis

As the key node of electric vehicle (EV) energy supply, the distribution, capacity and operational efficiency of electric vehicle electric vehicle charging station (EVCS) are directly related to the experience of EV users and the operational efficiency of the whole transportation system. Big data technology can collect, process and analyze the massive data of EVCS in real time, including multi-dimensional information such as the running state of EVCS, charging behavior of users, power grid load and so on. This paper proposes a real-time scheduling framework for EVCS based on big data analysis, which realizes real-time scheduling and intelligent management of EVCS. Through the in-depth mining and analysis of these data, we can accurately grasp the actual operation and user needs of EVCS, and provide data support for the scheduling and optimization of EVCS. The output value of the model is very close to the actual value, which shows that the model proposed in this paper has high accuracy in predicting the charging demand of EVCS. Through the intelligent forecasting algorithm, this method can accurately predict the future charging demand of EVCS, so as to allocate and schedule resources in advance.