Private charging pile real-time load prediction method based on edge calculation

By building a bp neural network model on edge computing devices, combining user charging portraits, charging status and weather influencing factors, accurate prediction of real-time load of private charging piles is achieved, solving the problem of inaccurate prediction in the existing technology, and improving charging efficiency and power resource utilization.

CN119648005AInactive Publication Date: 2025-03-18SHANDONG GUODIAN INVESTMENT ENERGY MARKETING CO LTD
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Patent Information

Application Number
CN202411793492.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-09
Publication Date
2025-03-18
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

It is difficult for the prior art to accurately predict the real-time load of private charging piles, especially when considering the interaction and coupling relationships of various factors such as user behavior, weather, traffic and electricity prices.

Method used

A real-time load prediction method for private charging piles based on edge computing is adopted. By obtaining historical charging efficiency trajectory data and environmental impact data, a bp neural network model is built, and real-time load prediction is carried out based on user charging image coefficient, charging status coefficient and weather impact coefficient.

Benefits of technology

It realizes accurate prediction of real-time load of private charging piles, reduces data transmission delay, improves the real-time and accuracy of prediction results, and provides users with a personalized charging plan, optimizing the operational efficiency of charging piles and the utilization of power resources.

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Abstract

The invention discloses a private charging pile real-time load prediction method based on edge calculation, and relates to the technical field of load prediction, and the method comprises the following steps: obtaining historical charging efficiency trajectory data and environmental influence data of a private charging pile; constructing a private charging pile real-time load prediction model based on a bp neural network according to the charging efficiency trajectory data and the environmental influence data; acquiring real-time data of the private charging pile, inputting the real-time data into the private charging pile real-time load prediction model, and performing real-time load prediction on the edge computing equipment to obtain a load prediction value of the private charging pile in the future t time; and the prediction result is sent to the user terminal equipment through the communication module, and a charging plan is reasonably arranged according to the prediction result. According to the invention, the powerful processing capability of the edge computing device is fully utilized, and rapid acquisition, preprocessing and load prediction of the real-time data of the private charging pile are realized.
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