A Dynamic Load Prediction and Optimization System and Method for Charging Piles Based on Edge Computing

By identifying the charging and discharging status of charging piles through edge computing, prioritizing load prediction and multi-stage calculations are performed, solving the load prediction problem under the parallel charging and discharging state and improving the sensitivity and accuracy of prediction.

CN121012014BActive Publication Date: 2026-01-30GANSU PROVINCE COMM IND ENG CONSTR CO LTD
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Patent Information

Application Number
CN202511537253.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-10-27
Publication Date
2026-01-30
Estimated Expiration
2045-10-27

AI Technical Summary

Technical Problem

Existing charging pile load prediction methods cannot identify and handle the parallel state of charging and discharging, resulting in fixed prediction strategies, insufficient flexibility, reduced response sensitivity, and increased error rate.

Method used

A dynamic load prediction and optimization system for charging piles based on edge computing is adopted. By identifying the charging and discharging status, priority load prediction is performed, load capacity and time difference are calculated, prediction monitoring window is set, and multi-stage load prediction is performed by combining historical data.

Benefits of technology

It improves the response sensitivity of load forecasting, reduces the error rate, and enhances the ability to adapt to dynamic load changes.

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Abstract

This invention discloses a dynamic load prediction and optimization system and method for charging piles based on edge computing, belonging to the field of load prediction and optimization technology. It addresses the problem of reduced response sensitivity in load prediction models by setting peak and valley charging periods and identifying the current period. The system obtains the charging and discharging status of charging piles within the charging area, selects charging piles in the discharging state for priority load prediction based on their status, and collects the total number of high-load piles. It detects the charging and discharging time difference and remaining discharge amount to calculate the load capacity of charging piles in the charging and discharging states. Based on the load capacity, it determines priority prediction targets, sets a prediction monitoring window to collect the number of high-load piles and calculates the prediction difference. It combines the total number of high-load piles in the current period and historical peak and valley periods to calculate the high-load change value. Finally, it uses the prediction difference to determine whether to retain discharging piles and perform secondary prediction, thereby improving the dynamic response sensitivity of the load and reducing the load prediction error rate.
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