Power grid energy management optimization platform based on big data

By deploying real-time power monitoring equipment and machine learning models in concentrated areas of high-tech industries, dividing and scheduling power demands, the problem of power grid difficulty in coping with the surge in sudden power demand has been solved, and the stable operation of the power grid and the normal operation of enterprises have been achieved.

CN119647871AActive Publication Date: 2025-03-18南京迅集科技有限公司
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Patent Information

Application Number
CN202411752567.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-02
Publication Date
2025-03-18
Estimated Expiration
2044-12-02

AI Technical Summary

Technical Problem

The existing power grid is difficult to cope with the sudden surge in power demand in areas concentrated in high-tech industries, resulting in the risk of network paralysis and power outages.

Method used

By deploying real-time power monitoring equipment in a concentrated area of ​​high-tech industries, obtaining power consumption data in real time, performing feature extraction and machine learning model evaluation, dividing normal and explosive power demands, and using load prediction and distributed optimization algorithms for intelligent scheduling.

Benefits of technology

Accurate monitoring and intelligent dispatch of power consumption are realized, network paralysis and power outage risks caused by sudden power demand are avoided, and the stable operation of the power grid and the normal operation of the enterprise are ensured.

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Abstract

The invention discloses a power grid energy management optimization platform based on big data, and relates to the technical field of power grid energy management, and the platform comprises the following steps: firstly, deploying real-time power monitoring equipment in an area where high-tech industries are centralized, and obtaining real-time power consumption data of enterprises and key electric equipment in real time through the power monitoring equipment; and a data interface is established, an enterprise is allowed to autonomously upload power utilization data of internal key equipment, and the comprehensiveness and accuracy of the data are improved. According to the scheme, through real-time power monitoring, feature extraction and machine learning model evaluation, accurate monitoring and intelligent scheduling of power consumption are realized. Aiming at normal and surge power demands, stable power supply and efficient utilization of resources are ensured through load prediction and a distributed optimization algorithm, risks of network paralysis, power failure and the like caused by sudden power demands are avoided, and enterprise operation and power grid safety are guaranteed.
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