A big data-based energy storage capacity time-sharing lease control system and method

By using big data analysis and enterprise type classification, a differentiated energy storage demand forecasting model was established, which solved the problem of supply and demand mismatch in energy storage capacity time-sharing leasing and realized the efficient, fair allocation and rational utilization of energy storage resources.

CN122288846APending Publication Date: 2026-06-26NANTONG GOTION NEW ENERGY TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
NANTONG GOTION NEW ENERGY TECHNOLOGY CO LTD
Filing Date
2026-05-26
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing energy storage capacity time-sharing leasing technologies lack accurate classification and differentiated demand forecasting, leading to supply and demand mismatch and failing to meet the different electricity consumption patterns of different enterprises, resulting in idle or insufficient energy storage resources.

Method used

Through big data analysis, the system automatically extracts the time-of-use electricity consumption characteristics of electricity-consuming enterprises, classifies enterprise types, establishes differentiated energy storage demand prediction models, and sets priority weights based on enterprise importance for capacity pre-allocation and secondary optimization, thereby achieving precise energy storage resource allocation.

Benefits of technology

It has enabled the efficient use of energy storage resources, avoided resource waste, reduced enterprise usage costs, improved user satisfaction and operating revenue, and ensured the power supply reliability of key enterprises.

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Abstract

This invention discloses a big data-based energy storage capacity time-sharing leasing control system and method, relating to the field of time-sharing leasing control technology. It involves marking electricity-consuming enterprises within the monitoring range, acquiring historical electricity consumption records of these enterprises based on historical electricity consumption data from a preset monitoring period, and calculating the basic electricity load forecast values ​​for each time period, taking into account historical influencing factors. For enterprises with only a single working label, the corresponding predicted energy storage demand is calculated according to the label type. For enterprises with more than one working label, the maximum value of the predicted demand corresponding to each label is taken as the final demand. For enterprises without labels, the basic load forecast value is used as the final demand. Time-sharing capacity is pre-allocated based on enterprise type priority, and the results are pushed to the enterprise to confirm leasing intentions. This invention, through a priority capacity pre-allocation mechanism based on enterprise importance, prioritizes electricity demand when capacity resources are limited.
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