Park energy management method and system based on digital twinning and storage medium

By constructing a three-dimensional digital twin for digital mirror reconstruction and fuzzy semantic modeling, and combining differential evolution algorithm and long short-term memory neural network, the problems of virtual-real synchronization and prediction accuracy of the park's energy system were solved, achieving efficient energy management and predictive control.

CN122172548APending Publication Date: 2026-06-09HENAN TYRONE ELECTRICAL EQUIP CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HENAN TYRONE ELECTRICAL EQUIP CO LTD
Filing Date
2026-02-04
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies lack digital modeling of the park's physical energy system, making it difficult to synchronize virtual and real data. Fuzzy controller parameters cannot be adaptively optimized, and traditional forecasting methods struggle to handle long-term dependencies in time-series data, resulting in insufficient forecast accuracy and an inability to accurately capture the complex changing patterns of energy demand in the park.

Method used

By constructing a three-dimensional digital twin for digital mirror reconstruction, performing fuzzy semantic modeling and fuzzy decision-making, optimizing fuzzy decision parameters using differential evolution algorithm, and combining long short-term memory neural network for time series learning, a park energy behavior model is established to achieve real-time synchronization and accurate prediction.

Benefits of technology

It has achieved comprehensive digital monitoring and precise modeling of the park's energy system, improved the system's ability to handle uncertain and ambiguous information, enhanced the adaptability and prediction accuracy of energy management, and transformed into a proactive predictive management mode.

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Abstract

This application relates to the field of digital twin technology, and discloses a method, system, and storage medium for park energy management based on digital twins. The method includes: digitally mirroring and reconstructing the park's physical energy system to obtain a three-dimensional digital twin; performing fuzzy semantic modeling and fuzzy decision processing on multi-dimensional energy state information based on the digital twin to obtain intelligent energy scheduling decision data for the park; inputting the scheduling decision data into a differential evolution algorithm for optimization to obtain the optimal energy management strategy; and constructing a prediction engine based on the optimal strategy for time series learning to obtain a digital twin-driven park energy management scheme. This application solves the problem of difficult virtual-real synchronization of park energy systems by constructing a digital twin, further solves the problem of the inability of fuzzy controller parameters to adaptively optimize by using a differential evolution algorithm, and addresses the problem of insufficient time series data processing capability of traditional prediction methods by using a long short-term memory neural network.
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