Electric power big data mining and operation optimization system based on association rule model

Through the association rule model and time series method combined with the neural network model, the characteristics analysis and prediction of the power grid data are solved, and the problem of slow analysis response caused by the large amount of power grid data is achieved, and efficient optimization and safe operation of the power system are achieved.

CN120336711APending Publication Date: 2025-07-18JIANGXI DATANG INT XINYU NO 2 POWER GENERATION CO LTD
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Patent Information

Application Number
CN202510409835.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-02
Publication Date
2025-07-18

AI Technical Summary

Technical Problem

The large amount of power grid data in the existing power system leads to slow analysis response and difficult to obtain analysis results quickly, resulting in slow response to online optimization instructions, reducing the effectiveness of the optimization system.

Method used

The correlation rule model is used to identify the correlation rules between power grid data, and the characteristics of the power grid data are analyzed and predicted by combining time series method and neural network model to optimize the output and operation mode of the generator.

Benefits of technology

It improves the operating efficiency and reliability of the power system, ensures the accuracy of grid data analysis and prediction accuracy, and optimizes the safety and economics of the power system.

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Abstract

The invention discloses a power big data mining and operation optimization system based on an association rule model, and belongs to the technical field of big data mining. The problem that in the prior art, a data analysis result is difficult to rapidly obtain, and consequently online optimization response is slow is solved, the association rule model is established through the correlation analysis method, the association rule between the power grid data is recognized through the association rule model, and therefore feature information in the power grid data is obtained; therefore, the accuracy of subsequent analysis is ensured; the feature information is analyzed through a time sequence method, so that the stability and the change trend of the feature information are determined, and the accuracy of subsequent prediction is ensured; predicting the feature information of the power grid data in combination with a neural network model to obtain the change trend of the power grid load; and the output and operation mode of the generator in the power system are optimized on line according to the change trend, so that the operation efficiency of the power system is improved, and the reliability and safety of the power system are optimized.
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