Power transmission and transformation project cost prediction method based on grey correlation analysis

CN119623695APending Publication Date: 2025-03-14STATE GRID SHANXI ELECTRIC POWER CO ECONOMIC & TECH RES INST
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Patent Information

Application Number
CN202411585822.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-08
Publication Date
2025-03-14

AI Technical Summary

Technical Problem

The existing cost prediction methods for power transmission and transformation projects are difficult to effectively deal with the nonlinear correlation between complex factors, resulting in a lack of cost grading and affecting the practical application of the prediction results.

Method used

The gray correlation analysis method is used to collect and preprocess data of multiple influencing factors, and the gray correlation between each factor and the target cost index is calculated, and the cost index is graded according to the results.

Benefits of technology

It realizes the identification of key factors affecting cost more accurately in complex engineering environments, provides more accurate cost prediction results, and improves the efficiency and accuracy of project cost management.

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Abstract

The invention discloses a power transmission and transformation project cost prediction method based on grey correlation analysis. The method comprises the following steps: (1) data collection; (2) data preprocessing; (3) grey correlation analysis; (4) cost index grading; and (5) predicting and evaluating. According to the method, through introduction of grey correlation analysis, key factors influencing the cost can be effectively identified in a complex engineering environment, and the cost indexes are scientifically graded. Compared with a traditional method, the method can provide a more accurate cost prediction result, and adapts to dynamically changing engineering conditions, so that the efficiency and accuracy of engineering cost management are improved. In combination with grey correlation analysis and cost index grading, the accuracy and reliability of cost prediction are improved by analyzing historical data and influence factors. Establishment of a more accurate project cost prediction and control system is facilitated, and particularly, the method has important practical value in coping with variable factors in a dynamic environment of a power transmission and transformation project.
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