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Intelligent prediction of power grid transmission line engineering investment based on XGBoost algorithm

Published: 31 July 2024 Publication History

Abstract

Due to the lack of in-depth technical solutions, the accuracy of project investment for transmission lines determination method based on fixed budget estimation is low and the workload is large, so it is urgent to study the investment machine learning-based predictive model. In view of the high dimensional and nonlinear characteristics of transmission line investment, a method of transmission line project investment prediction based on limit gradient boost (XGBoost) algorithm is proposed. The model is trained and tested by using actual transmission line engineering data. The prediction results show that the XGBoost model is superior to the neural network and support vector machine in terms of prediction accuracy and result deviation, and can output the importance ranking of indicators, providing an effective reference for decision makers in investment and control indicators, and the model has high reliability and interpretability.

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 31 July 2024

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