Early Warning Model of Power Grid Meteorological Disaster Based on Machine Learning

Wei Chen, Xudong Wang, Jie Duan, Zhengyi Liu, Yibin Zhao

2025

Abstract

In order to solve the problem of early warning of meteorological disasters in power grid, this paper uses random forest model modeling to construct an efficient early warning system based on the analysis of the correlation between meteorological factors and power grid faults. In the process of research, this paper collects the power grid operation data and meteorological records of a province for 5 years, and uses a data-driven method to train and optimize the model. The experimental data show that the prediction accuracy of the model is 88.7%, which is significantly better than the traditional method and has a strong application prospect. The research results show that the early warning model of power grid meteorological disasters based on machine learning can effectively improve the early warning ability of the power grid under complex meteorological conditions after being constructed by using the random forest algorithm, and provide stable and strong support for the safe operation of the power grid.

Paper Citation

in Harvard Style

Chen W., Wang X., Duan J., Liu Z. and Zhao Y. (2025). Early Warning Model of Power Grid Meteorological Disaster Based on Machine Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 18-24. DOI: 10.5220/0013534600004664

in Bibtex Style

@conference{incoft25,
author={Wei Chen and Xudong Wang and Jie Duan and Zhengyi Liu and Yibin Zhao},
title={Early Warning Model of Power Grid Meteorological Disaster Based on Machine Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={18-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013534600004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Early Warning Model of Power Grid Meteorological Disaster Based on Machine Learning
SN - 978-989-758-763-4
AU - Chen W.
AU - Wang X.
AU - Duan J.
AU - Liu Z.
AU - Zhao Y.
PY - 2025
SP - 18
EP - 24
DO - 10.5220/0013534600004664
PB - SciTePress