Research on the Performance of Text Mining and Processing in Power Grid Networks

Yuzhong Zhou; Zhengping Lin; Liang Tu; Jiahao Shi; Yuliang Yang

doi:10.4108/eetsis.v10i4.3094

Authors

Yuzhong Zhou Research Institute of China Southern Power Grid, Guangzhou, China
Zhengping Lin Research Institute of China Southern Power Grid, Guangzhou, China
Liang Tu Research Institute of China Southern Power Grid, Guangzhou, China
Jiahao Shi Research Institute of China Southern Power Grid, Guangzhou, China
Yuliang Yang Research Institute of China Southern Power Grid, Guangzhou, China

DOI:

https://doi.org/10.4108/eetsis.v10i4.3094

Keywords:

Text mining, performance analysis, deep learning

Abstract

This paper employs deep learning technique to perform the research of text mining for power grid networks, focusing on fundamental elements such as loss and activation functions. Through some analysis and formulas, we explain how these functions contribute to deep learning. We also introduce major deep learning training models, including CNN and RNN, and provide visual aids to aid understanding. To demonstrate the impact of various factors on deep learning training, we employ control variable experiments to analyze the influence of factors such as learning rate, batch size, and data noise on model training trends. While the influence of hyperparameters and data noise are covered in this paper, other factors such as CPU and memory frequency, as well as GPU performance, also play a crucial role in deep learning training. Therefore, continuous adjustments to various factors are necessary to achieve optimal training results for deep learning models in power grid networks.

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