基于深度学习的供电安全指数预测技术研究

doi:10.3778/j.issn.1002-8331.2202-0132

摘要/Abstract

摘要： 高速铁路供电专业安全指数反映了高铁供电故障与事故的发生趋势，对其进行规律验证与预测对于高速铁路现场供电专业安全的综合评估与预警具有非常重要的现实意义。基于高铁十周年供电安全指数数据，提出了一种时间序列深度学习预测模型。该模型结合了时间序列统计建模与深度学习方法。首先利用ARIMA（autoregressive integrated moving average model）方法对安全指数时序数据进行建模，通过引入季节性特征变量，提高模型的修正拟合优度，验证了供电安全指数的季节性规律；然后利用深度学习门控递归单元神经网络对供电安全指数进行预测；最后利用皮尔森系数评价预测模型的有效性。结果表明，利用门控递归单元对供电安全指数的预测值在训练集和测试集上的皮尔森系数分别达到0.71和0.74，可有效拟合安全指数变化趋势。

关键词: 供电安全指数, 深度学习, 门控递归单元, 时间序列预测

Abstract: The safety index of high-speed railway power supply system reflects the occurrence trend of failures and accidents in the system, and the regular verification and prediction of the index are of very important practical significance for the comprehensive evaluation and pre-warning of the power supply system. Based on the data of the safety index over ten years, a time series predictive model is proposed, which combines time series statistics and deep learning methods. Firstly, ARIMA（autoregressive integrated moving average model） is used to model the time series of the safety index, and the adjust-R2 of the ARIMA model is improved by introducing the seasonal characteristic variable, verifying the seasonal law of the time series. Secondly, the GRU（gated recurrent unit） neural network is used to predict the time series. Finally, the Pearson coefficient is used to evaluate the effectiveness of the predictive model. The results show that the Pearson coefficients on the training set and test set are respectively 0.71 and 0.74, which proves the effectiveness of the predictive model.

Key words: safety index of power supply system, deep learning, gated recurrent unit, time series prediction

彭楠, 郭剑峰, 张文轩, 王婧, 陶凯, 侯森泉. 基于深度学习的供电安全指数预测技术研究[J]. 计算机工程与应用, 2023, 59(10): 314-320.

PENG Nan, GUO Jianfeng, ZHANG Wenxuan, WANG Jing, TAO Kai, HOU Senquan. Research on Prediction Technology of Safety Index of Power Supply System Based on Deep Learning[J]. Computer Engineering and Applications, 2023, 59(10): 314-320.

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