Time Series Prediction Method of Metrics of Dispatching Automation System Based on AI Platform. Issue 1 (1st February 2023)

• Record Type: Journal Article
• Authors: Wan, Xiong; Shen, Jialing; Kong, Yanru; Huang, Xinjian; Lao, Yinyin; Ji, Xuechun
• Is Part Of: Journal of physics. Volume 2433 Issue 1(2023)
• Publication Date: 2023-02-01
• Subjects: Time series prediction -- metrics of dispatching automation system -- AI platform; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2433/1/012014
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In view of the shortcomings of manual operation mode and passive anomaly perception in the current dispatching automation system, this paper provides a time series prediction method for the metrics of the dispatching automation system based on the artificial intelligence platform. Firstly, based on the collected metrics of the dispatching automation system, the time series prediction algorithm of metrics based on Long Short-Term Memory(LSTM) and LightGBM(LGB) combined model is studied. Then, the algorithm is integrated into a one-stop graphical interactive modeling tool and the model training process is drawn. Through the task is automatic split and distributed model training is automatically carried out on a regular basis. Then, the AI model service sharing technology based on Kubernetes is used to realize the model service release and automatic update, and the platform interactive development component is used to realize automatic online prediction and rolling iterative update. Finally, the actual data of the system metrics is used for example analysis, and the results show that the proposed method can realize the advance prediction of the system metrics, and realize the whole process automatic integration model training and prediction based on the artificial intelligence platform, and effectively improve the model training efficiency, meet the real-time and security requirements of the dispatching automation system, and is more suitable for engineering application scenarios.