Power communication digital flow prediction method based on VMD-LSTM-SVM model

Abstract

Under the current trend of abundant information on power business, large data concentration, and large flow explosion, aiming at the randomness, volatility, and uncertainty of massive flow of electric power communication network, a digital power flow prediction method based on VMD-LSTM-SVM model is proposed. The interaction between the values of each traffic index before and after time is considered. LSTM is used to process traffic data and make an accurate prediction of future traffic. The power communication network can make dispatch responses to possible communication congestion by using link resources according to traffic prediction results and ensuring the transmission quality of power service data.

Fuzzy Learning-Based Electric Measurement Data Circulation Monitoring and Security Risk Anomaly Evaluation

“…Nie et al (2021) proposed a multitask learning based deep learning framework and LSTM, which utilizes the information of link loads to assist in addressing backbone network traffic prediction in data circulation for the industrial internet of things applications, thus enhancing prediction accuracy. Wang et al (2023) introduced a digital traffic prediction method based on the variational mode decomposition-LSTM-support vector machine model. Traffic data are processed by LSTM to accurately predict future traffic, thereby ensuring the transmission quality of power service data circulation.…”