METHOD OF LOAD FORECASTING VIA ATTENTIVE KNOWLEDGE TRANSFER, AND AN APPARATUS FOR THE SAME

    公开(公告)号:US20230055079A1

    公开(公告)日:2023-02-23

    申请号:US17874925

    申请日:2022-07-27

    Abstract: A method of forecasting a future load may include: obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively; among a plurality of source machine learning models, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model through a negative transfer analysis; obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models; obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and predicting a future communication traffic load of the target base station based on the load forecasting model.

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