METHOD OF SHORT-TERM LOAD FORECASTING VIA ACTIVE DEEP MULTI-TASK LEARNING, AND AN APPARATUS FOR THE SAME

    公开(公告)号:US20220207357A1

    公开(公告)日:2022-06-30

    申请号:US17359919

    申请日:2021-06-28

    Abstract: A method of load forecasting using multi-task deep learning includes obtaining references data corresponding to commodity consuming objects, clustering the commodity consuming objects into clusters based on the obtained reference commodity consumption data; obtaining cluster models based on: reference commodity consumption data, reference environmental data, and reference calendar data; inputting, into the cluster models, present data corresponding to the commodity consuming objects; and predicting, based on an output of the cluster models, a future commodity consumption for the commodity consuming objects. The cluster models include multi-task learning processes having joint loss functions.

    ENERGY SAVING IN CELLULAR WIRELESS NETWORKS VIA TRANSFER DEEP REINFORCEMENT LEARNING

    公开(公告)号:US20240406861A1

    公开(公告)日:2024-12-05

    申请号:US18609797

    申请日:2024-03-19

    Abstract: The present disclosure provides methods, apparatuses, systems, and computer-readable mediums for operating a target base station by an apparatus. A method includes collecting a plurality of trajectories corresponding to the target base station and a plurality of source base stations, clustering, using an unsupervised reinforcement learning model, the plurality of trajectories into a plurality of clusters including a target cluster, selecting, as a target trajectory, a selected trajectory from the target cluster that maximizes an energy-saving parameter of the target base station, and applying, to the target base station, an energy-saving control policy corresponding to the target trajectory. The target cluster corresponds to the target base station and at least one source base station from among the plurality of source base stations.

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