A Hybrid Deep Learning Framework for Network Flow Forecasting of Power Grid Enterprise

Xin Huang, Ting Hu, Pei Pei, Qin Li, Xin Zhang and Fanlin Meng

Complexity, 2022, vol. 2022, 1-11

Abstract: With the expansion of the digital business line, the network flow behind the digital power grid is also exploding. To prevent network congestion, this article proposes a novel network flow forecasting model, which is composed of variational mode decomposition (VMD), GRU-xgboost block, and a forecasting adjustment block, to grasp the changing patterns and trends of network flow in advance, and to formulate reasonable and effective flow management strategies and meet the requirements of users for network service quality. The network flow series in power grid enterprise always contain complex patterns and outliers, and VMD is applied to adaptively process complex net flow time series into several subseries with simpler patterns. A GRU-xgboost block is designed to reconstruct the features of historical series. Then, xgboost model is applied to generate predictions for all decomposed subsignals. For the final predictions, we design a forecasting adjustment block to further remove the influence of random noise. Finally, the empirical results show the superior performance of the proposed model on network flow forecasting task.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5497574

DOI: 10.1155/2022/5497574

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