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- W4210725651 abstract "In response to the massive use of recurrent neural network models in distribution network situational prediction and the long training time of the model, this paper proposes a distribution network situational prediction model using temporal convolutional networks to shorten the training time of the model. Specifically, causal convolution is used to achieve the causality of the situation prediction; the larger one-dimensional situation perception field is achieved by expanding convolution, and the short training time of the model is achieved by combining these two convolution operations. The algorithm demonstrates the effectiveness of the model used in this paper in two data set scenarios, i.e., the model requires only 7% of the training time on the LSTM model while maintaining the same posture prediction accuracy as the LSTM model." @default.
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- W4210725651 date "2021-12-26" @default.
- W4210725651 modified "2023-10-18" @default.
- W4210725651 title "Temporal Convolutional Network-based Distribution Network Situation Forecast Method" @default.
- W4210725651 doi "https://doi.org/10.1109/icpds54746.2021.9689928" @default.
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