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Substation flood precipitation forecasting model based on spatio-temporal sequence UNet depth network

Published: 31 July 2024 Publication History

Abstract

Extreme climate change can intensify precipitation events in local areas. In order to predict the future precipitation intensity in the area around the substation within a short period of time, a forecasting model based on a spatio-temporal sequence depth network is proposed in this paper. The precipitation forecast is formulated as a spatio-temporal sequence forecasting problem, in which both the input and forecast targets are spatio-temporal sequences. The input-to-state and state-to-state transformations are performed using the convolutional structure in Unet, and an end-to-end trainable model is built for the precipitation forecasting problem. Experiments conducted on radar images from 2019 to 2022 show that the proposed method can better capture spatio-temporal correlations and outperforms fully connected long- and short-term memory networks and convolutional neural networks for short-time precipitation forecasting in areas around substations.

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  1. Substation flood precipitation forecasting model based on spatio-temporal sequence UNet depth network

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      PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
      January 2024
      969 pages
      ISBN:9798400716638
      DOI:10.1145/3674225
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 31 July 2024

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