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Dynamic Ensemble Deep Echo State Network for Significant Wave Height Forecasting

Applied Energy(2023)SCI 1区

Nanyang Technol Univ

Cited 32|Views149
Abstract
Forecasts of the wave heights can assist in the data-driven control of wave energy systems. However, the dynamic properties and extreme fluctuations of the historical observations pose challenges to the construction of forecasting models. This paper proposes a novel dynamic ensemble deep Echo state networks (ESN) to learn the dynamic characteristics of the significant wave height. The dynamic ensemble ESN creates a profound representation of the input and trains an independent readout module for each reservoir. To begin, numerous reservoir layers are built in a hierarchical order, adopting a reservoir pruning approach to filter out the poorer representations. Finally, a dynamic ensemble block is used to integrate the forecasts of all readout layers. The suggested model has been tested on twelve available datasets and statistically outperforms state-of-the-art approaches.
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Key words
Forecasting,Machine learning,Deep learning,Randomized neural networks,Echo state network
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要点】:本文提出了一种新型的动态集成深度回声状态网络(dynamic ensemble deep ESN),用于学习有效波高的动态特性,提高了波高预测的准确性,并在十二个数据集上超越了现有先进方法。

方法】:通过构建多个回声状态网络 reservoir 层,并对这些层采用 reservoir 裁剪方法,以过滤掉较差的表征,每个 reservoir 层都训练了一个独立的读出模块。

实验】:模型在十二个公开数据集上进行测试,结果表明该模型在统计上优于现有先进方法。