Dynamic ensemble deep echo state network for significant wave height forecasting

Ruobin Gao, Ruilin Li, Minghui Hu, Ponnuthurai Nagaratnam Suganthan and Kum Fai Yuen

Applied Energy, 2023, vol. 329, issue C, No S0306261922015185

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.

Keywords: Forecasting; Machine learning; Deep learning; Randomized neural networks; Echo state network (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (10)

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DOI: 10.1016/j.apenergy.2022.120261

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