Multivariate, Multistep Forecasting, Reconstruction and Feature Selection of Ocean Waves via Recurrent and Sequence-to-Sequence Networks
This work addresses ocean wave forecasting and reconstruction for applications like renewable energy, but it is incremental, building on existing neural network methods with specific optimizations and feature selection techniques.
The paper tackles multivariate, multistep forecasting and reconstruction of ocean wave characteristics, such as significant wave height and output power, using recurrent and sequence-to-sequence networks with Bayesian hyperparameter optimization and Elastic Net. Results show the proposed methods outperform alternatives on error metrics, with Adam and AMSGrad being robust optimizers, and deeper structures improving performance when features are numerous.
This article explores the concepts of ocean wave multivariate multistep forecasting, reconstruction and feature selection. We introduce recurrent neural network frameworks, integrated with Bayesian hyperparameter optimization and Elastic Net methods. We consider both short- and long-term forecasts and reconstruction, for significant wave height and output power of the ocean waves. Sequence-to-sequence neural networks are being developed for the first time to reconstruct the missing characteristics of ocean waves based on information from nearby wave sensors. Our results indicate that the Adam and AMSGrad optimization algorithms are the most robust ones to optimize the sequence-to-sequence network. For the case of significant wave height reconstruction, we compare the proposed methods with alternatives on a well-studied dataset. We show the superiority of the proposed methods considering several error metrics. We design a new case study based on measurement stations along the east coast of the United States and investigate the feature selection concept. Comparisons substantiate the benefit of utilizing Elastic Net. Moreover, case study results indicate that when the number of features is considerable, having deeper structures improves the performance.