Significant wave height prediction through artificial intelligent mode decomposition for wave energy management

Abstract

The prediction of significant wave height (SWH) is crucial for managing wave energy. While many machine learning studies have focused on accurately predicting SWH values within hours in advance, the primary concern should be given to the level of the wave height for real-world applications. In this paper, a classification framework for the time-series of SWH based on Transformer encoder (TF) and empirical mode decomposition (EMD) is developed, which can provide a lead time of 6 to 48 h with the fixed thresholds of 2 m for high level waves and 1.5 m for low level waves. The performance of this approach is compared to that of three mainstream algorithms with and without EMD features. Results from the datasets collected from buoy measurements in the Atlantic Ocean indicate that the optimal mean accuracy at a lead time of 6 h was 99.1% and the average training time was 75 s, demonstrating the accuracy and efficiency of this proposed model. This study provides valuable tools and references for real-world SWH prediction applications.