Wave energy forecasting: A state-of-the-art survey and a comprehensive evaluation
Published in Applied Soft Computing, 2025
Wave energy, a promising renewable energy source, has the potential to diversify the global energy mix significantly. Accurate forecasting of significant wave height (SWH) is crucial for enhancing the efficiency and reliability of wave energy conversion systems. As interest in this field grows, research into SWH forecasting has expanded dramatically. This comprehensive survey evaluates sixteen SWH forecasting methods, including Persistence, decision trees, deep neural networks, random neural networks, and random forests. The paper begins by establishing a detailed taxonomy that categorizes SWH forecasting algorithms, providing a framework to interpret the complexities of different methodological approaches. We then explore the interconnections between ensemble learning and decomposition-based frameworks and the integration of individual forecasting techniques within ensemble and hybrid models. In?
Recommended citation: Gao, R., Zhang, X., Liang, M., Suganthan, P. N., & Dong, H. (2025). Wave energy forecasting: A state-of-the-art survey and a comprehensive evaluation.?pplied Soft Computing, 112652.
Recommended citation: Gao, R., Zhang, X., Liang, M., Suganthan, P. N., & Dong, H. (2025). Wave energy forecasting: A state-of-the-art survey and a comprehensive evaluation.?pplied Soft Computing, 112652. https://doi.org/10.1016/j.asoc.2024.112652