Abstract:
The peak period of an energy-generating wave is one of the most important parameters that describe the
spectral shape of the oceanic wave, as this indicates the duration for which the waves prevail with
respect to their maximum extractable energy. In this paper, a half-hourly peak wave energy period (TP)
forecast model is constructed using a suite of statistically significant lagged inputs based on the partial
auto-correlation function with an extreme learning machine model developed and its predictive utility is
benchmarked against deep learning models, i.e., convolutional neural network (CNN/CovNet) and
recurrent neural network (RNN) models and other traditional M5tree, Conditional Maximization based
Multiple Linear Regression (MLR-ECM) and MLR models. The objective model (ELM) vs. the comparison
models (CNN, RNN, M5tree, MLR-ECM, and MLR) were trained and validated independently on the test
dataset obtained from coastal zones of eastern Australia that have a high potential for implementation of
wave energy generation systems. The outcomes ascertain that the ELM model can generate significantly
accurate predictions of the half-hourly peak wave energy period, providing a good level of accuracy
relative to deep learning models in selected coastal study zones. The study establishes the practical
usefulness of the ELM model as being a noteworthy methodology for the applications in renewable and
sustainable energy resource management systems.
