Effective Self-Attention-Based Deep Learning Model with Evolutionary Grid Search for Robust Wave Farm Energy Forecasting

TL;DR

This paper presents a novel deep learning model with self-attention and evolutionary hyperparameter tuning that significantly improves wave farm energy forecasting accuracy across multiple Australian sites, aiding renewable energy integration.

Contribution

It introduces a hybrid self-attention-based deep learning framework with grid search optimization for robust wave energy prediction, outperforming existing machine learning methods.

Findings

Achieved R2 scores above 82% across all tested sites.

Outperformed ten benchmark machine learning algorithms.

Demonstrated robustness and scalability in diverse marine environments.

Abstract

Achieving carbon neutrality, a key focus of UN SDG #13, drives the exploration of wave energy, a renewable resource with the potential to generate 30,000 TWh of clean electricity annually, surpassing global demand. However, wave energy remains underdeveloped due to technical and economic challenges, particularly in forecasting wave farm power output, which is vital for grid stability and commercial viability. This study proposes a novel predictive framework to enhance wave energy integration into power grids. It introduces a hybrid sequential learning model combining Self-Attention-enhanced Convolutional Bi-LSTM with hyperparameter optimization. The model leverages spatial data from Wave Energy Converters (WECs) and is validated using datasets from wave farms in Adelaide, Sydney, Perth, and Tasmania, Australia. Benchmarked against ten machine learning algorithms, the model achieves superior accuracy, with R2 scores of 91.7% (Adelaide), 88.0% (Perth), 82.8% (Tasmania), and 91.0% (Sydney). It outperforms conventional ML and deep learning methods, offering robust and scalable predictions for wave energy output across diverse marine environments, supporting reliable integration into energy systems.