标题： 基于有效自注意力的深度学习模型与进化网格搜索用于鲁棒波浪农场能源预测 显示英文标题

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

摘要： 实现碳中和是联合国可持续发展目标#13的关键重点，推动了波浪能的探索，这是一种可再生能源，每年有潜力产生30,000太瓦时的清洁能源，超过全球需求。然而，由于技术和经济挑战，波浪能仍发展不足，特别是在预测波浪农场发电量方面，这对电网稳定性和商业可行性至关重要。本研究提出了一种新的预测框架，以提高波浪能融入电网的能力。它引入了一个结合自注意力增强卷积双向长短期记忆网络与超参数优化的混合顺序学习模型。该模型利用来自波浪能转换器（WECs）的空间数据，并使用澳大利亚阿德莱德、悉尼、珀斯和塔斯马尼亚的波浪农场数据集进行验证。与十种机器学习算法相比，该模型实现了更高的准确性，R2分数分别为91.7%（阿德莱德）、88.0%（珀斯）、82.8%（塔斯马尼亚）和91.0%（悉尼）。它优于传统机器学习和深度学习方法，为不同海洋环境下的波浪能输出提供了强大且可扩展的预测，支持可靠地融入能源系统。 显示英文摘要

摘要： 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.