• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Renewable Power Generation, IET >Machine learning approach for optimal determination of wave parameter relationships
【24h】

Machine learning approach for optimal determination of wave parameter relationships

机译：机器学习方法可最佳确定波浪参数关系

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Wave parameter relationships have long been determined using methods that give non-standard and often inaccurate results. With increased commercial activity in the marine sector, the importance of accurate wave parameter relationship determination has become increasingly apparent. The outputs of many numerical models and buoy datasets do not include all requisite wave parameters, and a typical approach is to use a constant conversion factor or relationship based on defined spectra such as the Bretschneider or the joint North Sea wave observation project (JONSWAP) spectrum to determine these parameters. Given that relationships between wave parameters vary significantly over both hourly and seasonal and annual timescales, the currently employed methods are lacking, as subtleties are missed by the simpler approach. This paper addresses the determination of wave parameter relationships using a machine learning (ML)-based model, identifying and selecting the optimal method for the conversion of wave parameters (, ) in coastal Irish Waters. This approach is then validated at two sites on the West coast of Ireland. The aim is to highlight the utility of ML in approximating the relationship between wave parameters; using both buoy and modelled data, and mapping the predicted outcomes for a wave energy converter based on a variety of ML and measure correlate predict approaches.
机译：长期以来，波动参数之间的关系一直使用得出非标准且通常不准确的结果的方法来确定。随着海洋领域商业活动的增加，准确确定波浪参数关系的重要性变得越来越明显。许多数值模型和浮标数据集的输出未包含所有必需的波浪参数，典型的方法是使用恒定的转换因子或基于定义的光谱（例如Bretschneider或北海波浪联合观测项目（JONSWAP）光谱）的关系确定这些参数。考虑到波浪参数之间的关系在小时，季节和年度时间尺度上都存在显着变化，因此缺少当前采用的方法，因为较简单的方法会漏掉一些细微之处。本文介绍了使用基于机器学习（ML）的模型确定波浪参数关系的方法，确定并选择了爱尔兰沿海水域中波浪参数（，）转换的最佳方法。然后在爱尔兰西海岸的两个地点对该方法进行了验证。目的是强调ML在逼近波浪参数之间关系时的效用；使用浮标和建模数据，并基于各种ML绘制波能转换器的预测结果并测量相关的预测方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号