Improved short-term prediction of significant wave height by decomposing deterministic and stochastic components
Improved short-term prediction of significant wave height by decomposing deterministic and stochastic components
复制标题
通过分解确定性和随机分量改进有效波高的短期预测
DOI：
10.1016/j.renene.2021.06.008
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发表时间：
2021
期刊：
影响因子：
8.7
通讯作者：
Dong Sheng
中科院分区：
文献类型：
--
作者：
Huang Weinan;Dong Sheng
对于波浪能装置的规划和操作来说，必须预测接下来几个小时的有效波高。对于特定地点的短期预报，经典的数值波浪预报方法可能不合理，因为需要详尽的气候数据和巨大的计算能力。本文提出了一种分解方法和长短期记忆网络相结合的有效波高预报方法。采用改进的完全集成经验模式分解算法和递归量化分析将原始时间序列分解为确定性和随机性分量。每个分解序列通过长短期记忆网络进行预测，并通过集成确定性和随机预测得到最终的预测有效波高。波测量数据在三个浮标站沿着美国东部海岸被用来验证混合模型。所提出的方法在三个不同的波高范围内的性能进行了评估。结果表明，混合模型优于独立的长短期记忆网络调整的未分离的信号，特别是，较长的前置时间和较大的波高。
Significant wave height prediction for the following hours is a necessity for the planning and operation of wave energy devices. For a site-specific and short-term prediction, classical numerical wave forecasting methods may not be justified as exhaustive climatological data and huge computational power are needed. In this paper, a combination of a decomposition approach and long short-term memory network was presented to forecast the significant wave heights. An improved version of complete ensemble empirical mode decomposition algorithm and recurrence quantification analysis were applied to separate the original time series into deterministic and stochastic components. Each decomposed series was forecasted by the long short-term memory network and the final predicted significant wave heights were obtained by integrating the deterministic and stochastic predictions. Wave data measured at three buoy stations along the eastern coast of the United States were utilized to verify the hybrid model. The performance of the proposed method in three different wave height ranges was evaluated. The results suggested that the hybrid model outperformed the stand-alone long short-term memory network adjusted on the unseparated signal; in particular, for longer lead times and larger wave heights.
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作者：
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DOI：
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发表时间：
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期刊：
Estuarine, Coastal and Shelf Science
影响因子：
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作者：
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通讯作者：
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DOI：
10.1080/00031305.2014.917055
发表时间：
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期刊：
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影响因子：
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作者：
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