A data-driven long-term metocean data forecasting approach for the design of marine renewable energy systems
A data-driven long-term metocean data forecasting approach for the design of marine renewable energy systems
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DOI：
10.1016/j.rser.2022.112751
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发表时间：
2022-07-11
影响因子：
15.9
通讯作者：
Iglesias, Gregorio
中科院分区：
文献类型：
--
作者：
Penalba, Markel;Aizpurua, Jose Ignacio;Iglesias, Gregorio
海洋可再生能源（MRE）系统的潜力通常是根据最近的海洋气象数据并假设 MRE 资源的稳定性来评估的。然而，文献中的不同研究表明了长期的资源变化，甚至海洋变暖与波浪能变化之间的联系。因此，准确描述未来资源的特征（包括这些长期变化）至关重要。为此，本文通过结合机器学习（ML）和海洋工程概念，提出了一种新颖的数据驱动预测方法。首先，对比斯开湾的历史资源进行了表征，包括根据通过 SIMAR 模型集合获得的数据集确定的不同长期趋势。其次，通过先进的统计技术提取和选择气象海洋数据集最相关的特征。最后，设计、验证和测试了三种不同的机器学习算法。尽管在再现特定峰值方面存在困难，但所有三个 ML 模型都证明能够充分表示数据集的整体模式。因此，针对三种不同的波高离散化水平提出了一种替代区间预测方法，显示出长期气象海洋数据预测的更大潜力。
The potential of Marine Renewable Energy (MRE) systems is usually evaluated based on recent metocean data and assuming the stationarity of the MRE resource. Yet, different studies in the literature have shown long-term resource variations and even the connection between ocean warming and wave power variations. Therefore, it is crucial to accurately characterise the future resource, including these long-term variations. To that end, this paper presents a novel data-driven forecasting approach through the combination of machine -learning (ML) and oceanic engineering concepts. First, the historical resource is characterised in the Bay of Biscay, including the different long-term trends identified based upon the dataset obtained via the SIMAR model ensemble. Secondly, the most relevant features of the metocean dataset are extracted and selected via advanced statistical techniques. Finally, three different ML algorithms are designed, validated and tested. All three ML models demonstrate to adequately represent the overall pattern of the dataset, although showing difficulties with reproducing particular peak values. Accordingly, an alternative interval prediction approach is presented for three different wave height discretisation levels, showing a greater potential for long-term metocean data forecasting.