[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120710-en":3,"doc-seo-120710-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120710,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments","Accurate prediction of ocean waves underpins wave energy converter control and floating wind turbine operation, yet most machine-learning studies for phase-resolved forecasting rely on two-dimensional data, while real ocean waves are inherently three-dimensional. This work formulates phase-resolved wave elevation prediction as supervised learning from historical wave measurements. Multiple learning models are evaluated and a Dual-Branch Network (DBNet) is proposed. Nine directional spectra across three sea states are collected in wave-basin experiments for validation.","Applied Energy 348 (2023) 121529  \n| Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments\u003Cbr>Rui Li a, Jincheng Zhang a, Xiaowei Zhao a,∗, Daming Wang b, Martyn Hannb, Deborah Greaves b\u003Cbr>a Intelligent Control & Smart Energy (ICSE) Research Group, School of Engineering, University of Warwick, Coventry, UK b School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>3D waves\u003Cbr>Convolutional neural network Machine learning\u003Cbr>Multilayer perceptron\u003Cbr>Phased-resolved wave forecasting Wave tank experiments |  | Accurate prediction of ocean waves plays an essential role in many ocean engineering applications, such asthe control of wave energy converters and floating wind turbines. However, existing studies on phase-resolved wave prediction using machine learning mainly focus on two-dimensional wave data, while ocean waves are usually three-dimensional. In this work, we investigate, for the first time, the phase-resolved real-time prediction of three-dimensional waves using machine learning methods. Specifically, the wave prediction is modeled as a supervised learning task aiming at learning mapping relationships between the input historical wave data and the output future wave elevations. Four frequently-used machine learning methods are employed to tackle this task and a novel Dual-Branch Network (DBNet) is proposed for performance improvement. A group of wave basin experiments with nine directional wave spectra under three sea states are first conducted to collect the data of 3D waves. Then the wave data are used for verifying the effectiveness of the machine learning methods. The results demonstrate that the upstream wave data measured by the gauge array can be used for control-oriented wave forecasting with a forecasting horizon of more than 20 s, where the directional information provided by the upstream gauge array is vital for accurately predicting the downstream wave elevations. In addition, further investigations show that by using only local wave data (which can be easily obtained), the very short-term phase-resolved prediction (less than 5 s) can be achieved. |\n\n1. Introduction  \nAs one of the main renewable energy sources, wave energy is an important and promising low-carbon alternative to fossil fuels. Although many kinds of Wave Energy Converters (WECs) have been designed and verified [1,2], when compared to solar and wind energy, wave energy is still far from being commercially competitive [3]. One major challenge in further reducing the cost of wave energy is the design of a control technique suitable for various sea states. To improve the control performance, the preview-based hydrodynamic control [4–6] has been proposed where the controller is designed to react in advance before the waves hit the WEC structures. It can significantly enhance the power generation of WECs [7]. For example, the investigation of an Azura WEC under experimental conditions showed that a 36% improvement in power generation could be achieved by the Model Predictive Control (MPC) compared with the standard fixed damping control [8]. However, the WEC control is a non-causal optimal control problem [9] where the current control decision must be based on  \nthe future wave excitation force [6]. Thus, the real-time forecasting of the wave information is essential for executing energy-maximizing controllers [10]. A feasible and promising scheme to obtain the future wave excitation force is to compute it from wave elevation predictions [11,12]. Indeed, as an essential technology in WEC control design, wave elevation prediction has drawn a lot of attention and has now become an active research area.  \nBased on the spectral transport and energy balance equations, the traditional phase-averaged wave forecasting method aims at predicting the wave spectrum instead of the ","cbCaidH9TxaVbkzu","https://ap.wps.com/l/cbCaidH9TxaVbkzu","pdf",4351046,1,14,"English","en",105,"# Introduction\n## Motivation for phase-resolved real-time wave forecasting\n## Limitations of traditional phase-averaged forecasting models\n# Methodology and experimental data\n## Supervised learning formulation\n## Machine learning models and proposed DBNet\n## Wave basin experiments and validation approach\n# Results and discussion\n## Forecasting horizon using upstream gauge array\n## Short-term phase-resolved prediction using local wave data","[{\"question\":\"What is the main goal of the study on ocean wave forecasting?\",\"answer\":\"To achieve phase-resolved, real-time prediction of three-dimensional ocean wave elevations using machine learning.\"},{\"question\":\"How is the forecasting problem formulated for machine learning?\",\"answer\":\"As a supervised learning task that learns the mapping between input historical wave data and output future wave elevations.\"},{\"question\":\"What experimental setup is used to collect data for evaluation?\",\"answer\":\"Wave-basin experiments are conducted using nine directional wave spectra under three sea states, and the collected 3D wave data are used to verify the models.\"}]","Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments | 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is the main goal of the study on ocean wave forecasting?","Question",{"text":75,"@type":76},"To achieve phase-resolved, real-time prediction of three-dimensional ocean wave elevations using machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the forecasting problem formulated for machine learning?",{"text":80,"@type":76},"As a supervised learning task that learns the mapping between input historical wave data and output future wave elevations.",{"name":82,"@type":73,"acceptedAnswer":83},"What experimental setup is used to collect data for evaluation?",{"text":84,"@type":76},"Wave-basin experiments are conducted using nine directional wave spectra under three sea states, and the collected 3D wave data are used to verify the 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