Work

Ultra-Short-Term Mooring Forces Forecasting for Floating Wind Turbines With Response-Frequency-Informed Deep Learning and On-Site Data

Year: 2024

Type: article

Source: Journal of Offshore Mechanics and Arctic Engineering

DOI: 10.1115/1.4067395

Authors: Yirou Kang, Zhengshun Cheng, Peng Chen, Longzhi Yang, Gareth Erfort, Lei Liu, Zhiqiang Hu

Institutions: Shanghai Jiao Tong University, State Key Laboratory of Ocean Engineering, Northumbria University, Stellenbosch University, University of Newcastle Australia, Newcastle University

Language: English

Topic: Wave and Wind Energy Systems

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: closed

Funder: National Natural Science Foundation of China

Awards: G2772559391, G4970973584, G6720105526

OpenAlex ID: W4405307267

FWCI: 0.174

Cites: 27

Cited by: 1

Related to: 10