Winds and waves for the design of floating type wind farm were evaluated by using variuous kinds of re-analysis and prediction data including NCEP wind data, JMA meteorological data, NEDO data and Hourly GPV data. Statistical values of winds and waves for several return periods were obtained. Wave characteristics were determined for maximum wave height, crest height, 2D height-period distribution, wave energy spectrum and so on. Tide, tidal current and wind-induced current were also evaluated.Professor Ocean University of China 1
The floating type wind turbine demonstration project has been promoted in Japan. In 2012, a 1:2 scale model was installed off Kabashima Island in Nagasaki Prefecture. And a year later, a full scale model was installed. For the design of the wind turbine's floating body, winds, waves and other parameters were analyzed. For the construction and daily management, a prediction system was developed and the predictions and observations of winds and waves were compared and the agreement between them was good.
Accurate and timely wave forecast is vital for port maintenance and management as well as general maritime safety. Modern numerical wind-wave modeling calculates the physical processes involved in wave generation, interaction, propagation, and dissipation, requiring large amounts of computational power to timely complete the required calculations. Convolutional Neural Networks (CNNs) have shown to improve wave prediction in viewpoints of lesser computational cost and processing speed. This study examines the application of Xception deep learning architecture for wave predictions along Japanese coasts of the Sea of Japan with input features of the Japan Meteorological Agency's Grid Point Value Meso-Scale Model (GPV-MSM). In particular, this study aims to verify the forecast skill for a testing period of one year and to understand how wave forecasts by deep learning models can be improved and utilized.
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