Wave Power Density Prediction With Wind Condition By LSTM in the Gulf of Mexico

Abstract: The uncertainty and enormous potential have been brought up high attention and plenty of research on exploration of wave energy. The movement of wave transfer generates huge available renewable energy and create quite harness on harvesting and prediction. To avoid and reduce the huge effect of spatial and temporal variation on wave energy, systematic analysis and prediction on wave condition are imminent. The dynamic behaviors of wave energy were displayed in this paper in a format of wave power density distribution, which was extracted and visualized in MATLAB. The scenario explored the location effect for wave density forecasting, which the RSME were implemented and visualized as a criteria estimator of the accuracy. The data period ranges from 1979 to 2019 in Gulf of Mexico, the lowest accuracy with different location effect in terms of RSME is 0.104. The method has the possibility to implement in other wave thriving locations, which fill the gap of forecasting on wave height and period based on buoy data for lack of measurements, as well as reflecting the correlations between wind speed and wave density, provide support on quantitative the correlation model based on deep learning-base model.