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Reducing the dependence on fossil fuels and utilizing the renewable energy have become essential due to the global resource exhaustion and unfriendly environmental impact from coal, petroleum and natural gas. Therefore, the rising attention has been paid to wave energy characterized by sustainability, clean, high energy density and extensive distribution. As one of the most important parameters of wave energy, significant wave height (SWH) is difficult to forecast accurately due to the complex marine condition and ubiquitous presence of chaos in nature. In this research, a novel hybrid model called STL–CNN–PE which combines seasonal-trend decomposition procedure based on loess (STL) and one-dimensional convolutional neural networks (CNN) with positional encoding (PE) was proposed to forecast SWH efficiently and accurately. To evaluate the proposed model comprehensively, the hourly standard meteorology data at station 44007, 46087 and 51000 from NOAA’s National Data Buoy Center were selected for model training and testing. The experimental results indicated that STL–CNN–PE provided more reliable forecasting values than the single model. Meanwhile, STL–CNN–PE had enormous advantage on speed and similar precision compared with EMD-LSTM. Finally, the experimental results revealed that the models provided better forecasting metrics at deeper waters.

For the survival and development of ‘One Belt, One Road’, the present work aimed to evaluate the current situation of wave energy resources around Sri Lankan (SL) waters. Thirty-year ERA-Interim wind data were used to drive the third-generation wave model WAVEWATCH-III, and the seasonal and regional distribution characteristics of wave energy resources in SL waters were analyzed. Furthermore, the optimal season and region that contribute most to wave power in the study area were determined. On the basis of 30-year hindcast wave data, the significant wave height and wave power density, the occurrence of available SWH and rich WPD, the effective storage of wave energy, and the contribution and stability of wave energy were also analyzed. Results show that extremely optimistic wave energy resources are found at the western, southern, and southeastern waters of SL; moreover, the period of June, July, August (JJA) has great advantages in terms of the overall level of WPD, wave energy effective storage, and the contribution rate of wave energy. In addition, the wave energy during JJA is more stable than that of other periods and thus is beneficial to the transformation and development of wave energy. This study also provides important guiding value for disaster prevention and reduction, coastal zone management, and coastal development in the crucial region of the 21st Century Maritime Silk Road.

Based on 28 years of hindcast data, this paper describes in detail the long-term variation trend of wave power density in the South China Sea. WAVEWATCH-III (WW3), the third generation wave computing model, which plays an important role in wave prediction. WW3 was used for inverse calculation of wave parameters, with spatial resolution of 0.25° × 0.25° and time resolution of 6h. The validity of the data was verified by three typhoon processes and the significant wave height data of the satellite altimeter in 2010. According to 28-year(1990-2017) hindcast data set, the author respectively mapped the South China Sea mean monthly, seasonal, annual wave power density (WPD) and the change trend diagram, including the long-term trend of WPD Mann-Kendall (MK) test, come the following conclusions: (1) Influencing the area WPD change trend of the dominant increasing season is winter (DJF), in which season average growth of WPD is about 1kW/m, and the largest waters is closed to 3kW/m. (2)The dominant growing month for WPD is in December, by compared with the before and next month in the largest growing sea area, it shows an increase and decrease trend of 10kW/m and 20kW/m respectively. (3)MK test results indicate that the long-term change of WPD keep on an increasing trend, but it is not significant, among which WPD had an increased mutation in 2000. (4)The dominant sea area is around Luzon Strait-Taiwan Strait- D.S Islands (116°E, 21°N). The average growth of WPD among these area in 28 years are about 0.15-0.25(kW/m)/yr.