Prediction of the wave power in the Black Sea based on wind speed using artificial neural networks

Wind Speed ◽

Wave Height ◽

Black Sea ◽

Significant Wave Height ◽

The Black Sea ◽

Wave Power ◽

Significant Wave ◽

The paper proposes a prediction methodology for the significant wave height (and implicitly the wave power), based on the artificial neural networks. The proposed approach takes as input data the wind speed values recorded for different time periods. The prediction of significant wave height is useful both for assessment of wave energy as also for marine equipment design and navigation. The data used cover the time interval 1999 to 2007 and it was measured on Gloria drilling unit, which operates in the Romanian nearshore of the Black Sea at about 500 meters depth.

Simultaneous short-term significant wave height and energy flux prediction using zonal multi-task evolutionary artificial neural networks

Renewable Energy ◽

10.1016/j.renene.2021.11.122 ◽

2021 ◽

Author(s):

A.M. Gómez-Orellana ◽

D. Guijo-Rubio ◽

P.A. Gutiérrez ◽

C. Hervás-Martínez

Keyword(s):

Neural Networks ◽

Wave Height ◽

Energy Flux ◽

Significant Wave Height ◽

Short Term ◽

Significant Wave ◽

Application of the method of artificial neural networks to the downscaling of precipitation forecasts in the coastal region of the Black Sea

Physical Oceanography ◽

10.1007/s11110-006-0021-1 ◽

2006 ◽

Vol 16 (3) ◽

pp. 141-152 ◽

Cited By ~ 2

Author(s):

V. V. Efimov ◽

V. L. Pososhkov

Keyword(s):

Neural Networks ◽

Black Sea ◽

Coastal Region ◽

The Black Sea ◽

International Journal of Advanced Research in Computer Science and Software Engineering ◽

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

10.23956/ijarcsse.v7i9.404 ◽

2017 ◽

Vol 7 (9) ◽

pp. 13

Author(s):

Bhargavi Munnaluri ◽

K. Ganesh Reddy

Keyword(s):

Neural Networks ◽

Wind Speed ◽

Fossil Fuels ◽

Renewable Energy Sources ◽

Support Vector ◽

Hybrid Forecasting ◽

Artificial Neural ◽

Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed. In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions

Energy Conversion and Management ◽

10.1016/j.enconman.2014.12.053 ◽

2015 ◽

Vol 92 ◽

pp. 67-81 ◽

Cited By ~ 81

Author(s):

Hui Liu ◽

Hong-qi Tian ◽

Yan-fei Li ◽

Lei Zhang

Keyword(s):

Neural Networks ◽

Wind Speed ◽

Adaboost Algorithm ◽

Wave Energy Assessment in the Bohai Sea and the Yellow Sea Based on a 40-Year Hindcast

Water ◽

10.3390/w12082087 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2087

Author(s):

Jie Dong ◽

Jian Shi ◽

Jianchun Zhao ◽

Chi Zhang ◽

Haiyan Xu

Keyword(s):

Wave Height ◽

Wave Energy ◽

Yellow Sea ◽

Significant Wave Height ◽

Bohai Sea ◽

The Yellow Sea ◽

Wave Power ◽

Power Flux ◽

Significant Wave ◽

The Bohai Sea

A wave hindcast, covering the period of 1979–2018, was preformed to assess wave energy potential in the Bohai Sea and the Yellow Sea. The hindcase was carried out using the third generation wave model TOMAWAC with high spatio-temporal resolution (about 1 km and on an hourly basis). Results show that the mean values of significant wave height increase from north to south, and the maximum values are located at the south part of the Yellow Sea with amplitude within 1.6 m. The magnitudes of significant wave height values vary significantly within seasons; they are at a maximum in winter. The wave energy potential was represented by distributions of the wave power flux. The largest values appear in the southeast part of the numerical domain with wave power flux values of 8 kW/m. The wave power flux values are less than 2 kW/m in the Bohai Sea and nearshore areas of the Yellow Sea. The seasonal mean wave power flux was found up to 8 kW/m in the winter and autumn. To investigate the exploitable wave energy, a wave energy event was defined based on the significant wave height (Hs) threshold values of 0.5 m. The wave energy in south part of the Yellow Sea is more steady and intensive than in the other areas. Wave energy in winter is more suitable for harvesting wave energy. Long-term trends of wave power availability suggest that the values of wave power slightly decreased in the 1990s, whereas they have been increasing since 2006.