A Combined Method of Improved Grey BP Neural Network and MEEMD-ARIMA for Day-Ahead Wave Energy Forecast

Since wave fluctuates continuously, the forecast of the wave energy is very important for the operation of power systems integrated with large-scale wave energy generation. A combined model of day-ahead wave energy forecast based on improved grey BP neural network (BPNN) and modified ensemble empirical mode decomposition (MEEMD) -autoregressive integrated moving average (ARIMA) is proposed in this paper. Firstly, the wave is decomposed into wind waves and swells by wave theories. Secondly, the correlation between wind wave and wind speed is analyzed with improved grey BPNN, and the average height of wind waves can be forecasted based on the historical wind speed data. Thirdly, the MEEMD-ARIMA model is utilized to forecast the average wave height of swells. Thus, combining the wind wave and the swell, the average wave height of the integrated wave can be obtained. Finally, a conversion model from wave elements to wave energy for Archimedes wave swing (AWS) is introduced. A case study using the measured wind and wave data from a real ocean is illustrated, and the effectiveness of the proposed wave energy forecast model is validated.