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.

中文翻译：

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一种改进的灰色BP神经网络与MEEMD-ARIMA组合的日前波浪能预测方法

由于波浪是不断波动的，因此波浪能的预测对于大规模波浪能发电一体化的电力系统的运行具有重要意义。提出了一种基于改进灰色BP神经网络(BPNN)和改进集合经验模态分解(MEEMD)-自回归积分移动平均(ARIMA)的日前波浪能预测组合模型。首先，波浪理论将波浪分解为风浪和涌浪。其次，利用改进的灰色BPNN分析风浪与风速的相关性，根据历史风速数据预测风浪的平均高度。第三，利用MEEMD-ARIMA模型预测涌浪的平均波高。于是，结合风浪和涌浪，可以得到积分波的平均波高。最后，介绍了一种用于阿基米德摆动（AWS）的从波元到波能的转换模型。举例说明了使用来自真实海洋的实测风和波浪数据的案例研究，并验证了所提出的波浪能预测模型的有效性。