基于CEEMD-GA-BP神经网络的风光发电功率预测

Forecast of Wind and Solar Power Generation Based on CEEMD-GA-BP Neural Network

传统的BP神经网络(back propagation neural network,BPNN)虽然在功率预测方面已有广泛应用,但其对于随机波动性较强的风光发电功率预测准确度较低。文中提出一种基于CEEMD(complementary ensemble empirical mode decomposition)方法优化的遗传算法神经网络(genetic algorithmBPNN,GA-BPNN)模型,首先用CEEMD方法将原始数据分解成易于预测的分量,并将各分量预测结果集总平均得到最终结果。以德国巴登-符腾堡州地区能源系统中风光发电功率的历史实例验证该模型的效果,并与其地预测模型进行对比,结果表明,无论是日前预测还是超短期预测,文中所提模型能够提高风光发电功率预测的准确度。

Although the traditional BP neural network has been widely used in power prediction,it has low accuracy for forecasting wind and solar power generation which has strong random fluctuations.this paper proposes an optimized GA-BP network model based on the CEEMD method.First,the CEEMD method is used to decompose the original data into predictable components;second,the forecast result sets of each component are aggregated and averaged to obtain the final result.The effect of this model is verified using historical examples of wind and solar power generation in the energy system of BadenWürttemberg,Germany.The results of comparison with other prediction models show that the model proposed in this paper can improve the forecast accuracy of wind and solar power generation whether it is a day-ahead forecast or an ultra-shortterm forecast.