基于CEEMDAN和DBO-GRNN的风电功率超短期预测

Ultra-Short-Term Prediction of Wind Power Based on CEEMDAN and DBO-GRNN

针对风电数据波动性过大而导致的风电功率预测不精确问题,提出一种基于自适应噪声完备集合经验模态分解(complementary ensemble empirical mode decomposition with adaptive noise,CEEMDAN)与蜣螂算法(dung beetle optimizer,DBO)优化的广义回归神经网络(generalized regression neural network,GRNN)超短期风电功率预测方法。首先将原始风功率序列进行时滞特性分析,选取与预测时刻相关性强的时序进行多路时序建模;然后对相关性强的时序进行CEEMDAN分解,得到一组本征模态分量(intrinsic mode function,IMF)和剩余分量;其次将上述两组分量输入经蜣螂优化算法优化的GRNN网络进行各分量预测;然后将各预测分量叠加,得到最终预测结果。算例分析表明,所提的CEEMDAN-DBO-GRNN预测模型的预测精度更高,而且CEEMDAN能够减少风电功率波动性与随机性对预测结果的影响,同时利用蜣螂算法优化后的超参数模型进行预测,在一定程度上提高了超短期风电功率预测的精度。

To address the problem of inaccurate wind power prediction caused by the excessive volatility of wind power data,this paper proposes a generalized regression neural network(GRNN)method based on the optimization of complementary ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and dung beetle optimizer(DBO).A combination of GRNN and DBO optimization is used for ultra-short-term wind power prediction.First,the original wind power sequence is subjected to time-lag characteristic analysis,and the time series with a strong correlation with the predicted moments is selected for multiplexed time-series modeling.Subsequently,the time series with strong time series are subjected to CEEMDAN decomposition,and a set of intrinsic mode functions(IMFs)and a residual term are obtained.Second,the two sets of the above components are inputted into the GRNN network optimized by the DBO algorithm for the prediction of the components.Subsequently,the prediction components are superimposed to obtain the final prediction result.Example analysis shows that the CEEMDAN-DBO-GRNN prediction model proposed in this paper has higher prediction accuracy,and CEEMDAN can reduce the influence of wind power volatility and randomness on the prediction results.The prediction of the hyperparameter model optimized by the DBO algorithm improves the accuracy of the ultra-short-term wind power prediction to a certain extent。