1674-7070
article
基于自适应VMD-LSTM的超短期风电功率预测
Ultra-short term wind power prediction based on adaptive VMD-LSTM
针对风电功率波动性较强和预测精度较低的问题，提出了一种改进蜣螂优化算法(logistic-t-dung beetle optimizer, LTDBO)优化变分模态分解(variational mode decomposition, VMD)参数和LTDBO算法优化长短期记忆网络(long short-term memory, LSTM)超参数的混合短期风电功率预测模型。首先以平均包络谱峭度作为适应度函数，利用LTDBO算法对VMD分解层数和惩罚因子进行寻优，然后使用VMD对数据清洗后的风电序列进行分解，得到不同频率的平稳的固有模态分量(intrinsic mode function，IMF)；将各IMF输入由LTDBO进行超参数寻优的LSTM进行预测，最后将各IMF预测值进行叠加重构，得到最终结果。实验结果表明：LTDBO算法可以找到VMD和LSTM的最优超参数组合，LTDBO-VMD-LTDBO-LSTM组合模型在风电功率预测领域具有较好的预测精度和鲁棒性。
Aiming at the problems of strong fluctuation of wind power and low prediction accuracy, An improved logistic-t-dung beetle optimizer (LTDBO) is proposed to optimize variational mode decomposition. VMD) parameters and LTDBO algorithm optimize the hybrid short-term wind power prediction model of long short-term memory network (LSTM) hyperparameters. First, the kurtotic of the mean envelope spectrum is used as the fitness function, and LTDBO algorithm is used to optimize the decomposition layers and penalty factors of VMD. Then, the wind power sequence after data cleaning is decomposed with VMD to obtain the stationary intrinsic mode function (IMF) of different frequencies. Each IMF input was predicted by LSTM with LTDBO's hyperparameter optimization. Finally, each IMF predicted value was superposed and reconstructed to obtain the final result. The experimental results show that the LTDBO algorithm can find the optimal combination of VMD and LSTM hyperparameters, and the combined model of LTDBO-VMD-LTDBO-LSTM has better prediction accuracy and robustness in the field of wind power prediction.
风电功率；蜣螂优化算法；；变分模态分解；长短期记忆网络；数据清洗
Wind power; Dung beetle optimization algorithm; Variational mode decomposition; Long short-term memory network; Data clean
王迪,傅晓锦,杜诗琪
wangdi,fuxiaojin,dushiqi
上海电机学院
Shanghai DianJi Univerisity
njqxxyxb/article/abstract/20240404002