基于VMD-EMD-LSTM的日前电价预测

Day-Ahead Electricity Price Forecast Based on VMD-EMD-LSTM

针对电价非平稳性强的问题,为提高电价的预测精度,提出一种基于变分模态分解(VMD)、经验模式分解(EMD)和长短时记忆网络(LSTM)的混合预测模型。首先利用VMD将电价分解为若干子序列和残差项;针对残差项具有较强非平稳性的问题,利用EMD对残差项进一步分解;最后对各子序列分别利用LSTM模型进行预测,并将各子序列预测结果叠加得到最终预测电价。实验结果表明,该方法相比于其他对比方法具有更高的预测精度。

Aiming at the problem of strong un-stationarity of electricity price,in order to improve the forecast accuracy of electricity price,a hybrid forecast model based on Variational Mode Decomposition (VMD),Empirical Mode Decomposition (EMD) and Long Short-Term Memory Network (LSTM) is proposed.Firstly,it uses VMD to decompose the electricity price into several sub-sequences and residual terms.Then,for the problem that the residual terms have strong un-stationarity,it uses EMD to further decompose the residual terms.Finally,it uses LSTM model to forecast each sub-sequence respectively,and superimposes the forecast results of each subsequence to obtain the final forecast electricity price.The experimental results show that this method has higher forecast accuracy than other contrasting methods.