阳曾,丁施尹,叶萌,李晶,薛书倩,吴昊天.基于变分模态分解和深度学习的短期电力负荷预测模型[J].电测与仪表,2023,60(2):126-131. Yang Zeng,Ding Shiyi,Ye Meng,Li Jing,Xue Shuqian,Wu Haotian.Short-term load forecasting based on VMD and LSTM[J].Electrical Measurement & Instrumentation,2023,60(2):126-131.
提升负荷预测的准确性对于指导电力系统的生产计划、经济调度以及稳定运行至关重要。提出一种基于变分模态分解(Variational Mode Decomposition, VMD)和长短期记忆(Long Short Term Memory, LSTM)神经网络的短期负荷预测模型。利用VMD算法将负荷序列分解成不同的本征模态函数(Intrinsic Mode Functions, IMF),每个IMF结合LSTM进行预测,将各部分预测结果叠加得到VMD-LSTM模型的预测结果。分析实验结果,相比单一LSTM和经验模态分解(Empirical Mode Decomposition, EMD)组合LSTM预测方法,该方法能有效的提升负荷预测的准确性。
英文摘要:
Improving the accuracy of load forecasting is very important to guide the production planning, economic dispatch and stable operation of power system. A short-term load forecasting model based on variational mode decomposition (VMD) and long short term memory (LSTM) neural network is proposed. VMD algorithm is used to decompose load series into different intrinsic mode functions (IMF), and each IMF is combined with LSTM network for prediction. The prediction results of VMD-LSTM model are obtained by superimposing the prediction results of each part. Through experimental simulation, compared with the single LSTM and empirical mode decomposition (EMD) combined LSTM prediction method, this method can effectively improve the prediction accuracy.
