计及误差修正的变分模态分解￣长短期记忆 神经网络短期负荷预测.

Short-term load forecasting lays the foundation for the economic dispatch of the power system and optimal load distribution of units. Therefore, a short-term load forecasting model combined variational modal decomposition(VMD) and long short-term memory neural network(LSTM) was proposed, and support vector regression(SVR) was used to construct a revised error sequence to compensate the initial prediction sequence. Firstly, the VMD algorithm was used to decompose the non-stationary original load sequence into multiple relatively stable modal components. Then, each modal component was input to the LSTM model for prediction, and the prediction results of each component were combined to obtain the prediction result of VMD-LSTM. Finally, the residual value of the model was input into the SVR model to construct an error sequence to correct the VMD-LSTM load forecast results of the next day. Through actual case tests, the experimental results have lower prediction errors compared with the results of other models, which proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]