Short-term power load forecasting is an important part of power system operation and control.To improve the accuracy of load forecasting and address the nonlinearity and randomness of actual load data,a short-term power load forecasting model is proposed based on the variational modal decomposition(VMD)and a bidirectional long short-term memory network(BiLSTM)optimized by the sparrow search algorithm(SSA).First,the VMD is used to decompose the power load data to extract multiple modal components with different frequency characteristics.Second,the SSA algorithm is introduced to optimize the BiLSTM network parameters.Then,the SSA-BiLSTM prediction model is established according to the input modal components for prediction.The results show that the proposed model has higher prediction accuracy and better fitting performance than the Bi LSTM model and the VMD-BiLSTM model.
关键词
短期电力负荷预测/变分模态分解/麻雀搜索算法/双向长短期记忆网络
Key words
short-term power load forecasting/variational mode decomposition(VMD)/sparrow search algorithm(SSA)/bidirectional long short-term memory(BiLSTM)network
维普
万方数据