In order to fully explore the potential patterns of data and overcome the forecasting difficulties such as complexity and nonlinearity of power load,this paper proposes a hybrid forecasting model based on FNN-LSTM-Attention.Data features are extracted in the time dimension through feedforward neural network(FNN),different features are obtained,and long short-term memory(LSTM)is used to extract the impact of factors such as date and temperature on load.The Self-Attention layer is used to further explore data features and output predicted values.Taken actual load data from a certain region in Xinjiang as an example,the forecasting errors of different models are analyzed and compared.The results show that the proposed hybrid fore-casting model has smaller forecasting errors,proving the effectiveness of the model.
关键词
深度学习/电力负荷预测/长短期记忆网络/自注意力机制
Key words
deep learning/power load forecasting/long short-term memory network/Self-Attention mechanism
维普
万方数据