Research on Short-term Power Load Forecasting Based on FNN-LSTM-Attention
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.
deep learningpower load forecastinglong short-term memory networkSelf-Attention mechanism