Short-term power load forecasting based on Attention-LSTM
The accuracy of power load forecasting is interfered by many factors,such as climate change,economic development and regional differences,which make the power load present significant instability and complex nonlinear characteristics,thus increasing the difficulty of improving the forecasting accuracy.To address this challenge,this paper innovatively introduces a prediction method that combines self-attention mechanism with Long Short-term Memory Network(LSTM).The experimental results show that the coefficient of determination(R2)of this method is 0.96,the Mean Absolute Error(MAE)is 0.023,and the Root Mean Square Error(RMSE)is 0.029,which significantly improves the accuracy of prediction.This not only proves the effectiveness of the proposed model in improving the accuracy of power load forecasting,but also lays a certain foundation for its application in power load forecasting for ships.
万方数据
维普
