LSTM enhanced by dual-attention-based encoder-decoder for daily peak load forecasting

• A novel prediction model based on long short-term memory (LSTM) enhanced by dual-attention-based encoder-decoder is presented for daily peak load forecasting. • LSTM is functioned as the specific encoder and decoder for nonlinear dynamic temporal modeling. As the input sequence, the influence factors are converted into a feature vector by encoding. • A dual-attention mechanism is presented for taking into account the effects of different influence factors and time nodes on the daily peak load simultaneously. Feature attention mechanism seeks the relevant important factors while temporal attention mechanism integrates the factors across time nodes along with different weights for prediction. Daily peak load forecasting is a challenging problem in the filed of electric power load forecasting. Since the nonlinear and dynamic of influence factors and their sequential dependencies are significant for modeling daily peak load, a prediction model based on long short-term memory (LSTM) enhanced by dual-attention-based encoder-decoder is presented. Functioned as the specific encoder and decoder, LSTM is utilized to participate in the nonlinear dynamic temporal modeling. The encoder-decoder is used for information utilization of both the influence factors and daily peak load. Moreover, a dual-attention mechanism, which is inserted into the encoder-decoder, is designed to take into account the effects of different influence factors and time nodes on the daily peak load simultaneously. It is benefit for the above mechanism design to analyze the characteristics of daily peak load precisely and to achieve more accurate prediction results. Comprehensive experiments are performed based on a real set of one provincial capital city in eastern China. The case study shows that the proposed methodology provides the most accurate results with an average MAPE 2.07%, an average RMSE 133 MW and an average MAE 326.6 MW.