Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting

• Published in: International journal of electrical power & energy systems 2019-07, Vol.109, p.470-479
• Main Authors: Wang, Shouxiang, Wang, Xuan, Wang, Shaomin, Wang, Dan
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Attention mechanism; Bi-directional long short-term memory; Deep learning; Load forecasting; Rolling update
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2019.02.022

•The attention mechanism (AM) is introduced for load forecasting model.•The rolling update (RU) is adopted to improve the accuracy of forecasting model.•The bi-directional long short-term memory (Bi-LSTM) neural network is applied for load forecasting.•The adaptive optimization algorithm root mean square prop (RMSprop) with rapid convergence is used to train model parameters. A Dropout technique is applied so as to prevent over-fitting training. Short-term load forecasting (STLF) plays an important role in the planning and operation of power systems. However, with the wide use of distributed generations (DGs) and smart devices in smart grid environment, it brings new requirements on the accuracy, quickness and intelligence of STLF. To address this problem, a novel short-term load forecasting method based on attention mechanism (AM), rolling update (RU) and bi-directional long short-term memory (Bi-LSTM) neural network is proposed. Firstly, RU is utilized to update the data in real time, making the input data of the model more effective. Secondly, influence weights are assigned through AM to highlight the effective characteristics of the input variables. Thirdly, a Bi-LSTM is used for model training, and the predicted load values are obtained through the linear transformation layer and softmax layer. Finally, the actual data sets from the New South Wales (NSW) and the Victoria (VIC) in Australia are employed to verify the validity of the method. The results show that the introduction of AM and RU into forecasting model can improve the prediction accuracy. Compared with traditional Bi-LSTM model, both the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of Bi-LSTM model with AM and RU have declined in the load forecasting for the two data sets. And it proves that the proposed method has higher accuracy, less computation time and better generalization ability.