Daily load prediction study on SCASL and BiGRU models

Abstract

In the short-term power load prediction, the environmental uncertainty will have an impact on the prediction accuracy and increase the complexity of the prediction. To deal with this problem, the paper proposes a bidirectional gated recurrent unit (BiGRU) network model for and sine cosine optimization algorithm with self-learning strategy and Levy flight (SCASL). With real power load data as a data set, high correlation parameters are selected through the Pearson correlation coefficient analysis as input, using the SCASL algorithm and nonlinear weight factor. Finally, the key time points in capturing the historical information of the load changes are established according to the optimization parameters, and BiGRU model is used to learn to predict its accuracy. The experimental results show that the MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) of BiGRU model are better than the comparison model gated recurrent unit (GRU) network model. The final results show that the prediction accuracy of the power load system has been significantly improved.