Short-term Electricity Load Forecasting with BiGRU-Attention Based on Bayesian Optimization
The reliable supply of the power system is essential for industry,commerce and the life of residents.In order to meet the power demand and maintain the stable operation of the power system,it is especially critical to improve the accuracy and reliability of short-term power load forecasting.In view of the complex nonlinear characteristics of the load data,we propose a hybrid forecasting model based on Bayesian optimization algorithm with bidirectional gated recurrent unit and attention mechanism(BO-BiGRU-Attention)to accurately forecast the short-term power load.Firstly,the load data are normalized using the Min-Max Normalization method.Secondly,the BiGRU network is used to capture the long-term dependencies and contextual information in the sequences,and combined with the attention mechanism,the key features are highlighted by giving different weights in different parts of the input sequences.Finally,to address the problem that it is difficult to select the optimal solution for the hyperparameters of the BiGRU-Attention model,a Bayesian optimization algorithm is introduced to optimize the hyperparameters of the BiGRU-Attention model to complete the prediction of short-term electricity load.Electricity load data of a region in north India is used for forecasting analysis,and the simulation results show that the BO-BiGRU-Attention network outperforms the other models,with the minimum of each error evaluation index,in which the MAE,RMSE,and MAPE are 56.67,73.49,and 1.16%,respectively,and the prediction accuracy reaches 99.47% .
power systemload forecastingBayesian optimization algorithmbi-directional gated bad-cycle unitattention mechanism
万方数据
维普
