Research on Short-term Load Forecasting Based on GWO-BILSTM

Abstract

In short-term power load forecasting, changes in external multi-dimensional factors will have a certain impact on the accuracy of load forecasting. In response to this problem, this paper proposes a prediction model GWO-BILSTM that combines the Grey Wolf Optimizer (GWO) and the bi-directional long short-term memory (BILSTM) network. Taking the real power load data as the data set, the high correlation parameters are selected as the input through the Pearson correlation analysis, the hyperparameters of BILSTM are optimized by the GWO algorithm, and finally the GWO-BILSTM prediction model is established based on the optimized parameters to predict the data set. The experimental results show that the mean absolute percentage error, root mean square error and mean absolute error index of the GWO-BILSTM model are better than other comparison models, which effectively improves the prediction accuracy of multi-dimensional power load data.