Short-Term Power Load Forecasting with Hybrid TPA-BiLSTM Prediction Model Based on CSSA

Abstract

Since the existing prediction methods have encountered difficulties in processing the multiple influencing factors in short-term power load forecasting, we propose a bidirectional long short-term memory (BiLSTM) neural network model based on the temporal pattern attention (TPA) mechanism. Firstly, based on the grey relational analysis, datasets similar to forecast day are obtained. Secondly, the bidirectional LSTM layer models the data of the historical load, temperature, humidity, and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network, so that the influencing factors (with different characteristics) can select relevant information from different time steps to reduce the prediction error of the model. Simultaneously, the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism, so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input. Finally, the chaotic sparrow search algorithm (CSSA) is used to optimize the hyperparameter selection of the model. The short-term power load forecasting on different data sets shows that the average absolute errors of short-term power load forecasting based on our method are 0.876 and 4.238, respectively, which is lower than other forecasting methods, demonstrating the accuracy and stability of our model.