The bi-long short-term memory based on multiscale and mesoscale feature extraction for electric load forecasting

Abstract

Accurate power load prediction is beneficial to the efficient use of electric energy and the orderly development of power systems. Given the strong volatility and complexity of power load series, a hybrid load forecasting method based on multiscale and mesoscale information fusion, signal decomposition, model optimization, and bi-long-short-term memory (BiLSTM) is proposed. Firstly, the load sequence is analyzed on different time scales, and the extracted multi-scale information and mesoscale information are fused to improve the perception ability. Secondly, the empirical wavelet transform (EWT) with adaptive decomposition ability is used to decompose the sequence and extract the rich feature information. Thirdly, the complexity, volatility, and uncertainty of each mode component were analyzed, the data features were fully mined, and the feature fusion was carried out by the TOPISIS evaluation method. The BiLSTM model and the GWO-BiLSTM model are used to predict the low-frequency component and the high-frequency component, respectively. The optimization of Grey Wolf optimization (GWO) algorithm can improve the BiLSTM model's ability to learn long-term time series. Finally, the analysis of application examples shows that compared with various prediction models, the prediction error of mixed model EWT-SGEO-BiLSTM is the smallest, MAPE is as low as 1.07 %, and goodness of fit R2 is 0.99 which verifies the accuracy and applicability of the intelligent model.