Comparative Study of LSTM, GRU, and CNN-Based Hybrid Models for Short-Term Load Forecasting

Abstract

This study investigates the effectiveness of long short-term memory (LSTM) networks, Gated Recurrent Unit Networks (GRU), and their hybrid models with a Convolutional Neural Network (CNN), namely CNN-GRU and CNN-LSTM, in the short-term electrical load forecasting of practical power systems. This study uses hourly load data spanning 11 years from 2014 to 2024 from a typical regional transmission network. A set of experiments was conducted using various input configurations, model complexities, and activation functions. The models were tested in univariate and multivariate settings, including ten input features and recurrent layer variations. The results indicate that GRU consistently achieves the best performance in terms of mean absolute percentage error, whereas CNN-RNN hybrid models show sensitivity to the depth of architecture and choice of activation function. These deep learning methods were compared in terms of their model complexity and forecasting accuracy to provide guidance for selecting the optimal models for short-term electrical load forecasting.