Deep Learning-Based Approaches for Short-Term Residential Electricity Consumption Prediction: A Comparative Study of LSTM, CNN-LSTM, and CNN-GRU Models - Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup (Avestia Publishing, 2025)
The rapid increase in the world population has increased electricity consumption and demand for electricity. In order for electricity distribution companies to determine electricity demand, household electricity consumption must be predicted. In this study, an open access dataset was used to predict individual household electricity consumption. For this purpose, deep learning-based LSTM, CNN-LSTM and CNN-GRU models were developed and a comparative analysis was performed. The performance of the models was evaluated using four evaluation metrics commonly used in prediction model performance evaluation. According to the results obtained, it was seen that the models used in the study were successful in predicting short-term electricity consumption. In particular, the CNNLSTM model was found to have a lower error rate compared to the other two models. © 2025, Avestia Publishing. All rights reserved.
11th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2025 -- 17 August 2025 through 19 August 2025 -- Paris -- 338299
Keywords: CNN-GRU; CNN-LSTM; deep learning; electricity consumption prediction; LSTM