2026-10-30T01:04:08Zhttps://munzur-rdlopen.rawdatalibrary.net/oai/request

oai:munzur-rdlopen.rawdatalibrary.net:munzur/19532026-10-28T15:29:20Zopenairedrivercom_munzur_1col_munzur_2com_munzur_8col_munzur_154

Deep Learning-Based Approaches for Short-Term Residential Electricity Consumption Prediction: A Comparative Study of LSTM, CNN-LSTM, and CNN-GRU ModelsGunay, MihribanYildirim, OzalDemir, YakupBenedicenti, L.Liu, Z.CNN-GRUCNN-LSTMdeep learningelectricity consumption predictionLSTMThe 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.Avestia Publishing2025Conference Objecthttps://rdlopen.rawdatalibrary.net/r/munzur/1953https://doi.org/10.11159/eee25.112978-199080061-02369-811XenProceedings of the World Congress on Electrical Engineering and Computer Systems and Scienceinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/conferenceObjecthttps://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/1953