Forecasting of Electrical Energy Consumption Using Hybrid Models of GRU, CNN, LSTM, And ML Regressors

Basit Öğe Kaydı
dc.contributor.authorAbdulameer, Yahya Hafedh
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.date.accessioned2025-07-30T06:30:15Z
dc.date.available2025-07-30T06:30:15Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.description.abstractElectricity consumption predictions for a long period are critical in the institutions that distribute the electricity and governmental or private entities that supply the electricity. It guarantees optimum energy utilization and aids in making strategic decisions for improving the energy production quality. This need is especially important in nations like Iraq, which has suffered from energy crises for many years. This study uses daily household electricity consumption data acquired from the Ministry of Electricity in Iraq, namely the Rusafa area of Baghdad, from 2022 to 2024. Weather data for the same years was also included, which contains external weather factors such as temperature, humidity, and solar radiation that directly influence consumption patterns. This paper proposes a hybrid forecasting model that utilizes advanced deep learning architectures LSTM and CNN-based deep learning architectures for forecasting along with an upgraded stacked hybrid model that employs CNN, GRU, Stacked Bi-LSTM, and machine learning regressors, such as XGBoost Regressor, and LightGBM Regressor. These models are being trained to improve accuracy in the forecast and to improve energy acoustic production strategies. The 30 epochs were trained and evaluated on the proposed model using the mean relative absolute error (MAPE) and mean root mean square error (RMSE) to examine the prediction quality. Among all models tested, the best performance was achieved using LightGBM regressor in our hybrid model with MAPE and RMSE of periodic forecasts for the next spilled of time being 0.185155 and 0.094603, respectively. The results show the potential of hybrid modeling techniques for energy forecasts and electricity distribution systems optimization.
dc.identifier.citationAbdulameer, Y. H., & Ibrahim, A. A. (2025). Forecasting of Electrical Energy Consumption Using Hybrid Models of GRU, CNN, LSTM, And ML Regressors. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 16(1), 560-575. 10.58346/JOWUA.2025.I1.033
dc.identifier.doi10.58346/JOWUA.2025.I1.033
dc.identifier.endpage575
dc.identifier.issn2093-5374
dc.identifier.issn1793-6586
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105003058693
dc.identifier.scopusqualityQ2
dc.identifier.startpage560
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5804
dc.identifier.volume16
dc.indekslendigikaynakScopus
dc.institutionauthorAbdulameer, Yahya Hafedh
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherInnovative Information Science and Technology Research Group
dc.relation.ispartofJournal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCNN
dc.subjectGRU
dc.subjectLightGBM
dc.subjectLSTM
dc.subjectXGBoost
dc.titleForecasting of Electrical Energy Consumption Using Hybrid Models of GRU, CNN, LSTM, And ML Regressors
dc.typeArticle

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
Ä°sim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
Ä°ndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu