A hybrid model using 1D-CNN with Bi-LSTM, GRU, and various ML regressors for forecasting the conception of electrical energy

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dc.contributor.authorAbdulameer, Yahya Hafedh
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.date.accessioned2025-03-23T10:47:57Z
dc.date.available2025-03-23T10:47:57Z
dc.date.issued2025
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractTo solve power consumption challenges by using the power of Artificial Intelligence (AI) techniques, this research presents an innovative hybrid time series forecasting approach. The suggested model combines GRU-BiLSTM with several regressors and is benchmarked against three other models to guarantee optimum reliability. It uses a specialized dataset from the Ministry of Electricity in Baghdad, Iraq. For every model architecture, three optimizers are tested: Adam, RMSprop and Nadam. Performance assessments show that the hybrid model is highly reliable, offering a practical option for model-based sequence applications that need fast computation and comprehensive context knowledge. Notably, the Adam optimizer works better than the others by promoting faster convergence and obstructing the establishment of local minima. Adam modifies the learning rate according to estimates of each parameter's first and second moments of the gradients separately. Furthermore, because of its tolerance for outliers and emphasis on fitting within a certain margin, the SVR regressor performs better than stepwise and polynomial regressors, obtaining a lower MSE of 0.008481 using the Adam optimizer. The SVR's regularization also reduces overfitting, especially when paired with Adam's flexible learning rates. The research concludes that the properties of the targeted dataset, processing demands and job complexity should all be considered when selecting a model and optimizer.
dc.identifier.citationAbdulameer, Y. H., & Ibrahim, A. A. (2025). A hybrid model using 1D-CNN with Bi-LSTM, GRU, and various ML regressors for forecasting the conception of electrical energy. International Journal of Modern Physics C, 2441008.
dc.identifier.doi10.1142/S0129183124410080
dc.identifier.issn0129-1831
dc.identifier.issn1793-6586
dc.identifier.scopus2-s2.0-85217499225
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5730
dc.identifier.wosWOS:001413844900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.institutionauthorAbdulameer, Yahya Hafedh
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.institutionauthorid0000-0002-5599-348X
dc.language.isoen
dc.publisherWorld Scientific Publishing
dc.relation.ispartofInternational Journal of Modern Physics C
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGRU
dc.subjectLSTM
dc.subjectBi-LSTM
dc.subjectRMsprop
dc.subjectAdam
dc.titleA hybrid model using 1D-CNN with Bi-LSTM, GRU, and various ML regressors for forecasting the conception of electrical energy
dc.typeArticle

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