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

Yahya Hafedh Abdulameer and Abdullahi Abdu Ibrahim ()
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Yahya Hafedh Abdulameer: Institute of Graduate Studies, Altinbas University, Istanbul, Turkey
Abdullahi Abdu Ibrahim: Department of Computer Engineering, Altinbas University, Istanbul, Turkey

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 08, 1-25

Abstract: To 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.

Keywords: GRU; LSTM; Bi-LSTM; RMsprop; Adam (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124410080

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