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is 0.00025, both of which decrease as the number of epochs        [7] H. Cai, S. Shen, Q. Lin, X. Li, and H. Xiao, “Predict-
rises. Based on the aforementioned findings, we opt for the            ing the energy consumption of residential buildings for
70-piece batch size with the highest accuracy and lowest loss.         regional electricity supply-side and demand-side man-
                                                                       agement,” IEEE Access, vol. 7, pp. 30386–30397, 2019.
                   V. CONCLUSION
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a deep learning (DL) model that can automatically train and
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ing deep RNN, LSTM units, and accurately predict the load              “Modeling and forecasting of turkey’s energy consump-
demand consumption. The other proposal is called random                tion using socio-economic and demographic variables,”
forest, and when comparing deep learning and RF models,                Appl. Energy, vol. 88, no. 5, pp. 1927–1939, 2011.
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At the same time, RF was preferred based on the R2 Score be-     [10] K. B. Debnath and M. Mourshed, “Forecasting methods
cause it had the highest accuracy (98%), which was achieved            in energy planning models,” Renew. Sustain. Energy
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accuracy, future research will compare one deep learning
model and support vector machines.                               [11] H. Shi, M. Xu, and R. Li, “Deep learning for household
                                                                       load forecasting-a novel pooling deep rnn,” IEEE Trans.
              CONFLICT OF INTEREST                                     Smart Grid, vol. 9, no. 5, pp. 5271–5280, 2018.

   The authors have no conflict of relevant interest to this     [12] A. Rahman, V. Srikumar, and A. D. Smith, “Predicting
article.                                                               electricity consumption for commercial and residential
                                                                       buildings using deep recurrent neural networks,” Appl.
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