# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 5 Experimental Results
### Evaluation of the Proposed Model, DNN Model, and LSTM Model  
As shown in Figures 5-10, the hybrid DNN multilayered LSTM model performed slightly better than the LSTM model and much better than the DNN model. The train and test error lines in the model loss graphs showed that the LSTM model and the proposed model performed almost equally and that both were much better than the DNN model. Moreover, the graphs of the actual versus predicted values showed that the DNN model prediction performed poorly, while the prediction performance of the proposed model and the LSTM model was excellent. Table 2 shows the evaluation metrics for the three models to compare their performances. Figure 10: LSTM model, actual vs. prediction. Figure 9: DNN model, actual vs. prediction. The proposed model showed better performance than the DNN model, with a percentage of improvement of 97.05% in RMSE, 97.49% in MAE, and 97.35% in MAPE. This shows the superiority of the proposed model over the DNN model. Conversely, the proposed model has a slightly better improvement than the LSTM model, with a percentage of improvement of 3.5% in RMSE, 5.5% in MAE, 6.7% in MAPE, and 0.007% in \(R^{2}\). This shows that the proposed model performed better than using the DNN or LSTM alone. The hybrid DNN multilayered LSTM model was also compared with other deep learning models in previous studies that used the IHEPC dataset to determine the model's performance, as shown in Table 3. The model's performance was evaluated against the CNN multilayered bidirectional LSTM [29], linear regression algorithm [30], CNN-BiGRU [32], GRU [28], CNN-ESN [33], and R-CNN with ML-LSTM [34]. The proposed model was found to be better than the evaluated models, including the GRU [28] and the CNN multilayered bidirectional LSTM [29]. [PERSON], [PERSON], and [PERSON] attested to the adequate potential of hybrid models in the extraction of spatiotemporal features from historical electricity consumption data and renewable power generation [33]. In all the models evaluated, the ability to make accurate pattern predictions stands out, despite the various improvements to increase their reliability in power predictions. Compared with the other models shown in Table 3, the proposed model had the lowest error rates and the highest \(R^{2}\) value, indicating that the predictions made by the model had minimal errors and were identical to the observed values. The comparison in Table 3 showed a variety of models whose structure and parameters are different from those of the proposed model, except for the CNN multilayered bidirectional LSTM and R-CNN with ML-LSTM. The two models incorporated LSTM, which has the same structure and parameters as the proposed model. However, all the models had lower prediction accuracy than the proposed model, as indicated by the MAPE values. Only two models used \(R^{2}\) as an indicator and between the two, the proposed model stood out as the better model. The degree of deviation represented by the RMSE was also the lowest for the proposed model, an indication of a more stable model. Table 4 summarizes the percentage of improvement when comparing the proposed model with each of the above-mentioned models. R-CNN with ML-LSTM was the only model that showed a better MAE metric than the proposed model. However, considering the other metrics, we can assure that the proposed model is better. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MAPE** & \(R^{2}\) \\ \hline DNN & 0.81695 & 0.61333 & 0.68997 & - \\ LSTM & 0.02497 & 0.01656 & 0.01957 & 0.99904 \\ Proposed model: DNN & & & & \\ multilayered LSTM & 0.02410 & 0.01565 & 0.01826 & 0.99911 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model comparison of the proposed model, DNN model, and LSTM model.