# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 6 Conclusions  
The DNN multilayered LSTM model was proposed in this study to predict energy consumption in households. The IHEPC dataset was used, and the proposed model was discussed. Preprocessing methods were applied to clean, transform, and conduct feature selection for effective training. The proposed model was then trained. The DNN of the proposed model was applied to learn the patterns in the energy consumption data, and the outputs were used as inputs in the multilayered LSTM model. The DNN and LSTM models were also trained on the same dataset.  
The three models were compared, and the results showed that the proposed model performed better than the DNN and LSTM models. Moreover, the results of the proposed model were compared with those of various models from related studies. The proposed model also had a better performance than these models.  
As the proposed model produced satisfactory results, it can be tested over real-time energy forecasting and integrated into sensor data, which can be used to make energy consumption predictions. In addition, it can be tested in different domains, such as  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **\% of RMSE** & **\% of MAE** & **\% of MAPE** & **\% of R\({}^{2}\)** \\ \hline CNN multilayered bidirectional & 95.73\% & 95.48\% & 93.73\% & \\ LSTM [29] & 96.30\% & 96.88\% & 99.98\% & \\ CNN-BiGRU [32] & 94.26\% & 94.60\% & & \\ GRU [28] & 89.05\% & 91.76\% & 99.97\% & \\ CNN-ESN [33] & 48.94\% & 41.17\% & & \\ R-CNN with ML-LSTM [34] & 25.85\% & \(-\)8.68\% & 98.22\% & 1.53\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The proposed model’s percentage of improvement compared with various models.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **RMSE** & **MAPE** & \(R^{2}\) \\ \hline LSTM [31] & 0.041 & 0.00946 & 0.988 \\ Bidirectional LSTM [31] & 0.053 & 0.01193 & 0.98 \\ Multiple LSTM [31] & 0.023 & 0.0052 & 0.996 \\ WT multiple LSTM [31] & 0.019 & 0.0404 & 0.997 \\ Proposed model: DNN multilayered LSTM & 0.02410 & 0.01826 & 0.99911 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model comparison with LSTM models.  
renewable energy consumption, production prediction, and fault detection prediction in industries. In the future, the focus will be on developing a hybrid DNN with other LSTM networks, such as bidirectional LSTM.  
Conceptualization, M.A.-G. and A.A.-M.A.-G.; methodology, M.A.-G. and M.R.; software, M.A.-G.; validation, M.A.-G. and A.A.-M.A.-G.; formal analysis, M.A.-G.; investigation, A.A.-M.A.-G., M.A.-G. and M.R.; resources, M.A.-G.; data curation, M.A.-G., A.A.-M.A.-G. and M.R.; writing--original draft preparation, M.A.-G.; writing--review and editing, M.A.-G.; visualization; M.A.-G.; supervision, A.A.-M.A.-G. and M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset is available upon request.  
The authors gratefully acknowledge the support provided by the Faculty of Computing and Information Technology (FCIT), King Abdulaziz University (KAU), Jeddah, Saudi Arabia.  
The authors declare that they have no conflicts of interest to report regarding the present study.