## 7 Conclusions  
Recently, short-term load forecasting of customer profiles has become a critical technique to build a robust and reliable system in the smart grid. This paper proposes advanced preprocessing tools and hybrid deep learning models that applied to one-hundred electricity consumers in 2018. The real data set contains the consumption information obtained with different time periods of 56 residential and 44 commercial buildings. The purpose of the use of preprocessing techniques is to enhance the prediction accuracy by cleaning missing and corrupted data while collecting the dataset, which happens normally in most of data acquisition processes. Data aggregation, data cleansing, density-based clustering application for outlier analysis, and normalization were implemented at the preprocessing phase. The effectiveness of the strategy proposed in this paper was confirmed by combining the preprocessing techniques with hybrid deep learning, which can ultimately build a robust and reliable prediction load forecasting. The proposed forecasting method is based on the hybrid version of CNN-BLSTM integrated with a density-based outlier analysis. The proposed model has a flexible structure that can generate feasible solutions on real data sets and can be rearranged depending on the application. Moreover, it is a reasonable option for the situations where it is difficult to obtain complete data acquisitions or that necessities using less features due to data privacy. The forecasting model was evaluated according to different criteria. We conclude that the prediction performances of the proposed outperformed conventional prediction model. Considering the results obtained, the proposed method reduced MAPE values at different ranges for 72% of the dataset. Regarding the time complexity of training the proposed forecasting model, we found that it is inconsistent due to various customer load \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Time** & **RMSE** & **NRMSE** & **MAE** & **MAPE** \\  & **Steps** & **(kW)** & **(\%)** & **(kW)** & **(\%)** \\ \hline CNN-BLSTM & 1 h & 0.108 & 55.432 & 0.058 & 36.748 \\ -without(w/o) & 24 h & 0.108 & 56.478 & 0.059 & 36.911 \\ outliers & 48 h & 0.108 & 56.144 & 0.059 & 38.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Forecasting results of proposed method with different time steps. profiles and different factors such as dataset size, number of features, prediction model parameters, etc. The analysis performed in the paper showed that the preprocessing tools coupled with the hybrid deep learning model CNN-BLSTM are powerful artificial intelligence tools for modeling robust load forecasting model and have the potential to be applied in different areas, such as smart grids, microgrids, and smart city, due to its outperformance, as illustrated. Conceptualization, F.U. and A.A.; methodology, F.U. and A.A.; software, F.U. and A.A.; validation, F.U. and A.A. and S.E. ; formal analysis, F.U. and A.A. and S.E. ; investigation, F.U. ; resources, F.U. and A.A.; data curation, F.U., A.A. and S.E. ; writing--original draft preparation, F.U., A.A.; writing--review and editing, F.U., A.A. and S.E. ; visualization, F.U., A.A.; supervision, S.E. ; project administration, S.E. ; funding acquisition, F.U. and S.E. All authors have read and agreed to the published version of the manuscript. This research was funded by the Firat University Scientific Research Projects Unit (FUBAP) grant number TEKF20.25. This paper is also a part of the PhD. thesis of candidate [PERSON] in Firat University, Department of Energy Systems Engineering, Elazig, Turkey. The authors would like to thank [PERSON] and [PERSON] (Department of Automatic Meter Reading (AMR) System, Dicle Electricity Distribution Company) for providing helpful insights and supports for this project. The authors