## 6 Forecasting Results and Discussion
### Individual Customer Forecasting with Outliers Cancellation  
In this section, the proposed model CNN-BLSTM is compared among every individual customer in the dataset for examining the preprocessing techniques that are mentioned in Section 5.2. In this experiment, we compared the forecasting results from the proposed model using original data with (w) outliers and data without (w/o) outliers. Indeed, we examined the effects of outliers in the data and after cleaning them and identified the percentage of improvement in individual customers, as shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Improvement** & \multicolumn{4}{c}{**Residential and Commercial Building ***} \\ \hline \multirow{2}{*}{**Percentage**} & **RMSE** & **NRMSE** & **MAE** & **MAPE** \\  & **(kW)** & **(\%)** & **(kW)** & **(\%)** \\ \hline \multirow{2}{*}{(\(<\)0)\%} & 18 & 26 & 16 & 24 \\  & (0–25)\% & 35 & 25 & 29 & 35 \\  & (25–50)\% & 24 & 25 & 26 & 16 \\  & (50–75)\% & 6 & 7 & 12 & 12 \\  & (75–100)\% & 5 & 5 & 5 & 1 \\ \hline \hline \end{tabular}  
* Total available customer number for improvement comparison is 88 due to some issues mentioned in Section 5.2  
\end{table}
Table 6: Improvement (%) distribution of the testing results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Public** & **Dataset** & **Forecasting** & **Testing** & **MAPE** \\  & **Dataset Name** & **Size * & **Method** & **Interval** & **(\%)** \\ \hline [PERSON] et al. [1] & Smart Grid & 69 & LSTM & 23-Aug-2013 to & 44.060 \\  & Smart City & & & 31-Aug-2013 & \\ [PERSON] et al. [41] & Smart Grid & 69 & CNN-LSTM & 23-Aug-2013 to & 40.380 \\  & Smart City & & 31-Aug-2013 & & 31.481 \\ Proposed & Smart Grid & 69 & CNN-BLSTM & 23-Aug-2013 to & 31.481 \\  & Smart City & & & 31-Aug-2013 & \\ \hline \hline \end{tabular}  
* The dataset size is determined by considering the subsets selected for forecasting experiments (number of customers).  
\end{table}
Table 4: Benchmarking of different studies using mean absolute percentage error (MAPE) criterion.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Building** & **RMSE** & **NRMSE** & **MAE** & **MAPE** \\
**Type** & **(kW)** & **(\%)** & **(kW)** & **(\%)** \\ \hline Residential & 0.154 & 71.728 & 0.089 & 33.736 \\ Commercial & 0.135 & 26.672 & 0.084 & 27.374 \\ \hline \hline \end{tabular}  
* From these error rates, we can conclude that aggregated forecasting for all commercial buildings obtains the smallest error rates in comparison to residential buildings. This is because most of the individual commercial buildings have relatively consistent behaviors and load profiles during this clean data period, while most of the individual residential buildings have the opposite, where their load profiles seem nonuniform and nonstationary.  
\end{table}
Table 5: Performance of aggregated forecasting for different buildings types in the period April to August 2018.  
To know the impact of time series preprocessing on forecasting performance, we used the proposed model CNN-BLSTM for every individual customer in the dataset. We notice that the forecasting performance has been improved for almost every customer. In this experiment, we investigated the improvement percentage versus the number of customers that have been improved in each evaluation metrics. We conclude that the highest number of improved customers in evaluation metrics due to preprocessing techniques is in RMSE and MAPE in the improvement percentage range of 0-25%. The least number of improved customers is in the improvement percentage range of 75-100%. From these results, we can conclude that the preprocessing methods such as BDE-DBSCAN [22] have already improved the forecasting performance by using the proposed model by at least 25%.