## 4 Case Study
### The Profiling Process  
The profiling process used the daily energy consumption profiles, recorded with the help of smart meter during the year 2019. The sampling step has been set to a quarter-hour due to the dynamic of activities associated with the technological flow. The database has records for 250 days without the holidays and weekend days. These were normalized using daily energy consumption. To identify what is the best method to be used in the clustering process, the module has used the testing function based on the cophenetic correlation coefficient (CC). The Ward method, highlighted in red, led to the best performances in the case of this database, for which CC is equal with 0.840, see Figure 11. The results can be displayed if the Decision-Maker requests this information. The characteristics represented \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Mean**} & \multicolumn{3}{c}{**Standard**} & \multicolumn{3}{c}{**Confidence**} & \multicolumn{3}{c}{**Quantile**} & \multicolumn{3}{c}{**Quantile**} & \multicolumn{3}{c}{**Quantile**} & \multicolumn{3}{c}{**Quantile**} \\  & **(kWh)** & **Deviation** & **(kWh)** & **Degree (\%)** & **Q1** & **(kWh)** & **Q2** & **(kWh)** & **Q3** & **(kWh)** & **Q4** & **(kWh)** \\ \cline{2-13}  & **2018** & **2019** & **2018** & **2019** & **2018** & **2019** & **2018** & **2019** & **2018** & **2019** & **2018** & **2019** & **2018** & **2019** \\ \hline Consumption & 2274 & 1444 & 1142 & 493 & 50.2 & 34.1 & 1488 & 1112 & 1744 & 1252 & 2704 & 1632 & 5124 & 2496 \\ Cost & 2047 & 1300 & 1028 & 444 & 50.2 & 34.1 & 1303 & 1001 & 1569 & 1127 & 2433 & 1469 & 4612 & 2246 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison on the statistical results obtained from 2018 to 2019. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **(kWh)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Standard** \\ **Deviation** \\ **(kWh)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Confidence** \\ **Degree (\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Quantile** \\ **Q1** **(kWh)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Quantile** \\ **Q2** **(kWh)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Quantile** \\ **Q3** ** **(kWh)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Quantile** \\ **Q4** **(kWh)** \\ \end{tabular} } \\ \hline January & 77.7 & 29.5 & 38.0 & 56.8 & 77.0 & 95.8 & 145.0 \\ February & 124.8 & 22.4 & 17.9 & 109.0 & 127.5 & 145.0 & 157.0 \\ March & 107.7 & 19.6 & 18.2 & 96.0 & 110.0 & 122.0 & 141.0 \\ April & 58.6 & 18.6 & 31.7 & 49.0 & 51.0 & 70.0 & 97.0 \\ May & 50.9 & 8.4 & 16.5 & 46.0 & 50.0 & 57.0 & 68.0 \\ June & 49.1 & 13.2 & 26.9 & 40.5 & 47.5 & 60.5 & 72.0 \\ July & 57.0 & 13.3 & 23.3 & 45.0 & 56.0 & 63.0 & 83.0 \\ August & 50.1 & 7.1 & 14.3 & 44.0 & 49.5 & 56.0 & 63.0 \\ September & 58.5 & 10.7 & 18.3 & 50.5 & 56.0 & 67.5 & 78.0 \\ October & 54.7 & 13.2 & 24.2 & 47.5 & 57.0 & 63.5 & 76.0 \\ November & 77.7 & 8.1 & 10.4 & 72.0 & 79.0 & 84.0 & 90.0 \\ December & 69.2 & 10.4 & 15.0 & 68.0 & 72.5 & 75.0 & 80.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The statistical results obtained for the daily energy consumptions from each month of the year 2019.  
through the statistical variables (mean, standard deviation, and confidence degree in mean) of the daily energy consumption for the clusters obtained with the Ward method are presented in Table 6.  
The profiles have been obtained based on the averaging process of the profiles assigned with each cluster. Figure 12 presents the TECPs, and the hourly values are indicated in Table 13 from Appendix A. Because the working schedule is 9 h (between 8:00 a.m. and 5:00 p.m.), the other values from TECPs are 0. The dyeing installation cannot continuously operate during the 9 h of the working schedule.