## 4 Case Study
Appendix A includes in Tables A2-A5 the hourly values of each operating regime (identified through the Patterns P1-P4) containing the typical operating profiles divided into the intervals associated with the generating units (GU1,..., GU6). The input data used to obtain hourly loading patterns contain the following fields: water flows on the two pipes, the loading of each generator unit, and the upstream and downstream water level of the reservoir. The K-means clustering algorithm has been used to extract the patterns, and the optimal number has been 25. Figures 17-19 show how the hydropower plant has been operated based on the obtained patterns of hourly loading of the generation units in three consecutive years, 2017-2019. It can be observed that the loading of the six GUs is different in the developed strategy compared with the experience-based strategy adopted by DEMA. Table 2 presents the differences between the experience-based strategy and the expert system-based developed strategy. The signification of the colors is the following:  
* Red color is associated with the generation units that have been loaded in the experience-based strategy but not considered in the case of the expert system-based strategy (the sign is \"-\"). * Blue color is associated with the generation units that have been loaded in the expert-based strategy but not considered in the case of the experience-based strategy (only with the sign \"+\"). * Green color is associated with the generation units loaded in the expert-based strategy, having the same (value \"0\") or having another loading in the expert-based strategy (with signs \"+\" or \"-\"). * Yellow color is associated with the generation units that have not been loaded in either strategy. \begin{table}
\begin{tabular}{c|c|c|c|c|c|c} \hline \hline \multirow{2}{*}{**Hour**} & \multicolumn{2}{c|}{**P\_GU1**} & \multicolumn{2}{c|}{**P\_GU2**} & \multicolumn{2}{c|}{**P\_GU3**} & \multicolumn{2}{c|}{**P\_GU4**} & \multicolumn{2}{c|}{**P\_GU5**} & \multicolumn{1}{c}{**P\_GU6**} \\  & [MW] & [MW] & [MW] & [MW] & [MW] & [MW] \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 2: The differences between the experience-based strategy and expert system-based strategy. Figure 22: The active power distributed among the six GUs—the strategy adopted by the DM based on the Knowledge Discovery module. A comparison between results obtained with the adopted expert system-based strategy in 2020 and those obtained with the experience-based strategy in 2017 revealed an increase in the average loading for all six GUs with 21.5% (GU1), 18.9% (GU2), 16.9% (GU3), 14.8% (GU4), 6.3% (GU5), and 57.7% (GU6). The smallest increase has been observed in the case of GU5, the most elevated has been obtained for GU6, and between 14.8% and 21.5% for the first four GUs. However, the higher loading of UG6 was because it was in maintenance operation for two years and worked for very few hours during that time. The total energy production has increased by 20.8% from 313.6 TWh to 378.7 TWh, although the total operating time of all GUs decreased by 5.1% from 13,653 h to 12,960 h. The years 2018 and 2019 are not considered in our analysis because GU6 stopped in 2018 and had a few hours of operation in 2019.