## 3 Multi-Task Framework Integrated into the Knowledge Discovery Module
However, there are cases when the outliers identified for certain technical parameters can be associated with the different operating regimes compared to most regimes but which do not lead to the violation of the allowable limits. In these cases, an attention message will alert the DEMA, who will verify only those regimes. For all other cases, when the outliers are identified, the least squares method is applied to estimate the \"true\" value. The approach is based on a regression model determined for that parameter. A comparison between more years can be performed by choosing the analysis period. **Task 2--Determining the operating regimes. **  
This task is based on the clustering-based data mining process. The input data are associated with a matrix structure built with the values of technical parameters selected by the DEMA. The regimes are identified through the typical operating profiles divided into the intervals associated with the generating units (GU1,..., GUn) or hourly loading patterns, including the hourly average power of each generation unit. The DEMA can choose any algorithm from the hierarchical clustering category (complete-linkage clustering, single-linkage clustering, average linkage clustering, centroid linkage clustering, median linkage clustering, or Ward linkage clustering) or K-means clustering algorithm. In hierarchical clustering, a similarity measure between the sets of observations is necessary to determine which patterns should be grouped or separated. Usually, in hierarchical clustering, the set's similarity is determined by using a distance between the observations. It is carried out through a linkage criterion that specifies the set's similarity. When the DEMA requests characterization of the operating regimes through the typical operating profiles, then the matrix structure of the input data corresponding to the clustering process is identified with the fields of the active power produced by each generation unit in a certain time horizon (usually a year to cover all operating regimes). The matrix size is \(\text{H}_{\text{year}}\times(\text{N}_{\text{HPP}}\text{GU}\times 24+2)\), where \(\text{H}_{\text{year}}\) represents the number of hours when at least one generation unit worked and \(\text{N}_{\text{HPP}}\text{GU}\) represents the number of the generation units from the hydropower plant. The additional columns correspond to the water levels of the reservoir--upstream and downstream. Each obtained pattern is associated with a typical operating profile that characterizes the operating regime of the hydropower plant in certain periods (days inside a year). The DEMA can identify the operating regimes of the hydropower plant through an evolution along the time axis of the degree of hourly loading of all generating units and the water volume used in each operating regime. The DEMA can establish an operating strategy for the plant for the next day depending on the forecasting of the requested powers by the system. The input data matrix is different when the DEMA's requests are as the operating regimes to be characterized through hourly loading patterns. The structure is identified with the fields of the active power produced by each generation unit in a certain time horizon (usually a year to cover all operating regimes). The size of the matrix is \(\mathrm{H_{year}}\times\left(\mathrm{N_{HPP}}^{\mathrm{GU}}+2\right)\), where \(\mathrm{H_{year}}\) and \(\mathrm{N_{HPP}}^{\mathrm{GU}}\) have the same signification as above. The last two columns correspond to the water levels of the reservoir. **Task 3--Developing the strategies to load the generation units. **  
The task is associated with an expert system that uses the operating regimes to be characterized by hourly loading patterns determined above and the number of hours of operation. This last information is recorded in a database and considered in the decision-making process to avoid overloading the generation units over a long period, which leads to minimizing the number of maintenance operations.