## 2 Knowledge Discovery and Data Mining in Smart SCADA
### Data Mining Techniques  
The Data Mining techniques are divided into two main categories: descriptive and predictive, see Figure 2[25, 26, 27]. Cross-tabulation, correlation, and frequency are some of the characteristics used in the production of descriptive data mining. This process is utilized to identify the similarities between the data and the existing patterns. Another characteristic used in this type of analysis is associated with developing captivating subgroups. This is performed by analyzing the data and transforming it into meaningful information. Descriptive data mining involves techniques, such as clustering, association rule mining, and sequence discovery analysis. Clusteringis commonly used in data mining to organize information by grouping related data points. It helps the DEMA identify patterns and similarities between different sets of information. It can be used to classify and extract patterns in the data, identify anomalies, or analyze spatial data. Figure 1: The steps of a KD process. Association Rules.The goal is to find the correlations in the data sets from the database. Even if the data sets come from different sources, these correlations can identify patterns that can help reveal process trends or explain the operation characteristics of the equipment/installations. Sequence discovery analysis.The goal of sequence discovery analysis is to find interesting data sets that contain sequential patterns. This process usually involves identifying frequent patterns about a certain frequency support measure. The second category aims to predict the future results of a given variable with a high degree of accuracy. This data mining is carried out using supervised learning techniques. There are three categories of methods that are used in this type of mining: regression, classification, and time-series analysis. The latter two are utilized in predictive analysis to model the data. Regression.It is similar to the clustering technique in that it focuses on the relationship between a target and an independent variable. The target variables can be influenced by different predictors or independent factors, which is why a regression analysis is utilized. It predicts the outcomes based on the input fields relevant to the target. Classification.It is carried out by identifying the various features of the data. This process helps to identify patterns and extract meaningful insights. It also helps in improving the quality of the data. The appropriate features are selected to classify it after the data has been collected and analyzed. A suitable algorithm is then chosen to implement. * Support Vector Machine.This algorithm creates a boundary between the different classes/patterns. It identifies the features that are most important to the classification process. * Decision Tree.The classification process is carried out using a tree-based structure. This algorithm uses a set of conditions to categorize the data. The root nodes of the structure are set for the test conditions, while the leaf nodes are for the outcome. * Neural Network.A neural network model is a computational resource that can recognize the relationships between various data sets. These units, which act like neurons, are formed by connecting the inputs and outputs. The model considers the connection strength and outputs the information in a hidden layer. The neural network is similar to the human brain in that it requires training to be effective. Although it can be hard to interpret, the models are reliable and can even classify past training procedures. The Smart SCADA can improve the efficiency of the hydropower plant operation by combining Knowledge Discovery and statistical analysis. Figure 3 displays the interdependencies between the Knowledge Discovery and Smart SCADA (adapted after [28]). Figure 2: Data Mining techniques. Data are everything in the operation of a hydropower plant. The collection, analysis, and control actions make up the difference between classical and smart SCADA systems [29].