# Determining Signatures for Energy Mix Produced by Photovoltaic Systems and Wind Turbines
## 2 Materials and Methods  
In this article, the authors will present the practical application of clustering methods to describe the production of electricity by photovoltaic systems and wind turbines [69]. The presented unsupervised clustering method is very universal and can be applied to both low-power systems (several kW) and large-power systems with capacities greater than 1 MW. In the article, the authors will characterize the power produced by photovoltaic systems and wind turbines separately using traditional research methods as well as unsupervised clustering divided into different numbers of classes [70,71]. The conclusions from the analysis can have great practical application in order to determine the ratio of the power of individual sources in the mix for the purposes of effective energy balancing [72,73]. In Section 3, the authors will characterize the generation of power and energy production from photovoltaic systems and wind turbines. Traditional statistical tools will be used for this purpose. Then, in Section 4, the unsupervised clustering method will be applied. This method, known for over 20 years, allows for grouping variables due to similarity in multidimensional space [74,75]. Both the analysis using traditional methods and clustering were performed in the KNIME environment [76]. It allows reading, visualization, and processing of time series obtained from photovoltaic systems and wind turbines. The data flow diagram is shown in Figure 2.  
The appearance of the calculation algorithm in the KNIME software is presented in Figure 3. The KNIME analytical platform version 5.3.2 was used in the research. Figure 3 also shows the entire process of acquiring and processing measurement data in the form of a graphical programming language. Each user can easily reproduce the analyses, calculations, and visualizations performed using measurement data. They will be presented later in the article. Clustering is a technique known for over 20 years, which consists of grouping objects in a multidimensional space due to their similarity. Clustering is basically divided into two groups: clustering with labeling (called supervised clustering) and clustering without labeling (called unsupervised clustering). It is also possible to use clustering combining both clustering groups, and then it is called hybrid clustering. Supervised clustering consists of assigning all measurement points to known labels. This means that the labels must be initially known and defined. In this method, there is also no optimization of classes (labels),  
Figure 3: The appearance of the calculation algorithm in the KNIME software. Figure 2: Data flow diagram. because their number is known. The clustering without labeling used by the authors in the article consists of processing only raw measurement data without pre-imposing labels. In the beginning, the authors assume a simple energy mix consisting of a photovoltaic system and a wind turbine. This approach allows for detecting new labels in the processed data. However, greater expert knowledge is then needed to interpret the obtained results in order to define new labels and give them physical meaning. Unlabeled clustering is also widely used in the decomposition of known labels. Unsupervised clustering is also better for detecting anomalies, because by definition it assumes the existence of classes that require expert validation. The algorithm itself tries to detect new structures and substructures. Sometimes hybrid clustering is also used, combining the features of clustering with labeling and without labeling. Usually, clustering with labeling is used in such cases and, as a supplement, clustering without labeling. The hybrid approach allows for improving the quality of clustering and better use of the processed measurement data. The hybrid approach is also widely used in diagnostics, anomaly detection, and fraud detection. Previously defined \"suspicious labels\" are searched for in raw data from similar processes.