# Determining Signatures for Energy Mix Produced by Photovoltaic Systems and Wind Turbines
## 4 Discussion  
For the purpose of designing an effective photovoltaic-wind energy mix, a separate analysis of the power over time for these two RES is not sufficient. A method is needed that finds similarities between these two variables as a function of time. For the two measured powers generated by the photovoltaic system and the wind turbine, clustering without  
Figure 10: Power generated by the photovoltaic system of 3 MWp and the wind turbine of 3.45 MW in May. Figure 9: Histogram of the power generated by the turbine with a maximum capacity of 3.45 MW in the month of May. labeling into 10 classes was performed. The authors previously performed clustering into a smaller number of classes (Figure 11), but it turned out to be insufficient for a useful description of the relations between variables and their use for managing the energy produced (business intelligence). KNIME uses a k-means node that outputs cluster centers for a predefined number of clusters (no dynamic number of clusters). K-means performs sharp clustering, which assigns a data vector to exactly one cluster. The algorithm terminates when the cluster assignments no longer change. The clustering algorithm uses the Euclidean distance in the selected attributes. In the KNIME platform, scientists can find several alternative clustering algorithms to k-means. Here are some of them:  
* Node: Hierarchical Clustering (DistMatrix) creates a hierarchical cluster structure and allows cutting the dendrogram tree at different levels. * Node: DBSCAN. A density-based clustering algorithm that copes well with irregular shapes and noise. * Node: HDBSCAN (Hierarchical Density-Based Clustering). An improved version of DBSCAN automatically selects the optimal number of clusters. * Node: AP (Affinity Propagation) Clustering. The algorithm does not require the number of clusters to be specified; instead, it uses \"examples\" to find centers. * Node: SOM (Self-Organizing Map). Neural networks for clustering and visualizing data in two-dimensional space. * Node: Fuzzy c-Means. Unlike k-means, each point can belong to more than one cluster with different degrees of membership. * Node: Gaussian Mixture Model Learner. A probabilistic approach to clustering, assuming that the data comes from a mixture of normal distributions. Each of these algorithms has its advantages depending on the type of data. The authors chose the k-means algorithm for several reasons:  
* K-means is a fast and scalable algorithm, which is important when analyzing large data sets generated by sensors in RES systems. * Compared to hierarchical or density-based algorithms (DBSCAN), it copes better with a large number of samples. * K-means assigns each observation to a specific cluster, which allows for easy visualization of work patterns and anomalies. Figure 11: Clustering results on 4 clusters (**a**) and 7 clusters (**b**). * You can analyze the average values for each cluster and detect, for example, inefficient work periods. * If the data from the RES system differs significantly from standard clusters, this can be used to detect faults or drops in performance. * For example, a cluster with unusually low energy production on a sunny day may suggest dirty panels or a failure. * K-means can be easily integrated into IoT and KNIME platforms, which allows for ongoing analysis of data streams. K-means algorithm settings:
* Centroid initialization--random. * Max. number of iterations--99. * Number of clusters--4, 7, and 10. The clustering results on the graph of the dependence of the power generated by the turbine on the power generated by the photovoltaic system on 10 clusters are presented in Figure 12.  
The clustering process was started by dividing it into four classes, and its results are shown in Figure 11a. It was considered that this is definitely too few classes (clusters) to effectively describe the course of the power generation process by the photovoltaic system and wind turbine. The next division was made into seven classes, and its results are shown in Figure 11b.