# Determining Signatures for Energy Mix Produced by Photovoltaic Systems and Wind Turbines
## 3 Results
### Characteristics of the Power Generated by the Photovoltaic System  
Figure 4 shows the power generated by a 3 MWp photovoltaic system on individual days in May 2024. The authors used data generated by a 3 kWp photovoltaic system pointed exactly due south with a 40\({}^{\circ}\) tilt angle of monocrystalline photovoltaic panels. These data were multiplied by 1000 to scale to a power level close to that of a wind turbine. The measurement data were obtained from a cloud platform offered by the manufacturer of inverters for photovoltaic systems. This is an Internet of Things device that takes measurements in 15-min periods and sends them via wireless transmission to a cloud server. The author, who is the manager of the photovoltaic system, has free access to obtain measurement data in CSV format. The raw data presented in the graph show that May 2024 in the Lublin region in Poland was characterized by good sunlight and low temperatures, which resulted in high generated power and therefore large amounts of energy produced. It is clearly visible that on most days the course of generated power indicates a sunny and cloudless sky. Only on a few days are significant drops in generated power visible in the afternoon hours due to heavy cloudiness. We can also see that at noon, the photovoltaic system generated power close to or even exceeding the peak power provided by the manufacturer. Such system performance indicates that the Lublin region in Poland has solar conditions suitable for producing electricity from this RES.  
Much more about the characteristics of the power generated by the photovoltaic system can be obtained by plotting a box plot, as shown in Figure 5. The box plot includes analyses performed at 0.5 h intervals. The box plot is particularly useful for comparing several groups of data in one graph. It presents several key pieces of information, such as the median (the line inside the box), quartiles (the first quartile Q1--the lower boundary of the box and the third quartile Q3--the upper boundary of the box), minimums, and maximums (whiskers), and outliers (those lying outside the range defined by the whiskers are marked as small circles on the graph). The shape and symmetry of the box and the length of the whiskers provide insight into the data distribution, e.g., whether it is symmetrical or skewed. The symmetry for smaller generated powers and the skewness of the distribution for larger generated powers are clearly visible. Outliers present in the graph help to identify unusual observations in the data set. These abnormal behaviors include sudden cloud cover that causes sudden and usually short-term drops in generated power.  
Figure 4: Power curves of the power generated by a 3 MWp photovoltaic system on individual days in May 2024.  
Figure 5: Box plot of the power generated by a 3 MWp photovoltaic system at 0.5 h intervals.  
The information obtained from the box plot was supplemented by the information presented in the histogram (see Figure 6). The histogram is very useful when we want to understand the basic distribution of the data, compare different data sets in terms of the distribution (from photovoltaics and wind), as in this case, and identify potential anomalies or outliers. All measurement data were divided into 20 classes. The most numerous were classes with the lowest power (equal to 0 or close to 0). This class collects measurement points corresponding to night, morning, and evening hours. The rest of the distribution has a uniform (flat) shape--all classes have a similar frequency. The observed shape of the data distribution indicates that it is a unimodal distribution skewed to the left.