## 8 Conclusions  
This research focused on designing a solar energy and SPVS at GUTtech in Oman, where data from the Shams Solar Facility were used to model the system's performance. Four mathematical models (Ordinary Least Squares, LASSO, Ridge, and Elastic Net) were applied to the data to assess and compare the modeling results. The study highlighted key factors influencing model accuracy, the importance of selecting appropriate parameters, and the need for flexible modeling in SPVS. To be more precise, a solar energy and SPVS station called Shams Solar Facility was designed at GUTech in Oman, which included 56 modules (panels). The data were collected from several electronic monitors for a period extending from 3 April 2021 at 9:00 a.m. to 12 March 2022 at 5:05:00 p.m. As a result, we have extensive data for six sets, each set containing 36,851 observations and a number of features (factors). In this paper, we collected and studied six different datasets of the SPVS station (DC_Current_String1, DC_Current_String2, DC_Power_String1, and DC_Power_String2) extensively in order to learn their features, shapes, and properties for the purpose of modeling them accurately through a number of well-known mathematical models. Therefore, in this research, we proposed a technique for modeling any data of solar energy and SPVSs through a big application, which involved the six datasets of the Shams Solar Facility, taking into account the data features and characteristics like the autocorrelation between the independent parameters (factors) using four mathematical methods/models (the Ordinary Least Squares Method, LASSO method, Ridge Method and Elastic Net Method). The parameters/coefficients for all the four models were estimated, and the criteria R-squared and RMSE were computed for all the choices of the parameters (factors) in order to compare between the models. In addition, the results of modeling the above datasets using the four models were given, explained, and compared. In addition, the results show that all the performance of the four models of DC power with all choices of parameters (factors) is excellent based on the criteria R-squared and RMSE, except the models with the parameters (factors) X3 and X5, which are very good. At the same time, all the models behave badly for the parameters (factors) X3 and X7. These two parameters (factors) need additional investigation. Thus, this research is conducted on a comprehensive basis to be considered as a live application for modeling data parameters (factors) of the SPVS; it should be followed by researchers who are intent on investigating how to apply mathematical modeling in a solar energy and PV system. This is in order to describe historical data and model the performance/production of clean electrical energy from PV stations under various conditions (which were translated under various parameters or factors in this research); i.e., it is possible to model the performance of PV plants depending on any of the parameters (factors) like DC current, DC power, irradiance, temperature, humidity, and air pressure. We would also like to clarify that the available parameters (factors) affecting the performance of a solar energy and SPVSs should not all be used in any model; this means that the modeling process must be implemented in a flexible manner, and a number of mathematical/statistical characteristics/feature should be taken into account when modeling such data. This may be the result of obtaining different models' components. As a result, the extent of the differences between the different modeling methods will reflect different decomposition forms; we hope that this point will be clear for the researcher. It requires close attention by researchers when conducting the modeling process for such systems.