*2.3. PV System Design*

This study aimed to find the hourly electricity consumption values by using the monthly respective data set of the KAU hospital. Additionally, the data obtained from Harran University hospital (HUH) in Sanliurfa, Turkey, were employed for model building because the energy consumption of HUH is met by the SPP in accordance with the selfconsumption model. In SPP of HUH, the electrical energy generation and consumption values of the hospital, and the local meteorological data are measured and recorded every 5 min. In this study, the electricity consumption (electricity load) profiles of both hospitals were considered equal by benchmarking the parameters. The PV system has been designed to perform the following steps:

2.3.1. Determining the Hourly Distribution of the Energy Consumption of the Harran University Hospital

Using electrical energy consumption of HUH between 1 January 2019 and 31 December 2019, hourly energy consumption was determined for an average day of the month. In Figure 5, according to the monthly and annual data of HUH, the distribution of electricity consumption for an average day is given. As can be seen from Figure 5.

**Figure 5.** Monthly and yearly average distribution of the electricity consumption of Harran University hospital.


2.3.2. Determining the Load Profile of the Energy Consumption of the Harran University Hospital

The electrical energy consumption load profile (LR) of HUH was calculated according to the following equation. In this equation, it shows hourly electrical energy consumption value with Qhour and annual/monthly average hourly energy need with Qaverage. According to the 2019 data of HUH, the daily average electrical energy consumption profile for 2019 is presented in Figure 6.

$$\text{LR}\_{\text{họur}} = \text{Q}\_{\text{họur}} / \text{Q}\_{\text{average}} \tag{1}$$

**Figure 6.** Average daily electrical energy consumption profile of Harran university hospital for 2019.

2.3.3. The Hourly Energy Consumption Distribution of the KAU Hospital

The electricity consumption profiles of two hospitals are considered similar. Considering the daily electricity consumption profile of HUH, and the hourly electricity consumption distribution profile, and the monthly total energy consumption values of KAU hospital, the average hourly energy requirement (Qhour) of KAU hospital was calculated according to following equation:

$$\mathbf{Q\_{bour}} = \mathbf{L}\mathbf{R\_{bour}} \cdot \mathbf{Q\_{averzge}} \tag{2}$$

According to the consumption data in 2019, the KAU hospital's (Figure 3) hourly electricity requirement is 21.694 kWh on average. The daily electricity consumption profile of the hospital is presented in Figure 7. This figure shows the maximum electricity requirement of KAU hospital which shows that it is 25.135 kWh at 11:30. If the PV system is designed according to the maximum electricity requirement of the KAU hospital, a PV system with 25 MW of capacity should be sufficient. However, in real operating conditions (local climatic conditions) PV panels perform with lower efficiency than their efficiency stated in the catalogue because of high temperature. PV panels are tested under 1000 W/m2 solar radiation and 25 ◦C outdoor temperature conditions.

**Figure 7.** Daily average electrical energy consumption profile of KAU hospital.

PV system designing in accordance with the self-consumption model (according to the hourly electricity consumption need) aims to meet the electricity needs of KAU hospital. To meet up the electricity needs of KAU hospital, an on-grid PV system of different capacities, ranging from 25 MW to 100 MW, was designed. Thus, performance evaluation of PV systems designed for different capacities was made easy. In all PV systems, crystalline

silicon technology was used. PV panels are mounted and fixed in the open area to face them to the optimum tilt angle of the south direction. The optimum tilt angle for Jeddah is 22 degrees. Total losses of the PV system (inverter, cable, dust, etc.) were considered as 14%. In this study, all PV designs determined for KAU hospital were simulated under the local climate conditions of Jeddah and detailed analysis was carried out. For PV system simulation, the Solar-GIS program [39] was used. The PVGIS program [40] was used to validate the simulation results obtained from the Solar-GIS program which are the online ideal free tools that can be used for estimating electricity generation of the PV system. In Figure 8, the monthly electricity generation values obtained by both PVGIS and Solar-GIS programs of the PV system with 25 MW capacity are compared. The results obtained from both programs are very close to each other.

**Figure 8.** The comparison of monthly electricity generated from both PV-GIS and Solar-GIS programs for a 25 MW PV system.

#### **3. Solar PV System Analysis and Performance Prediction**

#### *3.1. Data Collection and Analysis*

Determining the optimal performance of the solar PV generation plant, precise and truthful parameters were ascertained, and related data were collected. The data employed in this study are for the time duration from January 2019 to December 2019 of radiation (W/m2), module surface temperature (◦C), wind speed (m/s), outdoor temperature (◦C), and wind direction which were gathered from Harran University solar power plant located in the university campus. Wind direction measurement is expressed with an angle showing 0◦ of the north, 90◦ of the east, 180◦ of the south and 270◦ of the west. Historical data for the (37.158/39.007) [Lat/Lon] of variabilities of solar resources were obtained from monitoring stations located in Sanliurfa, Turkey. A comprehensive statistical analysis was conducted to determine the multicollinearity to show the intercorrelation between the independent factors. The findings showed that the module surface temperature and outdoor temperature are highly related to the remaining independent variables. The 'P, F, t and VIF' tests indicated the availability of redundant information among the independent variables, and weak linear relations, the interactions of predictors may be nonlinear, and the nonlinear relations can be dealt with RSM, ANFIS and simulation approaches.

Truly, there is often no unique 'best' set of independent variables that can be said to yield the most excellent outcomes. Different techniques do not all automatically lead to the same final prediction of related variables. As a result of the fact that the variable selection process is sometimes subjective, analysts may therefore need to emphasise their judgments on the pivotal areas of the problem. In this study, the highest coefficient of determination (R2) was found 0.946 for several combinations of sets of independent (input) variables. One interesting combination of the input variables was the radiation, module surface temperature and outdoor temperature. The other combination was the addition of all parameters for model development, both giving 0.946 coefficient of determination ratio. Therefore, we used all five parameters for ANFIS model development.

#### *3.2. RSM for Optimization of Solar PV System*

RSM is an optimization method used to determine the operating conditions of a process leading to achieving the best process performance [41]. RSM has extensive applications in semiconductors, electronics manufacturing, and machining. In most RSM problems, the form of relationships between independent factors and response is assumed unknown. When there are curvature relations between the factors in a system, a higher degree polynomial of process optimization approach can be employed, such as a second order model or above. Obviously, a polynomial model is unlikely to be a reasonable estimate of the true functional relationship over the entire domain of independent parameters, but for a relatively small region, the method works quite well. Figure 9 shows that there is no serious indication of the abnormality or excessive evidence of possible outliers. This plot also reveals nothing of unusual interest among the residuals, and the residual scatter does not appear more for the outcomes that show nonhomogeneous conditions. Therefore, the model is assumed to be adequate, the investigation of the normality assumption also approves the adequacy.

**Figure 9.** The probability plot of solar PV generation response, and the dispersion of residual.

The regression equation of solar PV generation where the factors are radiation (*x*1), module surface temperature (*x*2), outdoor temperature (*x*3), wind direction (*x*4) and wind speed (*x*5) were established according to the following equation.

$$\log y\_{ijk} = \beta \mathbf{0} + \sum\_{j=1}^{k} \beta\_{j} \mathbf{x}\_{j} + \sum\_{j=1}^{k} \beta\_{j\bar{j}} \mathbf{x}\_{j}^{2} + \sum \sum\_{i$$

where *β*0, *βj*, *βjj*, and *βij* represent the overall mean effect, the effect of the *j*-th level of the row factor, the effect of the *j*-th level of column factor, and the effect of the interaction effect in the quadratic model, respectively. ∈*ijk* is a random error component of a second order RSM, where *yijk* is the response and refers to the solar PV generation level in this study. *xi* and *xj* present the variables that are called factors. Dirnberger and Kraling (2013) [42] described the measurement procedure and uncertainty analysis which covers the complete daily calibration process of measurement devices in detail, the correction to standard testing conditions, and determination of electrical module parameters. They presented recent progress in reducing the measurement uncertainty for crystalline silicon and thin-film PV modules.

#### Solar PV generation (*yk*)(kWh) = −13499 + 20.8*x*1+752*x*2+349*x*3−8.1*x*4+107*x*5+0.52*x*1*x*2+0.12*x*1*x*<sup>3</sup> + 0.1277*x*1*x*4+1.16*x*1*x*5+242*x*2*x*3+0.74*x*2*x*4−235*x*2*x*5−2.34*x*3*x*4+329*x*3*x*5+1.05*x*4*x*5−0.0389*x*<sup>2</sup> 1−103*x*<sup>2</sup> 2 −165.6*x*<sup>2</sup> 3−0.0275*x*<sup>2</sup> 4−198*x*<sup>2</sup> 5

The essential effects that arise from this analysis are the key impacts of *x*1, *x*2, *x*3, *x*<sup>4</sup> and *x*5. The interactions between the parameters *x*<sup>1</sup> *x*2, *x*<sup>1</sup> *x*3, ... are presented in the regression model with the coefficients presented above in the model.
