**1. Introduction**

The energy planning systems have transformed from specific objectives with constraints to more complex approaches due to the insertion of multiple criteria, investors and needs of nations that are usually in conflict. Renewable and non-renewable energy sources are the basis of different energy systems. The world energy need is presently met mainly from fossil fuels (81%), renewable energy (14%), and nuclear sources (5%) [1]. Fossil fuels are disposable, and unsafe for the environment due to their impacts on climate and pollution rising. Similarly, nuclear sources and power reactors are deemed dangerous by some scientists as a result of their high capital costs, the power systems' control, opposing public opinion, nuclear waste management, and economies of the scale envisaged. However, they have many advantages such as lower emissions, higher security of supply and enabling of possible other technologies. The major direction of the world is to develop independent small nuclear units for energy generation that bring greater simplicity of design, short construction times, and reduced siting costs. On the other hand,

**Citation:** Alamoudi, R.; Taylan, O.; Aktacir, M.A.; Herrera-Viedma, E. Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches. *Mathematics* **2021**, *9*, 2929. https:// doi.org/10.3390/math9222929

Academic Editors: Angel A. Juan and Marcin Kami ´nski

Received: 23 October 2021 Accepted: 15 November 2021 Published: 17 November 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

small nuclear units are much more easily manageable investments whose costs often rival the capitalization of large plants. Renewable energy sources alleviate their downsides, and eventually cost less than fossil fuels that own useless production technologies. For instance, due to urgent environmental pollution and climate change issues, using a mix of fossil fuels and renewable energy sources, Italy carried out an 'energy transition' towards a more sustainable energy production and consumption system by adopting nuclear power to reduce the consumption of fossil fuels [2]. Solar photovoltaic (PV) as a favorable renewable energy source can meet the electricity demand of Saudi Arabia providing a 50 GW additional capacity. However, the aggregate global renewable energy capacity has reached 227 GW in recent times [3]. The average sunlight energy falling on Kingdom's land is about 2200 thermal kWh/m2 per year which is acceptably higher when compared to some countries heavily investing in solar energy generation technologies. During the summer seasons, the electricity need reaches its peak load, which is twice higher than in the winter. Therefore, it is worthwhile to generate clean solar PV energy via sunlight [4]. In Saudi Arabia, the electricity consumption is estimated to exceed 40 GW nowadays and reach 120 GW per hour until the year 2028. The electricity consumption of industrial and service sectors is increasing about 6.9% per year mainly due to the investments and capacity expansions. This growth will require more fossil fuel consumption and eventually release a higher amount of CO2 into the atmosphere. Although the Kingdom's annual solar irradiance is about 2000–2450 kWh/m2, the availability of immense empty lands and ideal locations for solar installations and PV generation [4]; the renewable energy share of Kingdom is still less than 0.1%, compared to 14% share of the rest of the world [5]. In this context, Alnaser and Alnaser [6] claimed that only 0.1% of Kingdom's land is sufficient for the solar PV projects to meet the electricity demand estimated for 2050. Many countries are interested in reliable, sustainable, suitable, and diversified energy sources, and technologies due to the pros and cons of non-renewable energy sources and technologies. The challenging problem for a country is the determination of the proper energy sources and technologies for the public and private investments. Although Saudi Arabia has wind, and geothermal resources that can solve all energy demand in the future, the new PV technologies are more productive and can generate more energy efficiently. This study also aims to encourage government bodies and private organizations to invest in solar PV energy generation systems for achieving sustainable energy infrastructure.

A solar photovoltaic (PV) system aims to convert sunlight directly into electricity using PV cells. This system uses solar modules consisting of various solar cells containing semiconductor materials. Yildirim and Aktacir [7] investigated the efficiency of PV cells, the parameters affecting photovoltaic panel performance, and variabilities depending on PV technologies. Martin et al. [8] reported that the efficiency of converting solar energy into electrical energy is 9% using organic modules, which can reach 25% using crystalline modules. Monocrystalline, Multi-crystalline and thin film Silicone are broadly used in those PV technologies available which have the highest market share [7]. The most efficient PV modules are obtained from Monocrystalline technology, even highly more efficient than multi-crystalline technology are considered the leader of PV technologies [9] in industry. On the other hand, although solar radiation has the greatest influence on the power of the PV module [10] obtained, the module surface temperature affected by the wind speed and outdoor temperature are also important parameters. The wind speed and its direction have a cooling effect on the temperature of the PV module surface and significantly increase the electricity generation [11,12]. Kalledis et al. [10] have reported that the PV module surface temperature is reduced with rising wind speed. Although certain values of different parameters are considered as the ideal conditions, the reality is usually different, and the parameters do not represent the optimal field circumstances in which the PV panel operates [13].

Today, many studies in the literature related to renewable energy sources consider them as the alternative to fossil energy sources. Taylan et al. [1] used multi-criteria group decision making approaches for determining the attributes of energy sources, and selected technologies for PV energy generation. Lee [14] investigated the energy systems' essentials for the global economy to produce friendly new technologies for investment. Fan [15] stated that using energy more effectively results in energy efficient systems and reduces direct operating costs and initial investment costs. Tian et al. [16] built an energy evaluation procedure to integrate uncertain factors using stochastic models. Taylan et al. [1] used the experts' opinions and machine learning approaches to find out that solar PV was an attractive energy system for investment in the Kingdom. The regular daylight in the Kingdom is 12 h 8 min and 48 s on average, longer than several countries using solar PV systems extensively for energy generation. Akpolat et al. [17] investigated a PV system installed for a faculty building and found out that an 84.75-kWp grid- system can produce remarkable power and save about 90.298 kWh of energy annually for faculty buildings. Muteri et al. [18] summarized the current literature of life cycle assessment applied to different types of grid-connected PV systems to critically analyze the results related to energy and environmental impacts generated during the life cycle of PV technologies to provide information for future analyses. Yet, PV modules have 0.09 US\$/kWh, however, diesel generators on average have 0.25 US\$/kWh levelized electricity cost. Pradhan et al. [19] carried out a comparative analysis about different possible PV configurations in detail and found that the hybrid solar PV-wind energy system is the most suitable energy generation system. Almarshoud [20] examined the performance of a pilot PV system based on real time solar radiation data in 32 sites. Mittal et al. [21] used artificial neural networks (ANNs) to predict the PV Modules performance. Yahya-Khotbehsara, and Shahhoseini [22] merged the numerical and analytical approaches to determine the PV module parameters of Monocrystalline, Multi-crystalline and thin film technologies. Goverde et al. [23] investigated the PV module surface spatial temperature differences affected by wind. Goossens et al. [24] used wind tunnel experiments to investigate the influence of wind flow, and temperature patterns on the electrical performance of buildings integrated with PV modules. Curto et al. [25] investigated the economic impacts of feasibly generating energy from solar, wind and sea wave plants to achieve specific targets of decarbonization in Lampedusa, a small Italian island where currently the energy is supplied totally by diesel power plants. Awan et al. [26] determined that the northern province, Tabuk, is the most feasible region for a solar PV plant. Rani et al. [27] proposed a fuzzy TOPSIS approach for ranking the status of renewable energy sources. Daus et al. [28] calculated the unit cost of generated electric energy from solar PV for the utility sector, health facilities, housing, industrial enterprises, recreation areas and agricultural industries. Yoomak et al. [29] searched the location problem and its effect on the performance assessment of solar PV systems installed on the rooftop of residences in distinct regions of Thailand. Kassem et al. [30] analyzed the solar radiation of five distinct locations in Northern Cyprus statistically in addition to some meteorological parameters such as relative humidity, air temperature, sunshine, and solar radiation. Ascencio-Vásquez et al. [31] used the performance of PV systems to evaluate the risks occurring due to the diverse climate conditions for standardizing the evaluation criteria in regions. Zell et al. [32] believed that understanding the spatial and temporal variability requires considerably more data to optimize the planning and setting of solar energy power plants. Roy et al. [33] studied the features of perovskite solar cells and found them superior to the existing PV technologies for presenting the efficiency and various architectures used to date. Naderloo (2020) [34] predicted the solar radiation using ANN methods, ANFIS and RSM, carried out the sensitivity analysis, and found out that ANNs and RSM were superior to the ANFIS. Benmouiza and Cheknane (2019) [35] used fuzzy c-means (FCM), subtractive clustering, and grid partitioning algorithms to develop an ANFIS for forecasting solar radiation. The findings depicted that the ANFIS model developed with the FCM clustering algorithm gave the best results considering the RMSE approach of 112 W/m2. Mohammadi et al. (2016) [36] developed and employed an ANFIS model to identify the solar radiation relevant parameters and predict the daily level of solar radiation. The results revealed that the climate conditions influence the solar radiation characteristic which is not identical for all locations. Aldair et al. (2018) [37] validated the

effectiveness of ANFIS for tracking the maximum power point tracking (MPPT) approach in a stand-alone PV system. The results indicated that the ANFIS model controllers are more efficient and give better dynamic responses than the incremental conductance method and constant voltage method. Khosravi et al. (2020) [38] investigated the ANFIS and genetic algorithm combination and based on teaching-learning optimization algorithms and determined the optimum design parameters of different 100 MW solar power stations with a molten salt storage system.

PV systems generate cheaper and cleaner energy during the daytime and stop generating after the sun sets. So, these systems must be connected to the local electricity grid for transferring the excessively generated power to the grid and taking it from the grid back during the night. The disadvantage of these systems is that a self-balanced energy is needed for instantaneous energy consumption. The excessive power must be well-managed to avoid the problems. Hence, a well-established control system and restrictions of the energy generation for balancing the supply and demand level is required. This approach is called offsetting energy generation and consumption. This system is called an on grid photovoltaic system.

The design of grid photovoltaic systems requires detailed analysis by considering local parameters. Knowing the performance of PV panels under real operating conditions is extremely important. Solar panel manufacturers only give PV panel performance under standard test conditions (STC). Although STC defines solar radiation at 1000 W/m2, surface temperature 25 ◦C and air mass (A.M.) 1.5 as ideal conditions, the reality is different; these parameters do not always represent the optimal field circumstances in which the PV panel operates [13]. In this study, to determine the optimal solar PV energy generating conditions and the panel performance, as a statistical and mathematical approach RSM was employed for modeling and analysis of this complex problem. As it was clearly stated, the response (the amount of solar PV energy generated) is affected by several factors. However, the response (PV generated) and the independent parameters' relations are not usually clearly known. On the other hand, the response cannot be formed well by linear approximations due to the complexity of problems, therefore higher degree polynomials might be employed.

This study aims to design a solar PV system for generating the electricity need of King Abdulaziz University (KAU) Hospital in Jeddah city. The hospital's energy demand is very high, and the energy consumption bill is around \$1.5 million per month. Initially a detailed field work was conducted to determine the PV system performance for self-consumption and self-sufficiency models under real operating conditions. A two step work was carried out: in the first step, a 40 MW PV system was constructed to generate the electricity need of the KAU hospital. The second step includes determining the optimal operating conditions by RSM and ANFIS approaches. Both approaches were employed using the following parameters: surface temperature (◦C) of modules, wind speed (m/s), radiation (W/m2), outdoor temperature (◦C), and wind direction. The RSM aimed to find out the optimal operating conditions of the solar PV panels and the factor space operating intervals required for the PV panel system. Our investigations depicted that generating maximum solar PV of 42.27 MW is possible for the KAU hospital, if the radiation level is about 896.3 W/m2, the module surface temperature is 50.0 ◦C, the outdoor temperature is 40.3 ◦C, the wind direction is 305.6 and the wind speed is 6.7 m/s. On the other hand, the operation conditions of solar PV panels were simulated under different conditions, for instance, it was determined that obtaining a 33.96 MW solar PV system, the radiation should be 896.3, the module surface temperature should be 43.4 ◦C, the outdoor temperature should be 40.3 ◦C, the wind direction should be 305.9 and the wind speed should be 6.7 m/s.

The ANFIS intended to develop and analyze the solar PV modules by estimating the performance of them. The ANFIS models developed for the PV generation system can predict the performance of modules containing five, nine and eleven rules. Figure 1 presents the flow chart for this study including the solar power plant (SPP) design procedure and the applied methods.

**Figure 1.** The solar PV panel system flow chart.

Thus, the design of this study is as follows; in Section 2, the solar PV system design is explained. Simulation of the solar PV system is discussed in Section 3. The data related to solar PV system parameters are analyzed, additionally, the performance prediction and optimization methods; the RSM and ANFIS approaches are given in Section 4. Section 5 covers the results, finding and discussions for the PV system. Section 5 is devoted to the conclusions.
