###### Abstract
However, recent trends suggest that the research direction is moving toward customized ML models or models that are designed for a particular application. In other words, the highest degree of accuracy can be achieved through the development of a case-based ML model [6]. It was found that the enhancement of the predictability of renewable energy systems and demand enables the replacement of expensive standby power generation assets with advanced control and optimization systems [7]. In this study, ANFIS, which is a combination of an artificial neural network (ANN) and fuzzy logic, is used to evaluate the performance of an integrated solar combined cycle gas/steam power plant. Thereafter, it was hybridized for the training phase with evolutionary algorithms, namely particle swarm optimization (PSO) and genetic algorithm (GA), which are meta-heuristic algorithms. Some studies have successfully applied ANFIS in the prediction of power generation in solar power plants, control and modeling of interconnected combined cycle gas turbine plants, and diagnosis of these systems. However, until recently, no work applying the ANFIS model in the design or analysis of the efficiency or the performance of a combined cycle gas turbine was found in the literature [8]. To date, this gap is further widened by the integration of renewable energy systems, namely solar systems, in conventional combined cycle gas turbine plants in an effort to reduce their carbon footprint and increase the overall efficiency of the plants. Furthermore, there is a necessity to explore advanced optimization techniques to improve the prediction accuracy of the ANFIS model for these hybrid power systems. The present investigation is motivated by and undertaken to fill this gap. Some researchers have conducted related work; for example, [PERSON] et al. [9] tried to determine the optimal design parameters of a solar-only power tower system using molten salt for storage. They used machine learning, specifically a hybrid adaptive neuro-fuzzy inference system with a combination of a genetic algorithm and a teaching-based optimization algorithm. They used four parameters as inputs, namely latitude, longitude, design point direct normal irradiance (DNI), and solar multiple (SM), which is the ratio of the solar field size to the power block, all expressed as nominal thermal power. Three parameters (annual energy produced, levelized cost of energy, and capacity factor) were used as output parameters in the analysis. An extremely high correlation coefficient, close to 1, for the hybrid ANFIS-GATLBO was reported in this study. [PERSON] and [PERSON] [10] investigated the suitability of the adaptive neuro-fuzzy inference system (ANFIS) method for predicting the performance parameters of a solar thermal energy system (STES) used for household hot water and space heating applications. They found that the predicted values were in agreement with the experimental data, with mean relative errors less than 1%. The ANFIS results were compared to the ANN results, and it was found that the ANFIS approach performed slightly better than the ANN one because of higher accuracy and reliability in the prediction of the performance of the energy system. However, the ANN model was more flexible in terms of implementation and reduced computation time. [PERSON] et al. [11] presented a comparison between ANN and ANFIS to predict the daily power output of a solar power plant in eastern Morocco. The plant itself is an integrated solar combined cycle (ISCC) plant, which is made of a CSP plant and a natural gas-fired combined cycle (NGCC) power plant. The whole system has two gas turbines fueled by natural gas, a steam turbine, two recovery boilers, a solar field (made of parabolic troughs), and a heat exchanger. The total installed capacity is 472 MW, of which 20 MW is of solar source.