###### Abstract
For modeling purposes, six variables (daily direct normal irradiance, day of the month, mean wind speed, daily mean ambient temperature, relative humidity, and previous daily electric production) were used as inputs, while the daily electricity generation of the plant was used as the output. They concluded that both the ANN and ANFIS models had similar performance with regard to prediction accuracy. The coefficient of correlation, R\({}^{2}\), was in the range of 0.94 for the training and testing phases, while the RMSE was in the range of 0.072 for training and 0.089 for testing. In an effort to identify the operating variables that can improve the efficiency of a combined cycle gas turbine (CCGT), [PERSON] et al. [8] modeled the cycle using an adaptive neuro-fuzzy inference system (ANFIS). Three input variables, namely, the compression ratio in the gas cycle, the pressure of the bled steam for water heating, and the heat lost to the steam turbine exhaust, were considered. They found that the pressure ratio in the gas turbine had the most significant effect on the efficiency of the combined system. The ANFIS results were compared to the analytical results and found to be similar. [PERSON] et al. [12] conducted a critical review of optimization techniques for the thermal performance of solar energy devices using metaheuristic algorithms. Many power arrangements integrating solar PV, CSP (dish collectors, heliostats, parabolic troughs), geothermal wells, etc. were covered. It was highlighted in this study that more research is needed in hybrid optimization strategies to solve complex obstacles and obtain high efficacy in solar energy systems. Evolutionary algorithms for multi-objective optimization of hybrid renewable energy systems were recommended for future research. [PERSON] et al. [13] studied the optimization of an integrated solar combined cycle. The system was made of an open-air Brayton cycle, which was thermodynamically connected to a base steam Rankine cycle and a CSP hybrid plant. The CSP plant was based on pressurized air receiver technology assisted by a natural gas burner. For analysis, the Thermoflex software tool was used. An exclusive contribution of thermal energy through the solar thermal receiver was first considered, and then a mixed thermal contribution of solar energy and natural gas was examined. Scenarios of different configurations of the combined system were considered. It was found, among other things, that the overall system efficiency was far above that of modern conventional combined cycle systems, whose conversion efficiencies are around 60% because of pressure limitations for pressurized air receivers. An investigation the performance of an integrated solar combined cycle (ISCC) plant situated in the tropical climate of southern Algeria was carried out by [PERSON] et al. [14]. The plant was a combination of a parabolic trough solar field with a fossil fuel combined cycle, which was made of two gas turbines and a steam turbine. The authors developed, from first principles, a model for each component of the plant and concluded that an overall thermal efficiency of about 60% could be reached. It is worth noting that developing a thermodynamic model for such a complex system or plant is a very tedious process, and some assumptions need to be properly made. [PERSON] et al. [15] developed and validated a dynamic simulation model for an integrated solar combined cycle (ISCC) power plant in Karaymat in Egypt using Apros (Advanced PROcess Simulation), a design software. The power plant (135 MW total electrical power) consisted mainly of a parabolic trough collector solar field, a gas turbine (70 MW), a steam turbine (65 MW), and a heat recovery steam generator (HRSG). The boiler used hot water from the heat exchanger with the heat-transfer fluid of the CSP field, and also flue gas from the gas turbine. The model was initialized and tuned using operational data measured from the plant.