###### Abstract  
The hybridization of conventional thermal power plants by the incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision making, and streamline power production planning. This paper explores the novel application of machine learning techniques to predict the performance of hybrid thermal power plants, specifically the integrated solar combined cycle power plant (ISCCPP). These plants combine conventional thermal power generation with renewable energy sources, making them crucial in the context of carbon reduction and enhanced efficiency. We employ three machine learning approaches: the adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized via particle swarm optimization (ANFIS-PSO), and ANFIS optimized through a genetic algorithm (ANFIS-GA). These methods are applied to the complex ISCCPP, comprising steam and gas turbine sections and a concentrated solar power system. The results highlight the accuracy of ANFIS-based models in evaluating and predicting plant performance, with an exceptional overall correlation coefficient of 0.9991. Importantly, integrating evolutionary algorithms (PSO and GA) into ANFIS significantly enhances performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA, with ANFIS-GA outperforming the others. This research provides a robust tool for designers, energy managers, and decision makers, offering valuable support in assessing the performance of hybrid thermal power plants. As the world transitions to cleaner energy sources, the insights gained here are poised to have a significant impact on the growing number of these thermal power plants globally. hybrid thermal power plant; integrated solar combined cycle power plant (ISCCPP); adaptive neuro-fuzzy inference system (ANFIS); particle swarm optimization (PSO); genetic algorithm (GA); ANFIS-PSO; ANFIS-GA; evolutionary algorithms +
Footnote †: journal: Applied Sciences  
Hybridizing thermal power plants is the way forward if governments take energy transition and climate change seriously. Moreover, doing so can improve the overall efficiency of the power plant [2]. For the system studied in this research, the gas system and the solar system interact through the heating unit, where low-grade energy in the exhaust gas from a gas turbine is used to produce steam by heating water, in addition to the solar heating through a heat-transfer fluid that flows in a concentrated solar power system which is made of parabolic troughs, in this case. Engineers are very concerned and sensitive to the use of energy or the performance of systems or plants that they design or operate. With the increasing level of complexity of hybrid thermal power plants, it becomes much harder to assess or evaluate the performance of such systems from a thermodynamics standpoint or governing equations [3]. Therefore, there is a need for alternative approaches such as those developed as part of machine learning. Machine learning (ML) techniques have been adopted in recent years to model and analyze hybrid energy systems. ML models have been hybridized and refined with optimization strategies in a quest for better solutions. As a result, an outstanding rise in the accuracy, precision, robustness, and generalization ability of ML models in energy systems using hybrid models has been noted. Hybridization of ML models has even been reported to be effective in the advancement of prediction models [4; 5], particularly for renewable energy systems.