## 4 Conclusions  
In this investigation, the ANFIS model and the hybrid ANFIS model combined with evolutionary algorithms (PSO and GA) were alternatively employed to model and analyze the performance of a combined cycle gas turbine power plant integrated with a concentrated solar power system utilizing parabolic troughs. The results demonstrated  
Figure 17: ANFIS-GA regression plots for (**a**) training, (**b**) testing, and (**c**) all data. Note: Blue line represents the liner data.  
remarkable accuracy and efficacy across all the models, with coefficient of correlation (R2) values reaching an impressive 0.9991 for ANFIS, 0.9994 for ANFIS-PSO, and 0.9997 for ANFIS-GA. Additionally, the root mean square errors were consistently minimal, substantiating the precision of these ANFIS-based approaches. Notably, the accuracy exhibited an upward trajectory as the foundational ANFIS model was enriched through integration with metaheuristic optimization techniques. The application of evolutionary algorithms (PSO or GA) to hybridize ANFIS showcased its robustness and reliability in analyzing and predicting the integrated solar combined cycle power plant's performance. However, it is essential to acknowledge that this hybridization, while enhancing accuracy, also led to an increase in computation time. Remarkably, among the ANFIS-based methodologies explored, the ANFIS-GA model emerged as a standout performer for the scenarios investigated in this study. The significance of this work is underscored by its revelation of the potential inherent in ANFIS-based methodologies for accurate performance prediction within hybrid thermal power plants. These methodologies present themselves as practical alternatives to more phenomenological approaches. As we look towards the future, several avenues for further exploration come to light:  
* Delving into the influence of clustering techniques, varying parameters, and the intrinsic model parameters on the ANFIS approach's performance.
* Thoroughly investigating the pivotal parameters of the hybrid ANFIS-PSO and ANFIS-GA models and discerning their impact on accurately forecasting the integrated solar combined cycle power plant's performance.  
In conclusion, this study highlights the efficacy of ANFIS-based methodologies in precisely predicting hybrid thermal power plant performance. By leveraging the capabilities of evolutionary algorithms, these methodologies can serve as invaluable tools, offering a level of accuracy that is both robust and practical when compared to traditional phenomenological methodologies. The performance of power plants is related to the output power, but the direct impact on the environment can't be ignored, noting that pollution, especially for thermal power plants is reduced when high efficiency or performance is maintained. This pollution reduction is due to, among other things, lower fossil fuel consumption for the same output power. The major limitation in this work was the non-availability of data on the performance of hybrid thermal power plants or integrated solar combined cycle power plants.  
Conceptualization, K.T.K. and L.K.T.; methodology, K.T.K.; software, K.T.K.; validation, K.T.K., I.O.O. and L.K.T.; formal analysis, K.T.K.; investigation, K.T.K.; resources, L.K.T.; data curation, K.T.K.; writing--original draft preparation, K.T.K.; writing--review and editing, K.T.K.; visualization, K.T.K.; supervision, L.K.T.; project administration, L.K.T.; funding acquisition, L.K.T. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by University of Johannesburg.  
Not applicable.  
Data Availability Statement: Data used in this study can be found at [[https://data.mendeley.com/datasets/crzxm8](https://data.mendeley.com/datasets/crzxm8) ggwn]([https://data.mendeley.com/datasets/crzxm8](https://data.mendeley.com/datasets/crzxm8) ggwn) (accessed on 3 March 2023).  
The authors thank the University of Johannesburg for financial support.  
The authors declare no conflict of interest.