Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.

1. Abuayyash K, Alsamamra H, Teir MA, et al. (2024). Wind speed prediction in Jerusalem using machine learning algorithms: A case study of using ANFIS and KNNR. American Journal of Modern Energy, 10(2), 25-37. https://doi.org/10.11648/j.asme.20241002.12
2. Akdemir B. (2016). Prediction of hourly generated electric power using artificial neural network for combined cycle power plant. International Journal of Electrical Energy, 4(2), 91-95. doi: 10.18178/ijoee.4.2.91-95
3. Amirkhani S, Nasirivatan S, Kasaeian, et al. (2015). ANN and ANFIS models to predict the performance of solar chimney power plants. Renewable Energy, 83, 597-607. https://doi.org/10.1016/j.renene.2015.04.072
4. Ani KA, Agu CM. (2022). Predictive comarison and assessment of ANFIS and ANN, as effective efficient tools in modeling degradation of total petroleum hydrocarbon (TPH). Cleaner Waste Systems, 6, 100107. https://doi.org/10.1016/j.clwas.2022.100052
5. Bandić L, Hasičić M, Kevrić J. (2020). Prediction of power output for combined cycle power plant using random decision tree algorithms and ANFIS. In: Avdaković, S., Mujčić, A., Mujezinović, A., Uzunović, T., Volić, I. (eds). Advanced Technologies, Systems, and Applications IV, Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT 2019). Lecture Notes in Networks and Systems, 83, 406-416. Springer, Cham. https://doi.org/10.1007/978-3-030-24986-1_32
6. Bendary AF, Abdelaziz AV, Ismail MM, et al. (2021). Proposed ANFIS-based approach for fault tracking detection, clearing, and rearrangement of photovoltaic systems. Sensors, 21(7), 2-16. https://doi.org/10.3390/s21072269
7. Botsaris PN, Ntantis EL, Topalis C. (2014). Case study of solar energy use for the thermal needs of ferrous casting industrial unit. In: Proceedings of the 10th National Conference on Renewable Energy Resources, November 26-28, 2014, Thessaloniki, Greece
8. Chen J, Li H, Sheng D, Li W. (2015). A hybrid data-driven modeling method on sensor condition monitoring and fault diagnosis for power plant. International Journal of Electrical Power & Energy Systems, 71, 274–284. https://doi.org/10.1016/j.ijepes.2015.03.012
9. Ekki BB, Aksov UT. (2011). Prediction of building energy needs in early stage of design by using ANFIS. Journal of Engineering Systems Applications, 38(5), 5352-5358. https://doi.org/10.1016/j.eswa.2010.10.021
10. Elfaki E, Hassan A. (2018). Prediction of the electrical output power of combined cycle power plant using regression ANN model. International Journal of Computer Science and Control Engineering, 6(2), 9–2. doi: 0.5281/zenodo.1285164
11. El-Hadjik AA. (1990). The impact of atmospheric conditions on gas turbine performance. Journal of Engineering Gas Turbines Power. 112(4), 590-596. https://doi.org/10.1115/1.2906210
12. Ersayin E, Ozgener L. (2015). Performance analysis of combined cycle power plants: a case study. Renewable and Sustainable Energy Reviews, 43, 823- 842. https://doi.org/10.1016/j.rser.2014.11.082
13. Faahmi ATWK, Kashyzadeh KR, Ghorbani S. (2022). A comprehensive review on mechanical failures cause vibration in the gas turbine of combined cycle power plants. Engineering Failure Analysis, 134, 106094. https://doi.org/10.1016/j.engfailanal.2022.106094
14. Gas Turbine Combined Cycle Generation. (2020). Available online: File:Gas Turbine Combined Cycle Generation 01.svg - Wikimedia Commons (assessed 15th July, 2023).
15. Gill J, Singh J. (2017). Performance analysis of vapour compression refrigeration system Using an adaptive neuro-fuzzy inference system. International Journal of Refrigeration, 82, 436-446. https://doi.org/10.1016/j.ijrefrig.2017.06.019
16. Guerra MIS, De Araujo FMU, De Carvalho Neto JT, et.al. (2024). Survey on adaptive neural fuzzy inference system (ANFIS) architecture applied to photovoltaic systems. Energy Systems, 15, 505-541. https://doi.org/10.1007/s12667-022-00513-8.
17. Guleryuz D. (2021). Determination of industrial energy demand in Turkey using MLR, ANFIS and PSO-ANFIS. Journal of Artificial Intelligence Systems, 3, 16-34. https://doi.org/10.33969/AIS.2021.31002
18. Hoang T, Pawluskiewicz DK. (2016). The efficiency analysis of different combined cycle power plants based on the impact of selected parameters. International Journal of Smart Grid Clean Energy, 5 (2), 77-85. doi: 10.12720/sgce.5.2.77-85
19. Hundi P, Shahsavari R. (2020). Comparative studies among machine learning methods for performance estimation and health monitoring of thermal power plants. Applied Energy, 265,114775. https://doi.org/10.1016/j.apenergy.2020.114775
20. Ibrahim AM, IA, Lawan SM, Abdukabir R, et.al. (2024). Solar radiation prediction using an improved adaptive neuro-fuzzy inference system (ANFIS) optimization ensemble. In: Constantin Voloşencu, C (ed). Adaptive Neuro-Fuzzy Inference System as a Universal Estimator. IntechOpen, 1-22. http://dx.doi.org/10.5772/interchopen.1003891
21. Ibrahim TK, Mohammed MK, Awad OI, et.al. (2017). The optimum performance of the combined cycle power plant: A comprehensive review. Renewable and Sustainable Energy Reviews, 79, 459-474. https://doi.org/10.1016/j.rser.2017.05.060
22. Kaur S, Kaur T, Khanna R.. (2021). Adaptive neuro-fuzzy inference system-based output power controller in grid-connected photovoltaic systems. Energy Sources ,Part A: Recovery, Utilization, and Environmental Effects, 1890860. https://doi.org/10.1080/15567036.2021.1890860.
23. Krishnan S, Wu E. (2019). AlphaClean: Automatic generation of data cleaning pipelines. arXIv preprint arXiv: 1904.11827. https://doi.org/10.48550/arXiv.1904.11827
24. Lorencin I, Mrzljak V. Car Z. (2019). Genetic algorithm approach to the design of multi-layer perceptron for combined cycle power plant electrical power output estimation. Energies, 12(22),4352. https://doi.org/10.3390/en12224352.
25. Mathworks, ANFIS NeuroFuzzy. MATLAB software. 2022. https://uk.mathworks.com/help/fuzzly/anfis.html
26. Melin P, Castillo O, (2005). Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy logic inference system, Information Sciences, 170 (2-4), 133-151. https://doi.org/10.1016/j.ins.2004.02.015
27. Melin P, Soto J, Castillo OC, et al. (2012). A new approach for time series prediction using ensembles of ANFIS models. Applied Energy, 88(11), 3494 – 3506. https://doi.org/10.1016/j.eswa.2011.09.040
28. Mohandes M, Rehman S, Rahman SM. (2011). Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Applied Energy, 88 (11), 4024 – 4032. https://doi.org/10.1016/j.apenergy.2011.04.015.
29. Nabipour N, Mosavi A, Hajnal E, et al. (2019). Modelling Climate change impact on wind power resources adaptive neuro-fuzzy inference system. Engineering Applications of Computational Fluid Mechanics, 14(1), 491-506. https://doi.org/10.1080/19942060.2020.1722241
30. Ntantis EL. (2009). Capability expansion of non-linear gas path analysis. PhD thesis, Cranfield University, UK.
31. Ntantis EL, Li YG. (2009). The impact of measurement noise on gas turbine GPA diagnostics. In: Proceedings of the Sixth International Conference on Condition Monitoring & Machinery Failure Prevention Technologies; June 2009, Dublin, Ireland.
32. Ntantis EL, Li YG. (2013) The impact of measurement noise in GPA diagnostic analysis of a gas turbine engine. International Journal of Turbo Jet-Engines, 30(4), 401-408. https://doi.org/10.1515/tjj-2013-0024
33. Ntantis EL, Botsaris PN. (2015). Diagnostic methods for an aircraft engine performance. Journal of Engineering Science and Technology Review, 8(4), 64-72. doi: 10.25103/jestr.084.10
34. Ntantis EL, Botsaris PN. (2016). The impact of gas turbine component leakage fault on GPA performance diagnostics. Journal of Engineering Science and Technology Review, 9(1), 116-123. doi: 10.25103/jestr.091.18
35. Nugraha YT, Simnjuntak PM, Irwanto M. (2024). Analysis of forecast of renewable development in North Sumatra using ANFIS. Jurnal Media Elektro, XIII(1), 27-36. doi: 10.35508/jme.v13i1.15310
36. Olatomiwa L, Mekhilef S, Shamsirband S, et al. (2015). Potential of support vector regression for solar radiation prediction in Nigeria. Natural Hazards, 77, 1055-1068. https://doi.org/10.1007/s11069-015-1641-x
37. Pa M, Kazemi A. (2022). ANFIS–based prediction of power generation for combined cycle power plant. ArXIV: 2210.09011. https://doi.org/10.48550/arXiv.2210.09011
38. Panella M, Gallo AS. (2005). An input-output clustering approach synthesis of ANFIS Networks. IEEE Transactions of Fuzzy Systems. 13(1), 69-81. doi: 10.1109/TFUZZ.2004.839659
39. Pawar N. (2022). Development of photovoltaic power generation model using ANFIS. International Journal of Engineering Management Resources, 12(4), 199-208. https://doi.org/10.31033/ijemr.12.4.26
40. Petkovic D, Cojbasic A, Nikolic V. (2013). Adaptive neuro-fuzzy approach for wind turbine power coefficient. Renewable and Sustainable Energy Reviews, 28, 191-195. https://doi.org/10.1016/j.rser.2013.07.049.
41. Quamming Y., Menngshuo W., Hugo J.E., Isabelle G., and Yang Y., (2018)., ‘‘ Talking human out of learning applications: A survey of automated learning, ‘‘.
42. Qu Z, Xu J, Chi R, et al. (2021). Prediction of electricity generation from a combined cycle plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method. Energy, 227, 120309. https://doi.org/10.1016/j.energy.2021.120309
43. Revalthy SR, Kirubarakan V, Rajeshwaran,M, et.al. (2022). Design and analysis of ANFIS-based MPPT method for solar photovoltaic applications. International Journal of Photoenergy, 2022:9625564. https://doi.org/10.1155/2022/9625564
44. Rezakazemi M, Mosavi A, Shirizian,S. (2016). ANFIS pattern for molecular membranes Separation optimization. Journal of Molecular Liquids, 274, 470-476. https://doi.org/10.1016/j.molliq.2018.11.017
45. Sabouhi H, Abbaspour A, Fotuhi-Firuzabad M, Dehghanian P. (2016). Reliability modeling and availability analysis of combined cycle power plants. International Journal of Electrical Power & Energy Systems, 79, 108–119. https://doi.org/10.1016/j.ijepes.2016.01.007
46. Saleel CA .(2021). Forecasting the enerhu output from a combined cycle power plant using deep learning models. Case Studies in Thermal Engineering, 28, 101693. https://doi.org/10.1016/j.csite.2021.101693.
47. Salisu S, Mustafa MW, Mustapha M. (2018). A wavelet-based solar radiation prediction in Nigeria using the adaptive neuro-fuzzy approach. Indonesian Journal of Electric Engineering Computer Science, 12(3), 907-915. http://doi.org/10.11591/ijeecs.v12.i3.pp907-915
48. Samuel OD, Okwu MO, Tartibu LK, et al. (2021). Modelling of Nicotiana Tabacum L. oil. Biodiesel production: Comparison of ANN and ANFIS. Frontier in Energy Research, 8, 612165. https://doi.org/10.3389/fenrg.2020.612165
49. Savrum MM, Inci M. (2021). Adaptive neuro-fuzzy inference system combined with genetic algorithm to improve power extraction capability in fuel cell applications. Journal of Cleaner Production, 299,1269944. https://doi.org/10.1016/j.jclepro.2021.126944
50. Santamouris M., (2015). On the energy impact of urban heat island and global warmimg on buildings. Joirnal of Energy and Buildings, Vol.82, SP. 100, https://doi.org/10.1016/j.enbuild.2014.07.022.
51. Shanbedi M., Amiri A, Rashidi S, et al. (2014). Thermal performance Prediction of two-phase closed thermosiphon using adaptive neuro-fuzzy inference system. Heat Transfer Engineering, 36(3), 315-324. https://doi.org/10.1080/01457632.2014.916161
52. Shukla AK, Singh O. (2014). Effect of compressor inlet temperature and relative humidity on gas tutbine cycle performance. International Journal of Scientific & Engineering Research, 5 (5), 664-670.
53. Siddiqui R, Anwar H, Ullah F, et al. (2021). Power prediction of combined cycle power plant (CCPP) using machine learning algorithm-based aradigm. Wireless Communications and Mobile Computing, 2021, 9966395. https://doi.or(2015g/10.1155/2021/9966395
54. Tay KG, Muwafaq H, Choy CC. (2019). Electricity consumption forecasting using adaptive neuro-fuzzy inference system (ANFIS). Universal Journal of Electrical Electronic Engineering, 6(58), 37-48. Doi:10.13189/ujeee.2019.061606
55. Tiwari MK., Bajpai S, Dewangan LK. (2012). Prediction of industrial solid waste with ANFIS model and its comparison with ANN model – a case study of Durg- Bhilai Twin City India. International Journal of Engineering and Innovative Technology, 2(6)
56. UCI Machine Learning Repository: Combined Cycle Power Plant Data Set. (2012). Available online: https://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant (accessed on 20th July, 2023).
57. Vimala C, Jayanthi N, Vijayalakshmi P, et al. (2024). Enhancing predictive modelling in power plants: A comparative analysis of ANFIS and ANN for electrical energy output estimation. In: Proceedings of the International Conference on Inventive Computation Technologies (ICITCT), 2024, Nepal. 10.1109/ICICT60155.2024.10544753
58. Wang X, Sun X, Chu S, et al. (2023). Improved butterfly algorithm applied to prediction of combined cycle power plant. Mathematics and Computers in Simulation, 24,337-353. https://doi.org/10.1016/j.matcom.2022.08.009
59. Xezonakis V. (2024). Enhancing electric energy production in Combined Gas Plant (COGAS) and Combined Cycle Power Plant (CCPP) through artificial neural networks and adaptive neuro-fuzzy logic inference system. PhD thesis, University of South Africa. Deparment of Mechanical, Bioresources and Biomedical Engineering.
60. Xezonakis V, Samuel OD, Enweremadu CC. (2024). Modelling and Output Power Estimation of a Combined Gas Plant and a combined cycle plant using an Artificial Neural Network approach. Journal of Engineering, 2024, 5540010. https://doi.org/10.1155/2024/5540010
61. Zaaoumi A, Bah A, Ciocan M, et al. (2021). Estimation of tbe energy production of a parabolic trough solar power plant using analytical and artificial neural networks models. Renewable Energy, 170, 620-628. https://doi.org/10.1016/j.renene.2021.01.129
62. Zamani HA, Taghanaki SR, Karimi M, Anabloo M, Dandashi A. (2015). Implementing ANFIS for prediction of reservoir oil gas. Journal of Natural Gas Science and Engineering, 25, 325-334. https://doi.org/10.1016/j.jngse.2015.04.008
63. Zayed ME, Zhao J, Li W, et al. (2021). A hybrid adaptive neuro-fuzzy inference system integrated with equilibrium optimizer for predicting the energetic performance of solar dish collector. Energy, 235,121289. https://doi.org/10.1016/j.energy.2021.121289
64. Zhao Y, Foong LK. (2022). Predicting electrical power output for a combined cycle power plant using a novel artificial neural network optimized by electrostatic discharge algorithm. Measurement, 198, 11405. https://doi.org/10.1016/j.measurement.2022.111405
65. Zhang Y, Dong ZY, Kong W, Meng K. (2020). A composite anomaly detection system for data-driven power plant condition monitoring. IEEE Transactions on Industrial Informatics, 16 (7), 4390–4402. doi: 10.1109/TII.2019.2945366