Data-Driven Machine Learning Models for Steam Turbine Efficiency Prediction
DOI:
https://doi.org/10.26877/asset.v8i4.2378Keywords:
Steam Turbine Efficiency, Machine Learning, Data-Driven Prediction, Thermal Power Plant, Feature ImportanceAbstract
Increased electricity consumption in Indonesia requires coal-fired power plants to operate at high efficiency. This study proposes a data-driven approach to predict the efficiency of coal-fired steam turbines by utilizing 844 historical operational data points from the XYZ coal-fired power plant. Input variables include main steam pressure and temperature, main steam flow, final feedwater temperature, and condenser vacuum pressure. Four machine learning algorithms, namely Random Forest, Extra Trees, XGBoost, and Support Vector Regression, were evaluated using R², RMSE, and MAE. Based on the results of the study, it can be concluded that predictive models using Random Forest, Extra Trees, and XGBoost can predict steam turbine efficiency with high accuracy, especially after hyperparameter adjustments that increase the R² value and reduce the RMSE and MAE values in all models. The Extra Trees model proved to be the best model with an R² value of 0.8655 and an MAE of 0.5612, demonstrating its ability to capture the complex relationship between operational variables and turbine efficiency. This approach has practical implications for continuous improvement strategies in coal-fired power plant operations.
References
[1] D. F. Silalahi, A. Blakers, and C. Cheng, “100% renewable electricity in Indonesia,” Energies, vol. 17, no. 1, p. 3, 2023, doi: https://doi.org/10.3390/en17010003.
[2] R. B. Wibowo and S. M. Damayanti, “Financial Feasibility of Geothermal Project J-3 and J-4 in Indonesia,” Cognizance Journal of Multidisciplinary Studies, vol. 5, no. 5, pp. 279–287, May 2025, doi: https://doi.org/10.47760/cognizance.2025.v05i05.018.
[3] A. E. Putra, “Literature Review: Study on the Utilization of Solar Power Plants (PLTS) as an Effort to Increase Electricity Access in Rural and Remote Areas,” International Journal of Research in Vocational Studies (IJRVOCAS), vol. 5, no. 1, pp. 31–43, 2025, doi: https://doi.org/10.53893/ijrvocas.v5i1.352.
[4] A. S. Karakurt and Ü. Güneş, “Performance analysis of a steam turbine power plant at part load conditions,” Journal of Thermal Engineering, vol. 3, no. 2, pp. 1121–1128, 2017, doi: https://doi.org/10.18186/thermal.298611.
[5] O. J. Khaleel, F. Basim Ismail, T. Khalil Ibrahim, and S. H. bin Abu Hassan, “Energy and exergy analysis of the steam power plants: A comprehensive review on the Classification, Development, Improvements, and configurations,” Ain Shams Engineering Journal, vol. 13, no. 3, p. 101640, 2022, doi: https://doi.org/10.1016/j.asej.2021.11.009.
[6] F. A. Hatem, M. K. A. Alhumairi, M. A. Al-Obaidi, A. T. Mohammad, and A. S. K. Darwish, “Upgrading Gas Turbine Efficiency for Sustainable Power Generation: An Energy and Exergy Analyses,” Results in Engineering, vol. 105489, 2025, doi: https://doi.org/10.1016/j.rineng.2025.105489.
[7] D. Adams, D. H. Oh, D. W. Kim, C. H. Lee, and M. Oh, “Prediction of SOx–NOx emission from a coal-fired CFB power plant with machine learning: Plant data learned by deep neural network and least square support vector machine,” Journal of Cleaner Production, vol. 270, p. 122310, 2020, doi: https://doi.org/10.1016/j.jclepro.2020.122310.
[8] J. I. Aizpurua, S. D. J. McArthur, B. G. Stewart, B. Lambert, J. G. Cross, and V. M. Catterson, “Adaptive Power Transformer Lifetime Predictions Through Machine Learning and Uncertainty Modeling in Nuclear Power Plants,” IEEE Transactions on Industrial Electronics, vol. 66, no. 6, pp. 4726–4737, 2019, doi: https://doi.org/10.1109/TIE.2018.2860532.
[9] F. Aminifar, M. Abedini, T. Amraee, P. Jafarian, M. H. Samimi, and M. Shahidehpour, A review of power system protection and asset management with machine learning techniques, vol. 13, no. 4. Springer Berlin Heidelberg, 2022. doi: https://doi.org/10.1007/s12667-021-00448-6.
[10] M. M. Forootan, I. Larki, R. Zahedi, and A. Ahmadi, “Machine Learning and Deep Learning in Energy Systems: A Review,” Sustainability (Switzerland), vol. 14, no. 8, 2022, doi: https://doi.org/10.3390/su14084832.
[11] Z. Liu and I. A. Karimi, “Gas turbine performance prediction via machine learning,” Energy, vol. 192, 2020, doi: https://doi.org/10.1016/j.energy.2019.116627.
[12] P. Hundi and R. Shahsavari, “Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants,” Applied Energy, vol. 265, no. February, p. 114775, 2020, doi: https://doi.org/10.1016/j.apenergy.2020.114775.
[13] S. Alketbi, A. B. Nassif, M. A. Eddin, I. Shahin, and A. Elnagar, “Predicting the power of a combined cycle power plant using machine learning methods,” Proceedings of the 2020 IEEE International Conference on Communications, Computing, Cybersecurity, and Informatics, CCCI 2020, 2020, doi: https://doi.org/10.1109/CCCI49893.2020.9256742.
[14] J. Dhanalakshmi, N. Ayyanathan, and N. S. Pandian, “Energy analytics and comparative performance analysis of machine learning classifiers on power boiler dataset,” Proceedings of the 11th International Conference on Advanced Computing, ICoAC 2019, pp. 72–78, 2019, doi: https://doi.org/10.1109/ICoAC48765.2019.246819.
[15] L. Zhao and F. You, “A data-driven approach for industrial utility systems optimization under uncertainty,” Energy, vol. 182, pp. 559–569, 2019, doi: https://doi.org/10.1016/j.energy.2019.06.086.
[16] C. Emexidis and P. Gkonis, “The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines,” Applied Sciences (Switzerland), vol. 14, no. 22, 2024, doi: https://doi.org/10.3390/app142210276.
[17] W. Zhang, D. Yang, and H. Wang, “Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey,” IEEE Systems Journal, vol. 13, no. 3, pp. 2213–2227, 2019, doi: https://doi.org/10.1109/JSYST.2019.2905565.
[18] J. Jung, H. Han, K. Kim, and H. S. Kim, “Machine learning‐based small hydropower potential prediction under climate change,” Energies, vol. 14, no. 12, 2021, doi: https://doi.org/10.3390/en14123643.
[19] G. Prokhorskii, M. Preißinger, S. Rudra, and E. Eder, “An interpretable and adaptable data-driven model for performance prediction in thermal plants,” Energy Conversion and Management: X, vol. 26, no. November 2024, p. 100950, 2025, doi: https://doi.org/10.1016/j.ecmx.2025.100950.
[20] J. F. Olesen and H. R. Shaker, “Predictive maintenance for pump systems and thermal power plants: State-of-the-art review, trends and challenges,” Sensors (Switzerland), vol. 20, no. 8, 2020, doi: https://doi.org/10.3390/s20082425.
[21] C. Bisset, P. V. Z. Venter, and R. Coetzer, “A systematic literature review on machine learning applications at coal-fired thermal power plants for improved energy efficiency,” International Journal of Sustainable Energy, vol. 42, no. 1, pp. 845–872, 2023, doi: https://doi.org/10.1080/14786451.2023.2244618.
[22] G. Prokhorskii, M. Preißinger, S. Rudra, and E. Eder, “An interpretable and adaptable data-driven model for performance prediction in thermal plants,” Energy Conversion and Management: X, vol. 26, no. November 2024, p. 100950, 2025, doi: https://doi.org/10.1016/j.ecmx.2025.100950.
[23] R. S. Kumar, A. R. Singh, P. L. Narayana, V. S. Chandrika, M. Bajaj, and I. Zaitsev, “Hybrid machine learning framework for predictive maintenance and anomaly detection in lithium-ion batteries using enhanced random forest,” Scientific Reports, vol. 15, no. 1, pp. 1–20, 2025, doi: https://doi.org/10.1038/s41598-025-90810-w.
[24] S. Chatterjee, P. W. Khan, and Y. C. Byun, “Recent advances and applications of machine learning in the variable renewable energy sector,” Energy Reports, vol. 12, pp. 5044–5065, 2024, doi: https://doi.org/10.1016/j.egyr.2024.09.073.
[25] X. Han, Q. Zhu, J. Guan, and Z. Han, “Research on wet steam condensation flow characteristics of steam turbine last stage under zero output condition,” International Journal of Thermal Sciences, vol. 179, p. 107691, 2022, doi: https://doi.org/10.1016/j.ijthermalsci.2022.107691.
[26] A. Al-Humairi, E. Khalis, Z. A. Al Hemyari, and P. Jung, “Machine learning-based predictive maintenance for photovoltaic systems,” AI, vol. 6, no. 7, p. 133, 2025, doi: https://doi.org/10.3390/ai6070133.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Advance Sustainable Science Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
