Modeling of Capacity Factor in Rembang Coal-Fired Steam Power Plant Using Regression Modeling

Abstract

Coal-Fired Steam Power Plant (PLTU) Rembang is an important power plant in the Central Java electricity system. Like other coal-fired steam power plants, fuel cost is the most significant expense when operating the PLTU Rembang. During the 2019-2021 period, the average fuel cost was 73.88% of total costs. One of the ways to reduce fuel costs is by improving the accuracy of fuel demand planning. Fuel procurement planning is very dependent on the projected amount of electricity sales from power plant, which is largely determined by the power plant's Capacity Factor (CF). However, PLTU Rembang does not have any CF prediction modeling. This research developed and compared four prediction models: random forest regression, support vector regression, multiple polynomial regression, and multiple linear regression. Based on the comparison of validation from the four prediction model with MAPE and R-squared parameters, the multiple linear regression models is the best model, with the lowest MAPE of 7.83% and the highest R-squared of 0.8814. This multiple linear regression model can be used to predict the CF of PLTU Rembang in the future so that fuel demand planning is more accurate.

Country : Indonesia

1 Ery Perdana2 Sulardjaka3 Budi Warsito

  1. Master of Energy, School of Postgraduate, Diponegoro University, Semarang, Indonesia
  2. Department of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
  3. Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia

IRJIET, Volume 8, Issue 4, April 2024 pp. 51-56

doi.org/10.47001/IRJIET/2024.804006

References

  1. L. Rochman, Feasibility Study PLTU-1 Jawa Tengah 2x (300-400) MW Rembang. Jakarta: PT. Arkonin Engineering MP, 2017.
  2. PT PLN (Persero), “Financial Highlight AMC Jawa 2011-2021,” Jakarta, 2022.
  3. Kementerian ESDM, “Permen ESDM No 10 Tahun 2017,” 2017.
  4. PT PLN (Persero), “Protap Deklarasi Kondisi Pembangkit dan Indeks Kinerja Pembangkit.” 2017.
  5. D. Mariani, Y. M. Safarudin, N. F. Aulia, A. H. Suudy, N. A. MS, and B. M. Hermawan, “Perhitungan Economic Dispatch Tiga Buah Pembangkit Menggunakan Metode Merit Order Dengan Mempertimbangkan Losses,” Eksergi J. Tek. Energi, vol. 17, no. 3, pp. 221–232, 2021.
  6. Delima and Syafii, “Operasi Ekonomis dan Unit Commitment Pembangkit Thermal Pada Sistem Kelistrikan Jambi,” J. Nas. Tek. Elektro, vol. 5, no. 3, 2016, doi: 10.20449/jnte.v5i3.331.
  7. M. F. Sanner, “Python : A Programming Language for Software Integration and Development,” 1999.
  8. M. H. Kutner, C. J. Natchtseim, J. Neter, and W. Li, Applied Linear Statistical Models, 5th ed., vol. 29, no. 2. New York: McGraw-Hill, 2004. doi: 10.1080/00224065.1997.11979760.
  9. G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, “An Introduction Statistical Machine Learning With Applications in Python,” Springer Texts Stat., pp. 425–472, 2023, [Online]. Available: https://www.statlearning.com/
  10. D. Polzer, “7 of the Most Used Regression Algorithms and How to Choose the Right One,” https://towardsdatascience.com/, 2021. https://towardsdatascience.com/7-of-the-most-commonly-used-regression-algorithms-and-how-to-choose-the-right-one-fc3c8890f9e3 (accessed Oct. 24, 2022).
  11. B. Putro, M. T. Furqon, and S. H. Wijoyo, “Prediksi Jumlah Kebutuhan Pemakaian Air Menggunakan Metode Exponential Smoothing,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 4679–4686, 2018.
  12. S. Weisberg, Applied Linier Regression, Fourth. Wiley, 2014.
  13. N. Sudjana, “Metode Statistika Edisi keenam,” Bandung PT. Tarsito, 2005.
  14. S. Raschka, “Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning,” 2018.
  15. Z. Jin, J. Shang, Q. Zhu, C. Ling, W. Xie, and B. Qiang, “RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12343 LNCS, pp. 503–515, 2020, doi: 10.1007/978-3-030-62008-0_35.
  16. L. Bi, O. Tsimhoni, and Y. Liu, “Using the support vector regression approach to model human performance,” IEEE Trans. Syst. Man, Cybern. Part ASystems Humans, vol. 41, no. 3, pp. 410–417, 2011, doi: 10.1109/TSMCA.2010.2078501.
  17. C. H. Wu, J. M. Ho, and D. T. Lee, “Travel-time prediction with support vector regression,” IEEE Trans. Intell. Transp. Syst., vol. 5, no. 4, pp. 276–281, 2004, doi: 10.1109/TITS.2004.837813.
  18. Y. G. Akhlaghi, X. Zhao, S. Shittu, J. Li, C. Science, and T. Engineering, “A statistical model for dew point air cooler based on the multiple polynomial regression approach,” 2019.
  19. K. A. Marill, “Advanced Statistics: Linear Regression, Part II: Multiple Linear Regression,” Acad. Emerg. Med., vol. 11, no. 1, pp. 94–102, 2004, doi: 10.1197/j.aem.2003.09.006.