Long-term Electricity Power Forecasting at PT PLN UP3 Kediri Using Trend and Monte Carlo Simulation Methods


Naizatul Zainul Rofiqi
M. Cahyo Bagaskoro
Langlang Gumilar
Aripriharta Aripriharta


Community consumption power in the use of electricity resources is growing very rapidly. This is because the power source is the main need for the community. Without electricity, daily needs cannot run smoothly. In various regions, especially Kediri, where the needs are changing, it is necessary to predict and provide electricity to meet the consumption needs of the people. There are many things that can be done to predict the need for electric power, one of which is forecasting. Forecasting methods that can be used include trend and Monte Carlo Simulation. The results of this study indicate that Monte Carlo simulations are better at predicting long-term electrical power requirements at ULP3 Kediri. Long-term electricity demand can be predicted with the equation: Yt =138691 + 6709 x t + 231 x t2. The results of the research can help PLN UP3 Kediri to provide electricity for consumers.


How to Cite
Naizatul Zainul Rofiqi, M. Cahyo Bagaskoro, Sujito, Langlang Gumilar, & Aripriharta, A. (2023). Long-term Electricity Power Forecasting at PT PLN UP3 Kediri Using Trend and Monte Carlo Simulation Methods. Jurnal E-Komtek (Elektro-Komputer-Teknik), 7(1), 21-38. https://doi.org/10.37339/e-komtek.v7i1.1131


[1] S. M. Al-Alawi and S. M. Islam, “Principles of electricity demand forecasting Part I Methodologies,” 1996.
[2] Y. Tüzüntürk, A. Eren ?enaras, and P. H. Kemal Sezen, “Forecasting Water Demand By Using Monte Carlo Simulation,” 2015. [Online]. Available: http://www.akademikbakis.org
[3] Y. P. Enggar. Afinda Y, “Peramalan Jangka Panjang Beban Listrik Sektor Rumah Tangga di Jawa Timur Menggunakan Metode Trend Proyeksi dan Regresi Linier,” 2020.
[4] M. W. S. I. H. and U. T. Kartini. Purnama, “Peramalan Kebutuhan Energi Listrik Jangka Panjan Sektor Rumah Tangga UID Jawa Timur Menggunakan Metode Analiysis Time Series?: Proyeksi Trend Quadratic dan Regresi Linier Berbasis Software MINITAB V19,” 2021.
[5] S. Dang, L. Peng, J. Zhao, J. Li, and Z. Kong, “A Quantile Regression Random Forest?Based Short?Term Load Probabilistic Forecasting Method,” Energies (Basel), vol. 15, no. 2, Jan. 2022, doi: 10.3390/en15020663.
[6] Y. et al. Lou, “Optimized partitioning and priority assignment of real-time applications on heterogeneous platforms with hardware acceleration,” Journal of Systems Architecture, vol. 124, Mar. 2022, doi: 10.1016/j.sysarc.2022.102416.
[7] Ö. F. Ertugrul, “Forecasting electricity load by a novel recurrent extreme learning machines approach,” International Journal of Electrical Power and Energy Systems, vol. 78, pp. 429–435, Jun. 2016, doi: 10.1016/j.ijepes.2015.12.006.
[8] Y.-Q. He et al., A Long-Term Forecast Method for the Investment Demand of Power Grid Based on Linear Regression and Error Correction of Variable. 2019.
[9] S. R. Khuntia, J. L. Rueda, and M. A. M. M. van der Meijden, “Long-term electricity load forecasting considering volatility using multiplicative error model,” Energies (Basel), vol. 11, no. 12, Dec. 2018, doi: 10.3390/en11123308.
[10] M. Salami, F. M. Sobhani, and M. S. Ghazizadeh, “Short-term forecasting of electricity supply and demand by using the wavelet-PSO-NNS-SO technique for searching in big data of iran’s electricity market,” Data (Basel), vol. 3, no. 4, Dec. 2018, doi: 10.3390/data3040043.
[11] O. T. B??K?N and A. Ç?FC?, “Forecasting of Turkey’s Electrical Energy Consumption using LSTM and GRU Networks,” Bilecik ?eyh Edebali Üniversitesi Fen Bilimleri Dergisi, Dec. 2021, doi: 10.35193/bseufbd.935824.
[12] Y. Zheng, Z. Shao, Y. Zhang, and L. Jian, “A systematic methodology for mid-and-long term electric vehicle charging load forecasting: The case study of Shenzhen, China,” Sustainable Cities and Society, vol. 56, May 2020, doi: 10.1016/j.scs.2020.102084.
[13] O. Alsayegh, O. Almatar, F. Fairouz, and A. Al-Mulla Ali, “Prediction of the long-term electric power demand under the influence of A/C systems,” Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, vol. 221, no. 1, pp. 67–75, Feb. 2007, doi: 10.1243/09576509JPE289.
[14] F. Mus’abil Hakim, A. Soeprijanto, O. P. Jurusan, and T. Elektro, “Study Keandalan Jaringan Distribusi 20 kV di Wilayah Malang dengan Metode Monte Carlo,” 2012.
[15] D. Guna and M. Sebagian, “Forecasting Performa Transformator Daya Transmisi Tegangan Tinggi Gardu Iinduk Kapasitas 60MVA Menggunakan Metode Monte Carlo,” 2020. [Online]. Available: http://digilib.mercubuana.ac.id/
[16] T. Tito and R. A. Ruli, “Penerapan Metode Monte Carlo untuk Peramalan Beban Puncak Listrik Jangka Pendek,” 2019.
[17] R. Oktavian, S. Rony, S. T. Wibowo, E. I. Made, and Y. Negara, “Analysis Of Power System Rreliability in Bali Region 150KV Power System Using Monte Carlo Methode,” 2017.
[18] Marchy Pallo, “Pemodelan Beban Puncak Konsumsi Listrik di Wilayah Kupang Menggunakan Bayesian Mixture Normal Autoregressif,” 2016.
[19] H. Bakhtiari, J. Zhong, and M. Alvarez, “Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation,” Applied Energy, vol. 290, May 2021, doi: 10.1016/j.apenergy.2021.116719.
[20] K. Erny, L. Manik, S. Santi, P. Rahayu, M. Si, and I. Setiawan, “Modelling of Electric Energy Needs in East Java with Simultaneous Equations Approach Undergraduate,” 2016.