Article Sidebar

Submitted
January 14, 2026
Accepted
April 25, 2026
Published
April 30, 2026
Download
PDF
Statistic
Read Counter : 16 Download : 8

Main Article Content

Abstract

The rapid growth of electricity demand in Batam, driven by increasing household and industrial consumption, necessitates accurate long-term energy forecasting. This study aimed to forecast household electricity demand in Batam from 2023 to 2047 using the multilayer perceptron (MLP) artificial neural network (ANN) model. Secondary data from PT PLN Batam (2013-2022), including customer numbers, electricity sales volume, and revenue, were analyzed. A total of 200 MLP models were trained, varying the number of hidden layers and nodes, with algorithms including BACKPROP, RPROP+, RPROP−, SAG, and SLR. The partial autocorrelation function (PACF) was used to determine the number of input layer nodes. The optimal model, using the smallest learning rate (SLR) algorithm with four hidden layers and ten nodes, achieved the best performance with the lowest mean squared error (MSE) of 35.93 and mean absolute percentage error (MAPE) of 0.47%. The projection results show a consistent increase in electricity demand, with a peak forecast of 2,114 GWh by 2047. These findings provide valuable insights for long-term energy planning and policy-making, ensuring adequate electricity supply and infrastructure development in Batam.  

Keywords

Energy Demand Forecasting Artificial Neural Network Multilayer Perceptron (MLP) Household Electricity Demand Batam

Article Details

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
How to Cite
Ginting, T. G., Hermansah, & Hanggara, Y. (2026). Household Electricity Demand Forecasting in Batam from 2023 to 2047 Using Multilayer Perceptron Neural Network. Enthusiastic : International Journal of Applied Statistics and Data Science, 6(1), 57–68. https://doi.org/10.20885/enthusiastic.vol6.iss1.art6
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. M.A. Saputra, and R. Rachmawati. “Perkembangan kawasan industri dan permukiman di Kota Batam tahun 1997-2007,” J. Bumi Indones., vol. 4, no. 1, pp. 409–417, 2015, Art. no. 222889, 2015.
  2. I. Saputra, D. Istardi, M. Ansori, C.B. Nugroho, and A. Maskarai. “Kajian proyeksi pemenuhan kebutuhan energi Provinsi Kepulauan Riau,” J. Integr., vol. 11, no. 2, pp. 125–129, Oct. 2019, doi: 10.30871/ji.v11i2.1116.
  3. N. Azizah, “Peramalan konsumsi beban listrik triple-SARIMA berdasarkan periodik seasonal (S),” B.S. thesis, Dept. Math. Inf. Technol., Institut Teknologi Kalimantan, Balikpapan, Indonesia, 2023.
  4. R. Saputra, S. Sunardiyo, A. Nugroho, and S. Subiyanto, “Analisis arsitektur jaringan syaraf tiruan-multilayer perceptron untuk efektivitas estimasi beban energi listrik PT. PLN (Persero) UP3 Salatiga,” ELKOMIKA, vol. 11, No. 3, pp. 664–676, Jul. 2023, doi: 10.26760/elkomika.v11i3.664.
  5. H. Hermansah, D. Rosadi, A. Abdurakhman, and H. Utami, “Automatic time series forecasting using nonlinear autoregressive neural network model with exogenous input,” Bull. Electr. Eng. Inform., vol. 10, no. 5, pp. 2836–2844, Oct. 2021, doi: 10.11591/eei.v10i5.2862.
  6. R. Saputra, S. Sunardiyo, A. Nugroho, and S. Subiyanto, “Implementasi multilayer perceptron artificial neural network untuk prediksi konsumsi energi listrik PT PLN (Persero) UP3 Salatiga,” Elektrika, vol. 15, no. 2, pp. 60–68, Oct. 2023, doi: 10.26623/elektrika.v15i2.6411.
  7. M.W. Purnama, S.I. Haryudo, W. Ariwibowo, and U.T. Kartini, “Peramalan kebutuhan energi listrik jangka panjang sektor rumah tangga UID Jawa Timur menggunakan metode analysis time series: Proyeksi tren quadratic dan regresi linear berbasis software Minitab V19,” J. Tek. Elektr., vol. 10, no. 2, pp. 485–495, May 2021, doi: 10.26740/jte.v10n2.p485-495.
  8. A.S. Caessar, I.M. Nrartha, and R. Rosmaliati, “Perbandingan metode koefisien dengan jaringan syaraf tiruan pada peramalan beban listrik jangka pendek,” Pros. Sem. Nas. Sains Teknol., vol. 6, 2024, pp. 176–183.
  9. P.A. Nugroho, “Implementasi jaringan syaraf tiruan multi-layer perceptron untuk prediksi penyinaran matahari Kota Bandung,” Komputa, J. Ilm. Komput. Inform., vol. 12, no. 1, pp. 83–90, May 2023, doi: 10.34010/komputa.v12i1.9419.
  10. A. Fathurrozi, G.D. Kalandro, A.R. Chaidir, S. Prasetyono, and M. Gozali, “Peramalan beban jangka panjang pada Gardu Induk Bangil dengan metode generalized regression neural network,” Techné, J. Ilm. Elektrotek., vol. 23, no. 2, pp. 185–198, Nov. 2024, doi: 10.31358/techne.v23i2.461.
  11. H. Hermansah, M. Muhajir, and P.C. Rodrigues, “Indonesian inflation forecasting with recurrent neural network long short-term memory (RNN-LSTM),” Enthusiastic, Int. J. Appl. Stat. Data Sci., vol. 4, no. 2, pp. 132–142, Oct. 2024, doi: 10.20885/enthusiastic.vol4.iss2.art5.
  12. M. Muhajir, H. Hermansah, A.P. Wicaksono, and L.A. Pratiwi, “A new approach for selecting optimal neuron number in input layer of generalized regression neural network model,” Baghdad Sci. J., vol. 22, no. 8, pp. 2738–2751, Aug. 2025, doi: 10.21123/2411-7986.5034.
  13. R. Yotenka, M. Muhajir, H. Hermansah, and P.C. Rodrigues, “Comparative analysis of activation functions in recurrent neural network: An application to Indonesian inflation forecasting,” Math. Modell. Eng. Probl., vol. 12, no. 3, pp. 754–762, Mar. 2025, doi: 10.18280/mmep.120302.
  14. H. Hermansah, “Strategy for enhancing GRU-RNN performance through parameter optimization,” Math. J. Modell. Forecast., Vol. 3, No. 1, pp. 16–24, Jun. 2025, doi: 10.24036/mjmf.v3i1.41.
  15. H. Hermansah, “Hybrid MODWT-ARMA model for Indonesia stock exchange LQ45 index forecasting,” Enthusiastic, Int. J. Appl. Stat. Data Sci., vol. 4, no. 1, pp. 51–57, Apr. 2024, doi: 10.20885/enthusiastic.vol4.iss1.art5
  16. H. Hermansah and M. Muhajir, “Comparison of accuracy between neural network and regression models in forecasting,” MAP J., vol. 5, no. 1, pp. 61–69, 2023, doi: 10.15548/map.v5i1.5949.
  17. T.S. Azzahra, J.J. Cerelia, F.A.L. Nugraha, and A.A. Pravitasari, “MRI-based brain tumor classification using inception ResNet V2,” Enthusiastic, Int. J. Appl. Stat. Data Sci, vol. 3, no. 2, pp. 163-175, Oct. 2023, doi: 10.20885/enthusiastic.vol3.iss2.art4.
  18. S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 competition: Results, findings, conclusion and way forward,” Int. J. Forecast., vol. 34, no. 4, pp. 802–808, Oct.–Dec. 2018, doi: 10.1016/j.ijforecast.2018.06.001.
  19. P. Gunoto and S. Sofyan, “Perancangan pembangkit listrik tenaga surya 100 Wp untuk penerangan lampu di ruang selasar Fakultas Teknik Universitas Riau Kepulauan,” Sigma Tek., vol. 3, no. 2, pp. 96–106, Nov. 2020, doi: 10.33373/sigma.v3i2.2754.
  20. S. Zahara, Y.N. Sukmaningtyas, R.M. Akbar, and M.Z. Abidin, “Pengaruh jumlah hidden layer dan neuron pada model multilayer perceptron untuk prediksi emas,” J. Ilm. ILKOMINFO, vol. 8, no. 2, pp. 269–275, Jul. 2025.
  21. E. Enung, H. Kasyanto, and R.R. Sari, “Penerapan algoritma multilayer perceptron (MLP) untuk memprediksi debit di Sungai Citarum bagian hulu (Pos Pengukuran Majalaya), Kab. Bandung, Jawa Barat,” Potensi, J. Sipil Politek., vol. 25, no. 1, pp. 1–8, Apr. 2023, doi: 10.35313/potensi.v25i1.4513.
  22. M. Tanaka, R. Takashima, S. Mori, and T. Oyama, “Special issue on developing sustainable energy and environmental systems in Japan: Energy crisis and challenges,” J. Energy Eng., vol. 143, no. 3, 2017, Art. no. Art. no. F2017001, doi: 10.1061/(ASCE)EY.1943-7897.0000457.
  23. H.S. Hippert, C.E. Pedreira, and R.C. Souza, “Neural networks for short-term load forecasting: A review and evaluation,” IEEE Trans. Power Syst., vol. 16, no. 1, pp. 44–55, Feb. 2001, doi: 10.1109/59.910780.
  24. C. Deb, F. Zhang, J. Yang, S.E. Lee, and K.W. Shah, “A review on time series forecasting techniques for building energy consumption,” Renew. Sustain. Energy Rev., vol. 74, pp. 902–924, Jul. 2017, doi: 10.1016/j.rser.2017.02.085.
  25. H. Hermansah, D. Rosadi, A. Abdurakhman, and H. Utami, “Selection of input variables of nonlinear autoregressive neural network model for time series data forecasting,” Media Stat., vol. 13, no. 2, pp. 116–124, Dec. 2020, doi: 10.14710/medstat.13.2.116-124.
  26. W. Triyuly, S. Triyadi, and S. Wonorahardjo, “Synergising the thermal behaviour of water bodies within thermal environment of wetland settlements,” Int. J. Energy Environ. Eng., vol. 12, pp. 55–68, 2021, doi: 10.1007/s40095-020-00355-z.
  27. S. Makridakis, E. Spiliotis, and V. Assimakopoulos. “Statistical and machine learning forecasting methods: Concerns and ways forward,” PloS One, vol. 13, no. 3, 2018, Art. no. e0194889, doi: 10.1371/journal.pone.0194889.
  28. Y. Rahmawati, and P.C. Taylor, Eds. Empowering Science and Mathematics for Global Competitiveness. Leide, The Nederlands: CRC Press/Balkema, 2019.
  29. T. Hong and S. Fan, “Probabilistic electric load forecasting: A tutorial review,” Int. J. Forecast., vol. 32, no. 3, pp. 914–938, Jul.–Sep. 2016, doi: 10.1016/j.ijforecast.2015.11.011.
  30. S. Fan and R.J. Hyndman, “Short-term load forecasting based on a semi-parametric additive model,” IEEE Trans. Power Syst, vol. 27, no. 1, pp. 134–141, Feb. 2012, doi: 10.1109/TPWRS.2011.2162082.
  31. H. Yu, D. Liu, G. Chen, B. Wan, S. Wang, and B. Yang, “A neural network ensemble method for precision fertilization modeling,” Math. Comput. Modell., vol. 51, no. 11–12, pp. 1375–1382, Jun. 2010, doi: 10.1016/j.mcm.2009.10.028.
No Related Submission Found