Electricity Consumption Forecasting Analysis using Linear Regression Method, DKL 3.2 Method and BaU Scenario

Muhammad Zainal Roisul Amin; Ratna Mustika Yasi; Wahyu Setyo Aji; Bambang Sri Kaloko

Authors

Muhammad Zainal Roisul Amin University of PGRI Banyuwangi
Ratna Mustika Yasi University of PGRI Banyuwangi
Wahyu Setyo Aji University of Jember
Bambang Sri Kaloko University of Jember

Abstract

Accurate long-term electricity demand forecasting is essential for ensuring reliable power system planning and sustainable energy development. Previous studies have generally focused on the application of a single forecasting approach, such as linear regression, or a limited comparison between two methods, resulting in insufficient evaluation of forecasting performance across multiple sectors and forecasting models. This research addresses this gap by conducting a comparative analysis of three forecasting approaches Multiple Linear Regression, Electricity Demand List Method 3.2 (DKL 3.2), and Business as Usual (BaU) for projecting electricity consumption in the ULP Kencong service area during the 2025–2029 period. The novelty of this study lies in the integration and comparison of these three forecasting methods within a single framework, combined with the utilization of customer growth, connected power capacity, and Land and Building Tax (PBB) indicators as forecasting variables. Forecasting simulations were performed using LEAP software and Microsoft Excel, while forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that all sectors are expected to experience continuous growth in electricity consumption, with the industrial sector showing the highest increase. Among the evaluated methods, Multiple Linear Regression demonstrated the best forecasting performance, achieving the lowest MAPE values in three of the four analyzed sectors, namely residential (3.49%), business (6.68%), and social (4.77%) sectors. In contrast, DKL 3.2 produced the highest forecasting errors, particularly in the industrial sector (45.98%), while BaU showed moderate and relatively stable performance. These findings support the claim that Multiple Linear Regression is the most suitable and accurate method for long-term electricity consumption forecasting in the ULP Kencong region, providing a reliable basis for future electricity supply planning and infrastructure development.

References

[1] W. Strielkowski, L. Civín, E. Tarkhanova, M. Tvaronavičienė, and Y. Petrenko, “Renewable Energy in the Sustainable Development of Electrical Power Sector: A Review,” Energies, vol. 14, no. 24, p. 8240, Jan. 2021, doi: 10.3390/en14248240.

[2] Y. Zhang et al., “The Relationship between the Low-Carbon Industrial Model and Human Well-Being: A Case Study of the Electric Power Industry,” Energies, vol. 16, no. 3, p. 1357, Jan. 2023, doi: 10.3390/en16031357.

[3] K. J. Hancock and J. E. Allison, The Oxford Handbook of Energy Politics. Oxford University Press, 2021.

[4] S. Deetman, H. S. de Boer, M. Van Engelenburg, E. van der Voet, and D. P. van Vuuren, “Projected material requirements for the global electricity infrastructure – generation, transmission and storage,” Resour. Conserv. Recycl., vol. 164, p. 105200, Jan. 2021, doi: 10.1016/j.resconrec.2020.105200.

[5] H. Jie, I. Khan, M. Alharthi, M. W. Zafar, and A. Saeed, “Sustainable energy policy, socio-economic development, and ecological footprint: The economic significance of natural resources, population growth, and industrial development,” Util. Policy, vol. 81, p. 101490, Apr. 2023, doi: 10.1016/j.jup.2023.101490.

[6] D. H. Gebremeskel, E. O. Ahlgren, and G. B. Beyene, “Long-term evolution of energy and electricity demand forecasting: The case of Ethiopia,” Energy Strategy Rev., vol. 36, p. 100671, Jul. 2021, doi: 10.1016/j.esr.2021.100671.

[7] S. P. Filippov, V. A. Malakhov, and F. V. Veselov, “Long-Term Energy Demand Forecasting Based on a Systems Analysis,” Therm. Eng., vol. 68, no. 12, pp. 881–894, Dec. 2021, doi: 10.1134/S0040601521120041.

[8] D. Kamani and M. M. Ardehali, “Long-term forecast of electrical energy consumption with considerations for solar and wind energy sources,” Energy, vol. 268, p. 126617, Apr. 2023, doi: 10.1016/j.energy.2023.126617.

[9] H. Zhang et al., “Research on medium- and long-term electricity demand forecasting under climate change,” Energy Rep., vol. 8, pp. 1585–1600, Jul. 2022, doi: 10.1016/j.egyr.2022.02.210.

[10]D. Kong, L. Li, D. Kong, S. Sun, and X. Qian, “Policy Synergy Scenarios for Tokyo’s Passenger Transport and Urban Freight: An Integrated Multi-Model LEAP Assessment,” Energies, vol. 19, no. 2, p. 366, Jan. 2026, doi: 10.3390/en19020366.

[11]C. Parsch, “Land Use Change in Papua, Indonesia – Assessing Deforestation Patterns and Predicting Future Hotspots of Habitat Loss to Inform Spatial Conservation Planning,” doctoralThesis, 2025. doi: 10.53846/goediss-11221.

[12]K. Park, R. Rothfeder, S. Petheram, F. Buaku, R. Ewing, and W. H. Greene, “Linear Regression,” in Basic Quantitative Research Methods for Urban Planners, Routledge, 2020.

[13]K. Stapor, “Linear Regression and Correlation,” in Introduction to Probabilistic and Statistical Methods with Examples in R, K. Stapor, Ed., Cham: Springer International Publishing, 2020, pp. 133–149. doi: 10.1007/978-3-030-45799-0_3.

[14]N. Roustaei, “Application and interpretation of linear-regression analysis,” Med. Hypothesis Discov. Innov. Ophthalmol., vol. 13, no. 3, pp. 151–159, Oct. 2024, doi: 10.51329/mehdiophthal1506.

[15]A. S. Mahajan, “INTEGRATING DATA ANALYTICS AND ECONOMETRICS FOR PREDICTIVE ECONOMIC MODELLING,” Int. J. Appl. Math., vol. 38, no. 2s, pp. 1450–1462, Feb. 2025, doi: 10.12732/ijam.v38i2s.983.

[16]“Theoretical and scientific approaches to the application of econometric methods and modelsin economic research.” Accessed: May 12, 2026. [Online]. Available: https://evnuir.vnu.edu.ua/items/74a09ded-7a5d-407e-89cd-40edb8e47ca3

[17]M. Almazroui and Z. Şen, “Trend Analyses Methodologies in Hydro-meteorological Records,” Earth Syst. Environ., vol. 4, no. 4, pp. 713–738, Dec. 2022, doi: 10.1007/s41748-020-00190-6.

[18]“Time Series Regression: Prediction of Electricity Consumption Based on Number of Consumers at National Electricity Supply Company,” TEM J., vol. 12, no. 3, pp. 1575–1581, 2023.

[19]A. P. Taruna, G. Arisona, D. Irwanto, A. B. Bestari, and W. Juniawan, “Electricity Theft Detection Using Machine Learning in Traditional Meter Postpaid Residential Customers: A Case Study on State Electricity Company (PLN) Indonesia,” IEEE Access, vol. 13, pp. 7167–7191, 2025, doi: 10.1109/ACCESS.2025.3526764.

[20]T. W. Adi, “Influence of fuel price, electricity price, fuel consumption on operating cost, generation and operating income: a case study on PLN,” Int. J. Energy Sect. Manag., vol. 17, no. 2, pp. 227–250, Apr. 2022, doi: 10.1108/IJESM-07-2021-0008.

[21]B. Williams, D. Bishop, P. Gallardo, and J. G. Chase, “Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations,” Energies, vol. 16, no. 13, p. 5155, Jan. 2023, doi: 10.3390/en16135155.

[22]K. Misiurek, T. Olkuski, and J. Zyśk, “Review of Methods and Models for Forecasting Electricity Consumption,” Energies, vol. 18, no. 15, p. 4032, Jan. 2025, doi: 10.3390/en18154032.

[23]R. V. Klyuev et al., “Methods of Forecasting Electric Energy Consumption: A Literature Review,” Energies, vol. 15, no. 23, p. 8919, Jan. 2022, doi: 10.3390/en15238919.

[24]A. H. Asfaw, G. Teklu, A. Birhan, Y. Eshetu, E. Regasa, and T. Wondimu, “Mathematical modeling of Ethiopia’s energy demand by sectors and energy types, with forecasts for the next 30 years,” Heliyon, vol. 10, no. 22, Nov. 2024, doi: 10.1016/j.heliyon.2024.e40185.

[25]H. Brugger, W. Eichhammer, N. Mikova, and E. Dönitz, “Energy Efficiency Vision 2050: How will new societal trends influence future energy demand in the European countries?,” Energy Policy, vol. 152, p. 112216, May 2021, doi: 10.1016/j.enpol.2021.112216.

[26]O. A. Nnene, D. Senshaw, M. H. P. Zuidgeest, T. Hamza, S. Grafakos, and B. Oberholzer, “Baseline scenario modelling for low emissions development in Ethiopia’s energy sector,” Energy Strategy Rev., vol. 49, p. 101166, Sep. 2023, doi: 10.1016/j.esr.2023.101166.

[27]“Historical Variation of IEA Energy and CO2 Emission Projections: Implications for Future Energy Modeling.” Accessed: May 12, 2026. [Online]. Available: https://www.mdpi.com/2071-1050/13/13/7432

[28]R. M. Gozali, S. Prasetyono, and M. Ferent Hasnitha, “Proyeksi Kebutuhan Energi Listrik dengan Metode Regresi Linear Berganda di UP3 Mojokerto Tahun 2022 sampai 2027,” JASEE J. Appl. Sci. Electr. Eng., vol. 4, no. 2, pp. 22–32, 2023, doi: 10.31328/jasee.