PERAMALAN KONSUMSI LISTRIK DI SUMATERA UTARA: ANALISIS KOMPARATIF METODE HOLT'S LINEAR TREND DAN METODE HEURISTIK TRADISIONAL
DOI:
https://doi.org/10.70585/jumali.v3i1.202Keywords:
Electricity Forecasting; Holt's Linear Trend; Moving Average; North Sumatra; Time SeriesAbstract
Accurate electricity consumption forecasting is crucial for infrastructure planning and grid stability, particularly in regions with rapid demand growth such as North Sumatra, Indonesia, where the residential sector accounts for over 93% of total consumption. This study conducted a comparative analysis between Holt's Linear Trend method and traditional heuristic approaches of Moving Average (MA) and Weighted Moving Average (WMA) using a simulated monthly dataset spanning 60 months with a linear growth rate of 1.5 GWh/month, a 12-month seasonal cycle with an amplitude of 25 GWh, and Gaussian noise. Holt's method outperformed MA and WMA with a Mean Absolute Percentage Error (MAPE) of 1.10%, compared to 3.68% and 3.23%, respectively. These findings provide actionable insights for energy planners and policymakers to improve the efficiency of resource allocation and grid management in North Sumatra.
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References
Abd Jalil, N. A., Ahmad, M. H., & Mohamed, N. (2013). Electricity load demand forecasting using exponential smoothing methods. Jurnal Teknologi, 64(1). https://doi.org/10.5829/idosi.wasj.2013.22.11.2891
Amjady, N. (2002). Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Transactions on Power Systems, 17(4), 1–8. https://doi.org/10.1109/59.932287
Andani, I. W. S., Sugiyono, A., et al. (2021). Decarbonizing the electricity system in Sumatra region using nuclear and renewable energy based power generation. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/753/1/012011
Aurna, N. F., Rubel, M. T. M., Siddiqui, T. A., et al. (2021). Time series analysis of electric energy consumption using autoregressive integrated moving average model and Holt Winters model. Telkomnika Indonesian Journal of Electrical Engineering, 19(4). https://doi.org/10.12928/telkomnika.v19i3.15303
Batih, H., & Sorapipatana, C. (2016). Characteristics of urban households' electrical energy consumption in Indonesia and its saving potentials. Renewable and Sustainable Energy Reviews, 57, 1160–1173. https://doi.org/10.1016/j.rser.2015.12.132
Chávez, H., & Molina, Y. (2025). Geospatial forecasting of electric energy in distribution systems using segmentation and machine learning with convolutional methods. Energies, 18(3). https://doi.org/10.3390/en18020424
Fildes, R., & Petropoulos, F. (2015). Simple versus complex selection rules for forecasting many time series. Journal of Business Research, 68(8), 1692–1701. https://doi.org/10.1016/j.jbusres.2015.03.028
Gardner, E. S. Jr. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1–28. https://doi.org/10.1016/j.ijforecast.2006.03.005
Hansun, S. (2013). A new approach of moving average method in time series analysis. In 2013 Conference on New Media Studies (CoNMedia). IEEE. https://doi.org/10.1109/CoNMedia.2013.6708545
Hasibuan, L. H., Musthofa, S., et al. (2023). Comparison of seasonal time series forecasting using Sarima and Holt Winter's exponential smoothing: Case study West Sumatra export data. Jurnal Statistika, 11(1). https://doi.org/10.30598/barekengvol17iss3pp1773-1784
Hirose, K., Wada, K., Hori, M., & Taniguchi, R. (2020). Event effects estimation on electricity demand forecasting. Energies, 13(20). https://doi.org/10.3390/en13215839
Ishak, I., Othman, N. S., & Harun, N. H. (2022). Forecasting electricity consumption of Malaysia's residential sector: Evidence from an exponential smoothing model. F1000Research, 11. https://doi.org/10.12688/f1000research.74877.1
Jabir, H. J., Teh, J., Ishak, D., & Abunima, H. (2018). Impacts of demand-side management on electrical power systems: A review. Energies, 11(5), 1050. https://doi.org/10.3390/en11051050
Jónsson, T., Pinson, P., Nielsen, H. A., & Madsen, H. (2014). Exponential smoothing approaches for prediction in real-time electricity markets. Energies, 7(6), 3710–3732. https://doi.org/10.3390/en7063710
Liu, C., Sun, B., Zhang, C., & Li, F. (2020). A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine. Applied Energy, 275. https://doi.org/10.1016/j.apenergy.2020.115383
Matushkin, D., Zaporozhets, A., Babak, V., Kulyk, M., et al. (2025). Hourly photovoltaic power forecasting using exponential smoothing: A comparative study based on operational data. Solar, 5(1). https://doi.org/10.3390/solar5040048
McNeil, M. A., Karali, N., & Letschert, V. (2019). Forecasting Indonesia's electricity load through 2030 and peak demand reductions from appliance and lighting efficiency. Energy for Sustainable Development, 49, 65–77. https://doi.org/10.1016/j.esd.2019.01.001
Mynhoff, P. A., Mocanu, E., & Gibescu, M. (2018). Statistical learning versus deep learning: performance comparison for building energy prediction methods. In IEEE PES Innovative Smart Grid Technologies Conference.
Putra, F. A., Reyseliani, N., et al. (2025). Pathway to achieve 100% renewable electricity by 2060 in North Sumatra. 2nd International Conference on Renewable Energy and Sustainable Development. https://doi.org/10.1109/NETPS65392.2025.11102076
Taylor, J. W., & McSharry, P. E. (2007). Short-term load forecasting methods: An evaluation based on European data. IEEE Transactions on Power Systems, 22(4), 2213–2219. https://doi.org/10.1109/TPWRS.2007.907583
Tumiran, T., Sarjiya, S., Putranto, L. M., et al. (2021). Long-term electricity demand forecast using multivariate regression and end-use method: A study case of Maluku-Papua electricity system. In International Conference on Technology and Engineering. https://doi.org/10.1109/ICT-PEP53949.2021.9601144
Waite, M., Cohen, E., Torbey, H., Piccirilli, M., Tian, Y., & Modi, V. (2017). Global trends in urban electricity demands for cooling and heating. Energy, 127, 786–802. https://doi.org/10.1016/j.energy.2017.03.095
Wheelwright, S., Makridakis, S., & Hyndman, R. J. (1998). Forecasting: Methods and applications (3rd ed.). Wiley.
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