SHORT-TERM ELECTRICITY LOAD FORECASTING SEASONAL PATTERN USING TIME SERIES REGRESSION (TSR) MODEL IN PT.PLN (PERSERO) MEDAN CITY

Feby Mayori  Rambe; Rina Widyasari

doi:10.47709/cnahpc.v7i1.5533

Authors

Feby Mayori Rambe
Rina Widyasari

DOI:

https://doi.org/10.47709/cnahpc.v7i1.5533

Keywords:

Time Series Regression, electricity load, short-term forecasting, seasonal pattern, PLN.

Abstract

Electricity is a crucial component of modern life, where daily consumption fluctuates significantly. Uncertain electricity demand can lead to imbalances between supply and consumption, potentially causing energy wastage or power outages. To address this issue, a forecasting method capable of accurately predicting electricity load is essential. The Time Series Regression (TSR) model is applied for short-term electricity load forecasting by considering daily and weekly seasonal patterns. The forecasting results indicate that Monday and Tuesday have the highest electricity load, while Sunday has the lowest. When the Kolmogorov-Smirnov test is used to analyse the model, the p-value is 0.9608, which shows that the residuals have a normal distribution. The model's accuracy is assessed with a Root Mean Square Error (RMSE) value of 378.0069 MW, which is relatively high for a small dataset. Given the considerable forecasting error, further improvements such as hybrid models are recommended to enhance accuracy. The implementation of these forecasting results can help optimize electricity management and improve power distribution efficiency.

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References

M. Al'afi, W. Widiarti, D. Kurniasari, and M. Usman, "Forecasting Seasonal Time Series Data Using Spectral Analysis Method," Journal of Siger Mathematics, vol. 1, no. 1, pp. 10-15, 2020.

M. Arumsari and A. T. R. Dani, "Forecasting time series data using a hybrid time series regression-autoregressive integrated moving average model," Journal of Siger Mathematics, vol. 2, no. 1, pp. 1-12, 2021.

Aswi and Sukarna, Time Series Analysis. Makassar: Andira Publisher, 2006, p. 21.

L. Bowerman and R. T. O'Connell, Time Series Forecasting: Unified Concepts and Computer Implementation, 2nd ed. Boston: Prindle, Weber & Schmidt, 1987.

G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control. New Jersey: Prentice-Hall, Inc, 1994.

Central Bureau of Statistics of North Sumatra Province, 2022. [Online]. Available: https://sumut.bps.go.id/

J. W. Creswell, Research Design: Qualitative, Quantitative and Mixed Methods Approaches. Yogyakarta: Student Library, 2019.

Furqon, Applied Statistics for Research. Bandung: Alfabeta, 2004.

S. Isnawati, N. A. Salehah, D. D. Prastyo, H. Kuswanto, and M. H. Lee, "Hybrid SSA-TSR-ARIMA for water demand forecasting," International Journal of Advances in Intelligent Informatics, vol. 4, no. 3, 2018.

Khair and H. A. d. S., "Prediction Study of Electric Energy Usage in Sanglepongan Village, Enrekang Regency Using the Moving Average Method," Journal of Electronic Media, vol. 81, no. 2, pp. 63-68, 2021.

M. Law and W. D. Kelton, Simulation, Modeling and Analysis, 3rd ed. New York: McGraw-Hill, 2000.

Muslim, "Export Forecasting with ARIMA-ANFIS Hybrid," Review of Economics & Finance, vol. 1, no. 2, pp. 128-142, 2017.

S. Perdana, "Comparison of time series regression and arimax methods on modeling clothing sales data in boyolali," Journal of Statistics, 2010.

H. Prasetya and F. Lukiastuti, Operations Management. Yogyakarta: Media Pressindo, 2009.

K. Ramadani, S. Wahyuningsih, and M. N. Hayati, "Modeling the Share Price of PT Telekomunikasi Indonesia Tbk Using the Linear TSR Model," EKSPONENSIAL, vol. 13, no. 1, pp. 45-50, 2022.

L. J. Soares and M. C. Medeiros, "Modeling and Forecasting Short-Term Electricity Load: A Comparison of Methods with an application to Brazilian Data," International Journal of Forecasting, vol. 24, pp. 630-644, 2008.

Sugiyono, Qualitative, Quantitative, and R&D Research Methods. Bandung: Alfabeta, 2020.

Y. Triwulan, N. Hariyanto, and S. Anwari, "Short-term Electricity Peak Load Forecasting Using Artificial Neural Network Method," Journal of Reka Elkomika, vol. 1, no. 4, pp. 340-344, 2013.

W. W. S. Wei, Time Series Analysis: Univariate and Multivariate Methods. New York: Pearson Education, Inc. 2006.

S. Wirdyacahya and M. Prastuti, "Forecasting cement demand at PT XYZ using time series regression and ARIMA," ITS Journal of Science and Arts, vol. 11, no. 1, pp. D96-D101, 2022.