Peramalan Beban Listrik Kabupaten Pesisir Selatan Dengan Analisis Regresi

Zulka Hendri; Efendi Efendi; Junaidi Asrul; Fitriadi Fitriadi

doi:10.30630/eji.15.1.340

Zulka Hendri Politeknik Negeri Padang
Efendi Efendi Politeknik Negeri Padang
Junaidi Asrul Politeknik Negeri Padang
Fitriadi Fitriadi Politeknik Negeri Padang

DOI: https://doi.org/10.30630/eji.15.1.340

Keywords: Multiple Linear Regression Analysis, R2, F Test, T Test and MAPE.

Abstract

The more electric vehicles emerge, the more electricity demand will increase in each region. This will encourage electricity providers to increase the number or capacity of generators. The construction of a new power plant requires load forecasting to determine how much capacity the plant will build. This study aims to predict the electrical load in Pesisir Selatan, West Sumatra until 2031 using linear regression analysis and time series. Forecasting is done on each sector of PLN customers. Forecasting is done based on the PLN customer sector. The forecasting sectors are the household, business, social and government sectors. The four test criteria were carried out are namely the coefficient of determination test (R2), the F test, the T test and the mean absolute percentage error (MAPE). The forecasting results show that in 2031 the electricity load for the household sector is 120.1 MW, the business sector is 5.7 MW, the social sector is 56.9 MW and the government is 9.5 MW.

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References

[1] M Apel, Isolated Area Load Forcasting Using Linear Regression Analysis: Practical Approach. Energy and Power Engineering, 2011, 3, 547-550
[2] A. D. Papalxopoulos and T. C. Hiterbeg, “A Regression-Based Approach to Short-Term Load Forecasting,” IEEE Transactions on Power Systems, Vol. 5, No. 4, 1990, pp.1535-1547. doi:10.1109/59.99410
[3] D. Park, M. Al-Sharkawi, R. Marks, A. Atlas and M. Damborg, “Electric Load Forecasting Using an Artificial Neural Network,” IEEE Transactions on Power Systems, Vol. 6, No. 2, 1991, pp. 442-449. doi:10.1109/59.76685
[4] G. Gross and F. D. Galianan, “Short-Term Load Fore-casting,” Proceedings of the IEEE, Vol. 75, No. 12, 1987, pp. 1558-1572. doi:10.1109/PROC.1987.13927
[5] A. D. Papalxopoulos and T. C. Hiterbeg, “A Regression- Based Approach to Short-Term Load Forecasting,” IEEE Transactions on Power Systems, Vol. 5, No. 4, 1990, pp. 1535-1547. doi:10.1109/59.99410
[6] G. T. Heinemann, D. A. Nordman and E. C. Plant, “The Relationship between Summer Weather and Summer Loads,” IEEE Transactions on Power Apparatus and Sys-tems, Vol. PAS-85, No. 11, 1966, pp. 1144-1154. doi:10.1109/TPAS.1966.291535
[7] J. Gonçalves, Â. P. Ferreira and P. Odete. (2019, May 28) A linear regression pattern for electricity price forecasting in the iberian electricity market, Revista Facultad de Ingeniería Universidad de Antioquia. [Online]. Available: https://www.doi.org/10.17533/udea.redin.20190522
[8] Reddy, M. Vishali, N. 2017/05/18 "Load Forecasting using Linear Regression Analysis in Time series model for RGUKT, R.K. Valley Campus HT Feeder" V6 International Journal of Engineering Research http://dx.doi.org/10.17577/IJERTV6IS050443
[9] Alita,Debby., Dewi P, Ade., Darmis, D. 2021."Analysis of Classic assumption test and multiple linear regression coefficient test for employee structural office recommendation"IJCCS (Indonesian Journal of Computing and Cybernetics Systems);Vol.15, No.3, July 2021, pp. 295~306 ; https://doi.org/10.22146/ijccs.65586
[10] D. A. Nani, M. T. K. Handayani, And V. A. D. Safitri, “Fraud Dalam Proses Akademik Pada Perilaku Mahasiswa,” Jaf-Journal Account. Financ., Vol. 5, No. 1, Pp. 11–20, 2021.
[11] Michelangelo, 2015. "Multiple Regression" Essential Statistics, Regression, and Econometrics.Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/B978-0-12-803459-0.00010-8
[12] H. White, “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity,” Econometrica, 1980.
[13] Bianco V, Manca O, Nardini S. Electricity consumption forecasting in Italy using linear regression models. Energy 2009;34(9):1413 e 21 .
[14] Mohamed Z, Bodger P. Forecasting electricity consumption in New Zealand using economic and demographic variables. Energy 2005;30(10):1833 e 43 .
[15] Azadeh A, Ghaderi SF, Tarverdian S, Saberi M. Integration of artiﬁcial neural networks and genetic algorithm to predict electrical energy consumption. Appl Math Comput 2007;186(2):1731 e41 .
[16] F.J. Ardakani, M.M. Ardehali, Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types,Energy,Volume 65,2014,Pages 452-461,ISSN 0360-5442,
[17] H. Daneshi, M. Shahidehpour and A. L. Choobbari, "Long-term load forecasting in electricity market," 2008 IEEE International Conference on Electro/Information Technology, Ames, IA, USA, 2008, pp. 395-400, doi: 10.1109/EIT.2008.4554335.
[18] S. Rahman and R. Bhatnagar, "An expert system based algorithm for short term load forecast," in IEEE Transactions on Power Systems, vol. 3, no. 2, pp. 392-399, May 1988, doi: 10.1109/59.192889.
[19] Zurada, J., Levitan, A. S., & Guan, J. (2011). A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context. The Journal of Real Estate Research, 33(3), 349–388. http://www.jstor.org/stable/24888380
[20] Al-Hamadi, H. M., & Soliman, S. A. (2005). Long-term/mid-term electric load forecasting based on short-term correlation and annual growth. Electric Power Systems Research, 74(3), 353–361. doi:10.1016/j.epsr.2004.10.015
[21] Makridakis, S., Wheelright, S.C., & McGee, V. E. (1999). Metode dan Aplikasi Peramalan, (U.S.Andriyanto dan A. Basith, terj.). Jakarta: Erlangga.
[22] 2007-2019. Kabupaten Pesisir Selatan Dalam Angka, Pesisir Selatan Regency in Figures. Badan Pusat Statistik Pesisir Selatan.