Peramalan Jumlah Pelanggan PLN Menggunakan Simple Linear Regression

  • Yayang Eluis Bali Mawartika STMIK Bina Nusantara Jaya Lubuklinggau
  • Hendra Di Kesuma Universitas IGM Palembang
Keywords: Forecasting, PLN Costumers, Simple Linear Regression

Abstract

The rate of population growth in Lubuklinggau City continues to increase every year, this is a factor in the increasing demand for electricity. The development of the number of PLN customers in Lubuklinggau City in 2017 was 90.606 customers, increasing to 120.809  customers in 2021. PLN must be able to predict the number of electricity customers in the future in order to provide electricity needs. One of the forecasting models that can be used to predict the development of the number of PLN electricity customers is the Simple Linear Regression model. Simple Linear Regression is a forecasting model based on the effect of the causal variables on the effect variables. The variables used are the population variable and the number of PLN customers. From these two variables, the process of analyzing the influence of population on the number of PLN customers in the future will be carried out. The results showed that 90% increase in population affects the number of PLN customers. With the prediction of the development of the number of customers, PLN Lubuklinggau can prepare the necessary facilities and can estimate how much additional power and electrical energy will be in meeting customer needs in the future.

References

[1] B. P. S. K. Lubuklinggau, Kota Lubuklinggau Dalam Angka: Lubuklinggau Municipality in Figures 2018. Lubuklinggau: Badan Pusat Statistik Kota Lubuklinggau, 2018.
[2] B. P. S. K. Lubuklinggau, Kota Lubuklinggau Dalam Angka: Lubuklinggau Municipality in Figures 2022. Lubuklinggau: Badan Pusat Statistik Kota Lubuklinggau, 2022.
[3] D. Rachmawati and B. Sutijo, “Pemodelan Konsumsi Listrik Berdasarkan Jumlah Pelanggan PLN Jawa Timur untuk Kategori Rumah Tangga R-1 dengan Metode,” vol. 2, no. 2, 2013.
[4] S.-S. BPS, “Sumatera Selatan Province in Figures 2022,” 2022.
[5] Y. E. B. Mawartika, A. SN, and A. Sihabuddin, “TOPSIS and SLR methods on the Decision Support System for Selection the Management Strategies of Funeral Land,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 2, p. 169, 2019.
[6] Rohmad and Supriyanto, Pengantar Statistika Panduan Praktis Bagi Pengajar dan Mahasiswa. Yogyakarta: Kalimedia, 2015.
[7] M. A. Awal, J. Rabbi, S. I. Hossain, and M. M. A. Hashem, “Using linear regression to forecast future trends in crime of Bangladesh,” 2016 5th Int. Conf. Informatics, Electron. Vision, ICIEV 2016, pp. 333–338, 2016.
[8] D. Saini, A. Saxena, and R. . Bansal, “Electricity Price Forecasting by Averaging Dynamic,” IEEE Int. Conf. Recent Adv. Innov. Eng., pp. 1–21, 2016.
[9] N. L. P. Wulandari, N. L. A. K. Y. Sarja, and I. G. A. D. Saryanti, “Prediksi Jumlah Pelanggan dan Persediaan Barang Menggunakan Metode Regresi Linier Berganda Pada Bali Orchid,” Eksplora Inform., pp. 1–12.
[10] G. N. Ayuni and D. Fitrianah, “Penerapan Metode Regresi Linear Untuk Prediksi Penjualan Properti pada PT XYZ,” vol. 14, no. 2, pp. 79–86, 2018.
[11] P. Subagyo, Forecasting Konsep dan Aplikasi. Yogyakarta: BPFE, 2013.
[12] Y. E. . Mawartika, “Implementasi Metode Case Based Reasoning untuk Mendiagnosa Penyakit Lambung Implementation of Case Based Reasoning Method for Diagnosing Gastric Disease,” J. Ilm. Bin. STMIK Bina Nusant. Jaya, vol. 3, no. 02, pp. 39–46, 2021.
[13] Sugiyono, Metode Penelitian Kuantitatif Kualitatif dan R&D. Bandung: Alfabeta, 2017.
[14] BPS Statistics Indonesia, Proyeksi Penduduk Indonesia (Indonesia Population Projection) 2015-2045 Hasil SUPAS 2015 (Resulr of SUPAS 2015). 2015.
Published
2022-05-10