IMPLEMENTATION OF LEAST SQUARE INTEGRATED SYSTEM IN FORMING THE REQUIREMENT OF ELECTRICITY REQUIREMENTS IN THE LHOKSEUMAWE CITY
Abstract
PLN (Perusahaan Listrik Negara) is one of the BUMN (Badan Usaha Milik Negara) in charge of providing electricity supply needs with the best service quality for the community. The growth of electricity demand for residents of Lhokseumawe City by 2020 is expected to increase to 10%. Based on these predictions, PLN must be able to forecast patterns from data needs lsitrik which has been then used to project the data that will come in order to give the best pelyanan for the community. Forecasting is one of the sciences in the field of intelligent systems that can predict long-term electrical demand in the city of Lhokseumawe. The least square forecasting model can be one of the components supporting the economic growth of Lhokseumawe City. Variables to be predicted in the fulfillment of electricity stocks are seen from household, industrial, commercial and public expenses. Furthermore, the need for electricity stock from each region will be seen from the installed capacity, power capable (MW) and peak load. Then the least quare forecasting model will determine the equation of data trend based on the data needs of electricity that has been then used to project the data needs of electricity to come.
This research is expected to produce accurate forecasting value so that can be used as a reference for PLN party in taking policy. Proper forecasting can help PLN to save production cost due to mis-distribution. The specific target of this research is to know the quality of service PLN Lhokseumawe to the public so that the distribution of electricity is always stable. The long-term goal of implementing this smart forecasting system is to help improve the quality of PLN's services in meeting the long-term electricity needs of the people of Lhokseumawe.
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References
[2] Mohammad, A. (2009). Pengaruh Kualitas Pelayanan Terhadap Kepuasan Pelanggan (Studi Pada Perusahaan Penerbangan PT. Garuda Di Kota Semarang). Jurnal Ekonomi dan Bisnis. Vol. 10. No.2. Juli. Hal. 171 186. Unisulla Semarang.
[3] Suswanto, D. (2009). Sistem Distribusi Tenaga Listrik Edisi Pertama. Buku Ajar, Universitas Negeri Padang, Padang.
[4] Marsudi, D. (2010). Operasi sistem Tenaga Listrik. Graha Ilmu, Yogyakarta.
[5] Firdaus, I. (2010). Analisa Prakiraan Beban Puncak Dengan Menggunakan Metode Dekomposisi Pada Gardu Induk Banda Aceh. Tugas Akhir Fakultas Teknik Unsyiah, Banda Aceh.
[6] Ginting, R. (2007). Sistem Produksi. Graha Ilmu, Yogyakarta.
[7] Purnamasari, I. dan Suhartono. (2012). Metode tlsar berbasis regresi time series dan moving average untuk peramalan beban listrik jangka pendek. Prosiding Seminar Nasional Penelitian, Pendidikan dan Penerapan MIPA, Fakultas MIPA,
Universitas Negeri Yogyakarta, Yogyakarta.
[8] Supranto, J. (2001). Statistik Teori dan Aplikasi Jilid 2 Ed.6. Penerbit Erlangga, Jakarta.
[9] Rambe, M. I. F. (2014). Perancangan Aplikasi Peramalan Persediaan Obat-obatan Menggunakan Metode Least Square (Studi Kasus : Apotik Mutiara Hati). Pelita Informatika Budi Darma, Volume : VI, Nomor: 1, Maret 2014 ISSN : 2301-9425.
[10] Nugroho, A. (2011). Perancangan dan Implementasi Sistem Basis Data. Penerbit Andi, Yogyakarta.
[11] Syahrizal, M. dkk. (2008). Peramalan Kebutuhan Beban Sistem Tenaga Listrik Menggunakan Algoritma Genetik. UIN Sultan Syarif Kasim, Riau.
[12] Simarta, J. dan Paryudi. I. (2010). Basis Data. Penerbit Andi, Yogyakarta.
[13] Soares, L. J., and Medeiros, M. C. (2008). Modeling and Forecasting short-term electricity load: A comparison of methods with an application to Brazilian data. International Journal of Forecasting, 24, 630-644.
[14] Sonya, M. (2010). Analisis Pengaruh Kualitas Pelayanan Terhadap Kepuasan Pelanggan Dalam Pembayaran Rekening Listrik (Studi Pada Unit Pelayanan Pelanggan Semarang Barat). Universitas Diponegoro.
[15] Utama, N. P. S. (2007). Prakiraan Kebutuhan Tenaga Listrik Propinsi Bali Sampai Tahun 2018. Teknik Elektro, Vol.6 No.1.
[16] Varga, (2010). Medium Term Electric Load forecasting Using Artificial Neural Networks. Electric Power Engineering. 2010 PowerTech Budapest 99. International Conference on. IEEE Conference Publications. hlm. 37, DOI:
10.1109/PTC.1999.826468, Teknik Analisis Regresi dan Korelasi.
[17] Wei, W., (2008). Time Analysis Univariate and Multivariate Methods. Addison Wesley Publishing Company, Inc. America.
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