Prediksi Kebutuhan Energi Listrik Menggunakan Metode Jaringan Syaraf Tiruan
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Joliando Pulungan
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Rice Novita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Government owned company of electricity play an important role in the distribution of electrical energy services. The Bagan Batu Auxiliary Service Unit (ULP) is one of the ULPs that plays an important role in the distribution of electrical energy in the Bagan Batu area. Along with the increase in the number of customers every year, the problem of demand for electrical energy changes and increases every year. To predict short-term electrical energy needs, this study uses the Backpropagation Artificial Neural Network method with the help of the MATLAB R2015B tool. The research data for training and network testing uses the history of energy sold (kWh) for the last 10 years with other variables consisting of household customers, business, social, industrial, population growth, Gross Regional Domestic Product (GRDP). The results of the research produce predictions of electrical energy for the next 3 years from 2022 to 2024. This research produces the best architectural model 6-6-1 with the smallest MSE error of 0.003312731 and produces Mean Absolute Percentage Error (MAPE) value of 6%. The research implies benefits for stakeholders to take action on the provision of electrical energy
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References
U. Situmeang, “Prakiraan Kebutuhan Energi Listrik Di Kelurahan,” SainETIn (Jurnal Sain, Energi, Teknol. Ind., vol. 3, no. 1, pp. 25–32, 2018.
T. Handayani, Atmam, and M. Putra Halilintar, “Studi Perkiraan Kebutuhan Energi Listrik Di Kota Dumai Sampai Tahun 2025 Dengan Metoda Fuzzy Logic,” SainETIn (Jurnal Sain, Energi, Teknol. Ind., vol. 3, no. 2, pp. 42–49, 2019, doi: 10.31849/sainetin.v3i2.3038.
P. Mangera, “Perkiraan Kebutuhan Energi Listrik Jangka Panjang Pada Pt. Pln (Persero) Wilayah Papua Dan Papua Barat Area Merauke Dengan Menggunakan Metode Regresi Linier,” Mustek Anim Ha, vol. 7, no. 3, pp. 247–256, 2018, doi: 10.35724/mustek.v7i3.1736.
S. Bandri, “Prediksi Perkembangan Kebutuhan Energi Listrik di Unit PLN Kayu Aro,” Menara Ilmu, vol. XIII, no. 6, pp. 187–205, 2019.
Y. T. Nugraha, K. Ghabriel, and I. F. Dharmawan, “Implementasi ANFIS Dalam Prakiraan Konsumsi Energi Listrik Di Kota Medan Pada Tahun 2030,” RELE (Rekayasa Elektr. dan Energi) J. Tek. Elektro, vol. 4, no. 1, pp. 55–59, 2021.
Y. Hakimah, “Analisis Kebutuhan Energi Listrik dan Prediksi Penambahan Pembangkit Listrik di Sumatera Selatan,” J. Desiminasi Teknol., vol. 7, no. 2, pp. 130–137, 2019.
S. Syahputri, S. Sinurat, and I. Saputra, “Prediksi Kebutuhan Energi Listrik Pada PT. PLN (Persero) Rayon Aek Nabara Dengan Metode Exponential Smoothing,” J. Informatics, vol. 1, no. 1, pp. 1–9, 2021.
P. H. T. Hutabarat, M. F. Zambak, and Suwarno, “Prediksi Kebutuhan Energi Listrik Wilayah PLN Kota Parapat Simalungun Sampai Tahun 2024,” J. Electr. Syst. Control Eng., vol. 5, no. 2, pp. 53–58, 2022.
Purwoharjono, “Penerapan Metode Jaringan Syaraf Tiruan Untuk Prediksi Kebutuhan Beban Listrik,” ALINIER J. Artif. Intell. Appl., vol. 2, no. 1, pp. 36–42, 2021, doi: 10.36040/alinier.v2i1.3566.
F. Tawakal, “Prediksi Masa Studi Mahasiswa Menggunakan Metode Backpropagation (Studi Kasus : Teknik Informatika Uin Sultan Syarif Kasim Riau),” Manaj. dan Teknol. Inf., vol. 9, no. 1, pp. 35–41, 2018.
F. S. Harahap, “Analisa Pengujian Prakiraan Kebutuhan Energi Listrik Wilayah Provinsi Sumatera Utara Menggunakan Metode Backpropagation Neural Network,” Universitas Sumatera Utara, Medan, 2020.
U. Khasanah and N. Ulinnuha, “Prediksi Biaya Konsumsi Bahan Bakar Gas Menggunakan Metode Backpropagation Neural Network (Studi Kasus: PLTU PT. Pembangkit Jawa Bali Unit Pembangkitan Gresik),” J. Sains Mat. dan Stat., vol. 5, no. 2, pp. 9–17, 2019.
M. N. Fadilah, A. Yusuf, and N. Huda, “Prediksi Beban Listrik Di Kota Banjarbaru Menggunakan Jaringan Syaraf Tiruan Backpropagation,” Mat. Murni Dan Terap., vol. 14, no. 2, pp. 81–92, 2020.
Y. A. Suryatna, “Peramalan Beban Puncak Menggunakan Metode Feed Forward Backpropagation dan Generalized Regression Neural Network,” J. Tek. Elektro, vol. 10, no. 01, pp. 109–118, 2021.
J. Permadi, H. Rhomadhona, and W. Aprianti, “Perbandingan K-Nearest Neighbor Dan Backpropagation Neural Network Dalam Prediksi Resiko Diabetes Tahap Awal,” Kumpul. J. Ilmu Komput., vol. 08, no. 3, pp. 352–365, 2021.
M. A. H. A. Y. Tiani Wahyu Utami, “Perbandingan Metode Backpropagation Neural Network Dan Fuzzy Wavelet Untuk Prediksi Kurs Dolar Terhadap Rupiah,” Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muhammadiyah Semarang, 2019.
B. W. N. Tantyo and D. Swanjaya, “Perbandingan antara Metode Holt-Winters dan Backpropagation pada Model Peramalan Penjualan,” Pros. SEMNAS INOTEK (Seminar Nas. Inov. Teknol., vol. 5, no. 3, pp. 174–181, 2021.
A. H. Wijaya, “Artificial Neural Network Untuk Memprediksi Beban Listrik Dengan Menggunakan Metode Backpropagation,” J. CoreIT, vol. 5, no. 2, pp. 61–70, 2019.
I. C. Saragih, D. Hartama, and A. Wanto, “Prediksi Perkembangan Jumlah Pelanggan Listrik Menurut Pelanggan Area Menggunakan Algoritma Backpropagation,” vol. 2, no. 1, pp. 48–54, 2020.
A. Hasibuan and W. V. Siregar, “Prakiraan Kebutuhan Energi Listrik Kota Subulussalam Sampai Tahun 2020 Menggunakan Metode Analisis Regresi,” RELE (Rekayasa Elektr. dan Energi) J. Tek. Elektro, vol. 1, no. 2, pp. 57–61, 2019, doi: 10.30596/rele.v1i2.3013.
A. C. Koloay et al., “Perencanaan Dan Pemenuhan Kebutuhan Energi Listrik Di Kota Bitung,” J. Tek. Elektro dan Komput., vol. 7, no. 3, pp. 285–294, 2018, doi: 10.35793/jtek.7.3.2018.22504.
N. F. Hasan, K. Kusrini, and H. Al Fatta, “Analisis Arsitektur Jaringan Syaraf Tiruan Untuk Peramalan Penjualan Air Minum Dalam Kemasan,” J. Rekayasa Teknol. Inf., vol. 3, no. 1, p. 1, 2019, doi: 10.30872/jurti.v3i1.2290.
A. Rifais, “Prediksi Konsumsi Energi Listrik Menggunakan Metode Jaringan Syaraf Tiruan Recurrent di PLN APJ Salatiga,” Fakultas Teknik, Universitas Negeri Semarang, Semarang, 2018.
M. W. Purnama, “Peramalan Kebutuhan Energi Listrik UID Jawa Timur Metode Time Series Berbasis Minitab v19,” J. Tek. Elektro, vol. Volume 10, pp. 485–495, 2021.
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