Artificial Neural Network Untuk Memprediksi Konsumsi Energi Listrik Pada PT.PLN (persero) UP3 Surabaya Selatan (Artificial Neural Network to Predict Electrical Energy Consumption at PT. PLN (Persero) UP3 South Surabaya)

Pangestu N.S.P, Manda (2022) Artificial Neural Network Untuk Memprediksi Konsumsi Energi Listrik Pada PT.PLN (persero) UP3 Surabaya Selatan (Artificial Neural Network to Predict Electrical Energy Consumption at PT. PLN (Persero) UP3 South Surabaya). Undergraduate thesis, Universitas 17 Agustus Surabaya.

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Abstract

Seiring bertambahnya penduduk dan berbagai faktor yang mempengaruhi kebutuhan listrik tiap tahun yang selalu meningkat, agar penyediaan konsumsi energi listrik dapat terpenuhi maka dilakukan suatu prakiraan ( forecasting ). yang bertujuan untuk mengambil suatu kebijakan yang akan terjadi dimasa mendatang. Pada penelitian ini akan dilakukan peramalan konsumsi energi listrik pada wilayah surabaya pada tahun 2021 – 2026 dengan menggunakan artificial neural network yang menggunakan metode backpropagation. setelah melalui berbagai tahap pelatihan model arsitektur jaringan yang berulang – ulang maka didadaptkan jaringan arsitektur terbaik berupa 6 masukan yang terdiri dari ( jumlah penduduk, jumlah pelanggan berbagai sektor seperti sektor sosial, rumah tangga, industri pemerintah, bisnis dari tahun 2015-2020), dengan 3 neuron pada hidden layer, dan 1 neuron pada output layer yaitu konsumsi energi listrik dalam satuan Gwh tahun 2015 – 2020. Setelah dilakukan prediksi prakiraan kebutuhan konsumsi energi listrik didapatkan nilai MAPE sebesar 1,334% dengan nilai MSE 0,2063 yang merupakan prakiraan tersebut sangat akurat dikarenakan berkisar dibawah (<10%), Sehingga metode ini dapat digunakan untuk memprediksi kebutuhan konsumsi energi listrik dimasa mendatang.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Backpropagation, Mape, Neural Network, Forecasting
Subjects: H Social Sciences > HG Finance
H Social Sciences > HJ Public Finance
Divisions: Fakultas Teknik > Program Studi Teknik Elektro
Depositing User: 1451800073 Manda Pangestu N.S.P
Date Deposited: 30 Sep 2022 07:34
Last Modified: 18 Mar 2024 09:55
URI: http://repository.untag-sby.ac.id/id/eprint/20107

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