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Liberty Tarigan
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Automotive Engineering Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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ANALISIS KEBUTUHAN ENERGI LISTRIK DENGAN JARINGAN SYARAF TIRUAN Tritiya A.R. Arungpadang; Febry A. Hontong; Liberty Tarigan
Jurnal Tekno Mesin Vol. 4 No. 2 (2018)
Publisher : Fakultas Teknik Jurusan Teknik Mesin Universitas Sam Ratulangi

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Energi listrik merupakan suatu kebutuhan dasar dan memegang peranan penting untuk kehidupanmasyarakat, karena berbagai peralatan elektronik di rumah, kantor, dan pabrik membutuhkan listrik sebagaisumber energi. Konsumsi listrik meningkat sejalan dengan bertambahnya jumlah pelanggan dan besarnyakonsumsi energi listrik yang digunakan. Kebutuhan konsumsi listrik pada periode mendatang perlu diprediksidengan suatu model forecasting yang sesuai. Penelitian ini bertujuan untuk memprediksi kebutuhan energi listrikyang diharapkan dapat dijadikan masukan dalam melakukan perencanaan pembangunan pembangkit tenagalistrik. Dengan menggunakan metode jaringan syaraf tiruan dan analisis gap terhadap total kapasitas pembangkityang ada, dapat diperoleh gambaran ketersediaan energi listrik untuk beberapa tahun ke depan.Kata Kunci : kebutuhan energi listrik, artificial neural network, analisis gap
ESTIMASI BEBAN PUNCAK ENERGI LISTRIK PADA SISTEM SULUTGO MENGGUNAKAN ARTIFICIAL NEURAL NETWORK DAN METODE MOVING AVERAGE Liberty Tarigan; Tritiya Arungpadang; Johan S C Neyland
JURNAL POROS TEKNIK MESIN UNSRAT Vol. 5 No. 2 (2016): Jurnal Poros Teknik Mesin Unsrat
Publisher : Fakultas Teknik Jurusan Teknik Mesin Universitas Sam Ratulangi

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Sulutgo interconnection system is the electrical energy suppliers for North Sulawesi and Gorontalo. Their role as the electrical energy supplier was complained by people in 2015, due to lack of electricity supply that lead to continuous rolling blackouts. Accordingly, it is important to identify the electrical peak load in Sulutgo system, so that the electrical necessity of the people can be properly fulfilled. The electrical peak load in the next 12 month is estimated using the backpropagation method artificial neural network and forecasting method moving average. The estimation was performed by using the last 24 month peak load data. Based on the results of both estimation, it is found the backpropagation method artificial neural network has fluctuated results while the forecasting method moving average gives stable results. The results of the estimation of peak load electricity using bacpropagation  artificial neural network method for the next 12 month starting from July 2016 to June 2017 are 327.48, 353.99, 316.32, 316.66, 332.37, 329.79, 332.31, 356.21, 318.60, 349.56, 351.37, 362.04 MW. While the results of the estimation method using moving average forecasting for the same period are 325.68, 326.03, 326.39, 326.72, 327.25, 328.09, 327.94, 328.72, 329.94, 330.32, 327.65, 326.52 MW.   Keywords: Estimation, Artificial Neural Network, Forecast Method Moving Average
Co-Authors Febry A. Hontong Johan S C Neyland Tritiya A.R. Arungpadang Tritiya Arungpadang