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ITS » Undergraduate Theses » Teknologi Informasi - D4
Posted by dee@its.ac.id at 26/10/2010 13:32:21  •  2443 Views


PERAMALAN BEBAN LISTRIK MENGGUNAKAN JARINGAN SARAF TIRUAN METODE KOHONEN

ELECTRICAL LOAD FORECASTING USING ARTIFICIAL NEURAL NETWORK KOHONEN METHOD

Author :
SYETO, GALANG JIWO  ( 7406040058 )




ABSTRAK

Mengkonsumsi daya listrik mempunyai peranan penting dalam pelaksanaan pembangunan untuk peningkatan kesejahteraan dan kegiatan ekonomi. Sehingga diperlukan peramalan beban listrik untuk menyelenggakan usaha penyediaan daya listrik dalam jumlah merata. Jumlah konsumsi daya listrik oleh masyarakat dalam satuan kWh sangat mempengaruhi perhitungan penyediaaan daya listrik. Tujuan dari peramalan beban listrik tersebut adalah untuk melakukan evaluasi kebijakan penyediaan listrik pada masa yang akan datang. Tujuan dari pembuatan tugas akhir ini adalah membuat suatu perangkat lunak yang dapat memprediksi konsumsi daya listrik menggunakan Jaringan Saraf Tiruan metode kohonen dan membandingkan tingkat keakuratan hasil peramalan yang dihasilkan oleh penggabungan metode backpropagation dengan kohonen dan counterpropagation dengan kohonen. Metode Kohonen sendiri dipilih untuk menyelesaikan peramalan beban listrik ini yang merupakan jaringan kompetisi dengan pelatihannya tanpa supervisi unsupervised competitive learning yang dapat secara langsung memproses tipe data musiman tanpa ada preprocessing terlebih dahulu. Dalam tugas akhir inisebelum masuk peramalan beban listrik menggunakan jaringan saraf tiruan metode kohonen digunakan terlebih dahulu metode backpropagation dan counterpropagation untuk menghitung peramalan mean dan standar deviasi. Kedua nilai peramalan mean dn standar deviasi selanjutnya akan digunakan sebagai parameter pembentuk jaringan kohonen.


ABSTRACT

Electrical energy consumption has an important role in the implementation of development for the welfare and increase economic activity. Thus necessary for forecasting electricity load menyelenggakan electric power supply business in a number of evenly. Number of electric power consumption by the public in kWh units greatly affect the calculation of the provision of electric power. The purpose of the electrical load forecasting is to make policy evaluation of electricity supply in the future. The objective of this final project is create software that can predict power consumption using Kohonen Neural Network method and to compare the forecasting accuracy generated by merging Kohonen with backpropagation method and Kohonen with counterpropagation. Kohonen method selected to solve this electrical load forecasting which is a network of competition with his training without supervision unsupervised competitive learning that can directly process the data types are seasonal with no preprocessing data. In this final project before entering the electric load forecasting using a Kohonen neural network method first used counterpropagation and backpropagation method to forecasting the mean and standard deviation. Both the mean and the standard deviation forecast value will then be used as a Kohonen network-forming parameters.



Keywordsbeban listrik;peramalan;kohonen;jaringan saraf tiruan
 
Subject:  Jaringan komputer
Contributor
  1. ARNA FARIZA, S.Kom, M.Kom
  2. SETIAWARDHANA, ST.
Date Create: 04/08/2010
Type: Text
Format: pdf.
Language: Indonesian
Identifier: ITS-Undergraduate-3100010040862
Collection ID: 3100010040862
Call Number: RSEP 006.3 Sye p


Source
Undergraduate Thesis, Informatics Engineering, RSEP 006.3 Sye p, 2010

Coverage
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Rights
Copyright @2010 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




[ Download - Open Access ]

  1.  ITS-Undergraduate-12482-Abstract_id.pdf - 76 KB
  2.  ITS-Undergraduate-12482-Abstract_en.pdf - 85 KB
  3.  ITS-Undergraduate-12482-Conclusion.pdf - 96 KB
  4.  ITS-Undergraduate-12482-Paper.pdf - 421 KB




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