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ITS » Master Theses » Teknik Elektro - Telematika S2
Posted by ansi@its.ac.id at 10/08/2012 15:10:11  •  1575 Views


ESTIMASI BEBAN LISTRIK HARIAN MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK BPNN DENGAN ERROR CORRECTION UNTUK PERUSAHAAN XYZ DI DAERAH OPERASI RIAU

DAILY LOAD FORECASTING USING BACKPROPAGATION NEURAL NETWORK BPNN WITH ERROR CORRECTION FOR XYZ COMPANY IN RIAU OPERATION

Author :
GUSTIADI, REFLI ( 2209206802 )




ABSTRAK

Ketersediaan bahan bakar menjadi isu utama di dunia saat ini dan bahan bakar yang umum digunakan masih berupa minyak dan gas bumi. Saat ini harga kedua jenis bahan bakar tersebut semakin mahal dan jumlahnya semakin terbatas. Oleh sebab itu maka diperlukan sebuah sistem perencanaan ketersediaan bahan bakar yang tepat efektif dan efisien untuk mengatasi permasalahan tersebut. Sebuah perencanaan ketersediaan bahan bakar pembangkit dibuat berbasiskan hasil estimasi beban listrik sehingga untuk menghasilkan sebuah perencanaan ketersediaan bahan bakar pembangkit yang tepat efektif dan efisien maka diperlukan sebuah proses estimasi beban listrik yang akurat. Penelitian ini mengusulkan penerapan estimasi beban listrik harian menggunakan metode Backpropagation Neural Network dengan Error Correction menggantikan metode Regresi Linier yang saat ini digunakan oleh perusahaan. Dari pengujian hasil estimasi beban listrik terhadap data aktual dapat ditunjukkan bahwa nilai rata-rata MSE Mean Square Error untuk peramalan beban listrik dalam rentang waktu hari ke-18 sampai hari ke-31 dengan metode RL 1.946 109 metode BPNN 0.16955 dan metode BPNN dengan Error Correction 0.08453. Hasil tersebut menunjukkan bahwa metode BPNN dengan Error Correction memberikan hasil MSE rata-rata terkecil untuk rentang waktu hari peramalan yang bervariasi. Oleh karena itu BPNN dengan Error Correction dapat diusulkan sebagai sebuah metode peramalan yang telah dibuktikan dapat menyelesaikan permasalahan estimasi beban listrik dengan sangat baik.


ABSTRACT

Fuel cost and availability currently become hot issue and headline in every country where the non renewable oil and gas still become the most important kind of fuel that used to produce energy. The cost of that kind of fuel are very expensive and only available in limited stock. So it is a must to develop an excellent plan of fuel consumption. In generator fuel case fuel consumption plan is developed based on load forecasting result. To develop an excellent fuel consumption plan it is a must to develop an accurate load forecasting result. These researches propose Backpropagation Neural Network with Error Correction method to provide high accuracy load forecasting result while the company still use Linear Regression method. Based on tested data for proposed load forecasting method compared with existing method shown that the average MSE Mean Square Error for load forecasting from day 18 until day 31 by using Linier Regression method 1.946 109 by using BPNN method 0.16955 and by using BPNN with Error Correction Method 0.08453. These results shown that BPNN with Error Correction method has the least average value of MSE Mean Square Error and has the minimum forecasting error value. So BPNN with Error Correction can be considered as an excelent load forecasting method to solve load forecasting problem.



KeywordsEstimasi beban listrik; Regresi Linier; Backpropagation Neural Network (BPNN); Error Correction.
 
Subject:  Rekayasa kelistrikan--Manajemen
Contributor
  1. Prof. Dr. Ir. Mauridhi Hery Purnomo, M.Eng.
  2. Mochamad Hariadi, S.T., M.Sc., Ph.D.
Date Create: 25/01/2012
Type: Text
Format: pdf
Language: Indonesian
Identifier: ITS-Master-22003120000038
Collection ID: 22003120000038
Call Number: RTM 658.403 55 Gus e


Source
Master Theses, Electrical Engineering, RTM 658.403 55 Gus e, 2012

Coverage
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Rights
Copyright @2012 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




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  1.  ITS-Master-20610-abstract-inpdf.pdf - 402 KB
  2.  ITS-Master-20610-abstract-enpdf.pdf - 215 KB
  3.  ITS-Master-20610-conclusionpdf.pdf - 387 KB




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