DESAIN MODEL UNTUK PRAKIRAAN BEBAN JANGKA MENENGAH DENGAN REGRESI MULTIPLE DAN JARINGAN SYARAF TIRUAN : Studi Kasus Pada PT.PLN (Persero) P3B Jawa Bali Region IIJawa Barat

Bramana, Andrian (2014) DESAIN MODEL UNTUK PRAKIRAAN BEBAN JANGKA MENENGAH DENGAN REGRESI MULTIPLE DAN JARINGAN SYARAF TIRUAN : Studi Kasus Pada PT.PLN (Persero) P3B Jawa Bali Region IIJawa Barat. S1 thesis, Universitas Pendidikan Indonesia.

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Official URL: http://repository.upi.edu

Abstract

Penelitianinimengkajitentangdesain model untukperkiraanbebanlistrikjangkamenengahdenganmetodemultiple regressiondanmetodebackpropagationberbasisjaringansyaraftiruan. Data yang dipakaiadalah data bebanlistrikdari PT. PLN (Persero) P3B Jawa Bali Region II Jawa Barat setiapbulanmulaijanuarisampaidengandesembermulaidaritahun 2007 sampaidengan 2013 yang kemudian data tersebutakandilakukanpembuatan model multiple regressiondenganmelakukanperhitungandenganmicrosoft excel. SedangkanuntukmetodeBackpropagationberbasisjaringansyaraftiruan data tersebutakandibelajarkanpadasistemperangkatlunak yang sudahdirancangdenganalgoritmabackpropagation.Softwarependukunguntukmerancang program tersebutdigunakanMatlab ver. R2009a dariThe MathWork. Inc. melaluiperhitungandarihasildesain model denganmenggunakanmetodemultiple regressionmenunjukkanrata-rata error 0,0114atausebesar 1.14% dandarihasilujiforecastuntuk data digesersatutahunmenunjukan rata-rata error 0.0386 atausebesar 3.86% halinimasihdalamtoleransinilai yang diberikanoleh PT. PLN yaitusebesar 5%. Sedangkandarihasilsimulasidesain model load forecastingdenganmetodebackpropagationberbasisjaringansyaraftiruanmenunjukantingkaterror rata-rata sebesar 0.012% dengannilaiepoch 9000 dannilailearning ratepada 0,5. Dengandemikiandapatdisimpulkanbahwaperamalanbebanlistrikjangkamenengahdenganmenggunakanmetodebackpropagationberbasisjaringansyaraftiruanlebihbaikdibandingkandengandesain model perkiraanbebanlistrikdenganmenggunakanmultiple regression. This studyexamines thedesign ofa modelforthe medium-termelectricityloadforecastby the method ofmultiple regressionandback propagationmethodbased onartificial neuralnetworks. The dataused is theelectricityload datafromPT. PLN(Persero) P3BJawaBaliRegionIIWest Javaeverymonth startingJanuaryuntil Decemberrangingfrom 2007to 2013and thenthe data isperformedmultiple regressionmodelingtoperformcalculationswithMicrosoft Excel. As for themethod ofbackpropagationartificial neuralnetwork-based datawillbe taughtina softwaresystemthat has beendesignedwithback propagationalgorithm. Supporting softwareis usedto designthe programMatlabver. R2009afrom TheMathWork. Inc..throughthe calculationofthe results ofthe designmodelsby usingmultiple regressionmethodshowsan averageerrorof1.14% or0.0114oftest resultsandforecastsforthe datais shiftedoneyearshowedan averageerrorof0.0386or3.86%, this is stillwithinthe tolerancevaluegivenbyPT. PLNis equal to5%. While the results ofthe simulationmodel designloadforecastingmethodbased onback propagationneural networksshowan averageerrorrateof0.012% with a9000epochvalueandthe valueof learning rateat 0.5. It can be concludedthat themid-termelectricload forecastingusing amethodbased onback propagationneural networkis better than thedesignestimatemodelsof electricloadby usingmultiple regression.

Item Type: Skripsi,Tesis,Disertasi (S1)
Additional Information: No. Panggil S TE BRA d-2014 ; Pembimbing : I. Sumarto, II. Tasma Sucita.
Uncontrolled Keywords: Peramalan Beban Jangka Menengah, Multiple Regression, Jaringan Syaraf Tiruan Berbasis Back propagation, Beban Listrik
Subjects: L Education > LC Special aspects of education
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Fakultas Pendidikan Teknologi dan Kejuruan > Jurusan Pendidikan Teknik Elektro
Depositing User: Staf Koordinator 3
Date Deposited: 29 Jul 2015 07:59
Last Modified: 29 Jul 2015 07:59
URI: http://repository.upi.edu/id/eprint/15378

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