Perdana, Januar Adi (2012) Peramalan Beban Listrik Jangka Pendek Menggunakan Optimally Pruned Extreme Learning Machine (OPELM) Pada Sistem Kelistrikan Jawa Timur - Short-Term Load Forecasting Using Optimally Pruned Extreme Learning Machine (OPELM) in Power System at East Java. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Peramalan beban listrik jangka pendek merupakan faktor yang
sangat penting dalam perencanaan dan pengoperasian sistem tenaga
listrik. Tujuan dari peramalan beban listrik adalah agar permintaan
listrik dan penyediaan listrik dapat seimbang. Karakteristik beban di
wilayah Jawa Timur sangat fluktuatif sehingga pada penelitian ini
digunakan metode Optimally pruned extreme learning machine
(OPELM) untuk meramalkan beban listrik. Kelebihan OPELM ada pada
learning speed yang cepat dan pemilihan model yang tepat meskipun
datanya mempunyai pola non linier. Keakuratan metode OPELM dapat
diketahui dengan menggunakan metode pembanding yaitu metode ELM.
Kriteria keakuratan yang digunakan adalah MAPE. Hasil dari
perbandingan kriteria keakuratan menunjukkan bahwa hasil peramalan
OPELM lebih baik dari ELM. Error rata-rata hasil pengujian peramalan
paling minimum menunjukkan MAPE sebesar 1,5534% terjadi pada
peramalan hari Jumat, sementara pada hari yang sama dengan metode
ELM menghasilkan MAPE sebesar 2,6832%.
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Short-term electric load forecasting is a very important factor for
planning and operation of electric power systems. The purpose of the
electrical load forecasting is that electricity demand and supply can be
balanced. Load characteristics in East Java is very fluctuative, so in this
research used methods of optimally pruned extreme learning machine
(OPELM) to forecast electricity load. Advantadges OPELM on a fast
learning speed and the selection of an appropriate model although the
data have non-linear pattern. OPELM accuracy can be determined by
using the comparison ELM method. Accuracy criteria used MAPE. The
results of the comparison criteria indicate that the forecasting accuracy
of OPELM is better than ELM. Result of testing on forecast gives the
smallest average error percentage by MAPE of 1,5534% for forecasting
Friday, while on the same day forecasting, ELM give MAPE of
2,6832%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSE 621.374 5 Per p |
| Uncontrolled Keywords: | Peramalan Beban Listrik Jangka Pendek, OPELM, ELM, Short-term electric load forecasting, OPELM, ELM. |
| Subjects: | T Technology > T Technology (General) > T174 Technological forecasting |
| Divisions: | Faculty of Industrial Technology > Electrical Engineering |
| Depositing User: | ansi aflacha |
| Date Deposited: | 12 Dec 2019 07:09 |
| Last Modified: | 12 Dec 2019 07:09 |
| URI: | http://repository.its.ac.id/id/eprint/72346 |
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