Khusna, Hidayatul (2015) Pendekatan Percentile Error Bootstrap Pada Model Double Seasonal Holt-Winters, Double Seasonal Arima, Dan Naïve Untuk Peramalan Beban Listrik Jangka Pendek Area Jawa Timur-Bali. Undergraduate thesis, Institut Technology Sepuluh Nopember.
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Abstract
Listrik memiliki karakteristik tidak dapat disimpan. Karena
itu, listrik harus dibangkitkan hanya jika diperlukan. PT. PLN
perlu melakukan pengoptimalan pendistribusian listrik hingga
skala operasional melalui peramalan beban listrik per setengah
jam. Interval prediksi pada model double seasonal Holt-Winters
(DSHW) tidak dapat dikonstruksi secara analitis. Jika digunakan
untuk meramal jauh ke depan, model double seasonal ARIMA
memiliki varians error yang semakin besar sehingga interval
prediksi semakin lebar. Sementara model Naïve untuk data
musiman memiliki varians error yang semakin besar setiap
kelipatan periode musiman. Percentile error bootstrap
merupakan metode nonparametrik yang dapat digunakan untuk
mengkonstruksi interval prediksi. Data yang digunakan dalam
penelitian ini adalah beban listrik jangka pendek area Jawa
Timur-Bali dalam satuan Mega Watt (MW) untuk periode 1
Januari 2013 hingga 30 September 2013. Hasil penelitian
menunjukkan bahwa model DSARIMA terbaik berdasarkan
kriteria out-sample sMAPE, kriteria in-sample AIC-SBC, serta
kriteria out-sample rata-rata lebar interval prediksi. Dengan
demikian, dapat disimpulkan bahwa model terbaik untuk
peramalan beban listrik jangka pendek area Jawa Timur-Bali
adalah model DSARIMA dengan interval prediksi yang
dikonstruksi menggunakan pendekatan percentile error bootstrap.
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Electricity characteristic cannot be saved. Thus, electricity
must be generated only if needed by customers. PT. PLN need to
optimize the electricity distribution until operational scale by
using half hourly electricity forecasting. The prediction interval
of double seasonal Holt-Winters (DSHW) cannot be constructed
analytically. Otherwise, the more step ahead to forecast, the
larger variance error of DSARIMA model. Thus, the prediction
interval become wider. Variance error of Naïve model for
seasonal data are bigger in each seasonal period multiply.
Percentile error bootstrap is one of the nonparametric methods
that used to construct the prediction interval. The data that used
in this research is short term electricity load demand forecasting
of East Java-Bali area in Mega Watt (MW) unit for January 1st
2013 until September 30th 2014. The result shows that DSARIMA
model is excellent based on out-sample sMAPE, in-sample AICSBC,
and out-sample average of prediction interval width
criteria. Hence, it can be concluded that the best model to
forecast short term electricity load demand of East Java-Bali
area is DSARIMA model which prediction interval is constructed
by using percentile error bootstrap approach.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSSt 519.535 Khu p |
| Uncontrolled Keywords: | beban listrik, double seasonal Holt-Winters, double seasonal ARIMA, Naïve, percentile error bootstrap |
| Subjects: | Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry) |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Mr. Tondo Indra Nyata |
| Date Deposited: | 28 May 2018 05:54 |
| Last Modified: | 28 May 2018 05:54 |
| URI: | http://repository.its.ac.id/id/eprint/51952 |
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