BibTex Citation Data :
@article{Medstat9202, author = {Marita Saptyani and Winita Sulandari and Pangadi Pangadi}, title = {PERAMALAN PENGGUNAAN BEBAN LISTRIK JANGKA PENDEK GARDU INDUK BAWEN DENGAN DSARIMA}, journal = {MEDIA STATISTIKA}, volume = {8}, number = {1}, year = {2015}, keywords = {}, abstract = { Bawen substation is a part of electrical distribution system. Forecasting load demand is required for power planning. Data used in this research are an hourly load demand of Bawen, Salatiga for 3 months, from February 2, 2013 to April 29, 2013, measured in Megawatt (MW).A half hourly load demand forecasting is needed for real time controlling and short-term maintenance schedulling. Since the data have two seasonal periods, i.e. daily and weekly seasonality with length 48 and 336 respectively, the model of double seasonal ARIMA (DSARIMA) is proposed as the most appropriate model for the case. Initial model is determined by the pattern of the data, based on the autocorrelation function plot. Some experiments was done by choosing several periods data. The most suitable model is chosen based on the outsample mean absolute percentage error (MAPE). The current study shows that the DSARIMA (0 , 1 , [1 , 20 , 47])(0 , 1 , 1) 48 (0 , 1 , 0) 336 is the best model to forecast 336 next period. Keywords : DSARIMA, MAPE, Electricity, Bawen }, issn = {2477-0647}, pages = {41--48} doi = {10.14710/medstat.8.1.41-48}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/9202} }
Refworks Citation Data :
Bawen substation is a part of electrical distribution system. Forecasting load demand is required for power planning. Data used in this research are an hourly load demand of Bawen, Salatiga for 3 months, from February 2, 2013 to April 29, 2013, measured in Megawatt (MW).A half hourly load demand forecasting is needed for real time controlling and short-term maintenance schedulling. Since the data have two seasonal periods, i.e. daily and weekly seasonality with length 48 and 336 respectively, the model of double seasonal ARIMA (DSARIMA) is proposed as the most appropriate model for the case. Initial model is determined by the pattern of the data, based on the autocorrelation function plot. Some experiments was done by choosing several periods data. The most suitable model is chosen based on the outsample mean absolute percentage error (MAPE). The current study shows that the DSARIMA (0, 1, [1, 20, 47])(0, 1, 1)48(0, 1, 0)336 is the best model to forecast 336 next period.
Keywords: DSARIMA, MAPE, Electricity, Bawen
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