This study uses intraday electricity load demand data from Kuaro Main Gate data in East Kalimantan as the basis of an empirical comparison of Double Seasonal ARIMA models for prediction up to a day ahead. For the purpose of this study, a one-year hourly Kuaro Main Gate data load demand from 1 January 2018 to December 2018 measured in Megawatt (MW) is used. In multiple times of load demand data, in addition to intraday and intra week cycles, and intra year seasonal cycle is also apparent. We extend the Double Seasonal ARIMA methods in order to accommodate the Intra year seasonal cycle. The mean absolute percentage error (MAPE) is used as the measure of forecasting accuracy. A notable feature of the time series is the presence of both an intraweek and an intraday seasonal cycle. We also propose that a Double Seasonal ARIMA model with the one-step-ahead forecast as the most appropriate model for forecasting the two-seasonal cycles Kuaro Main Gate data load demand time series. We use the Statistical Analysis System package to analyze the data. Using the least-squares method to estimate the coefficients in a Double Seasonal ARIMA model, followed by model validation and model selection criteria, we propose the ARIMA (1,1,1)(0,1,1)24(0,1,1)168 within-sample MAPE of 0.000992 as the best model for this study. Comparing the forecasting performances by using k-step ahead forecasts and one-step-ahead forecasts, we found that the MAPE for the one-step ahead out-sample forecasts from any horizon ranging from one week lead time to one month one week lead time are all less than 5%. Therefore we propose that a double seasonal ARIMA model with a one-step-ahead forecast must be considered in forecasting time series data with two seasonal cycles.
In the last decades, electricity becomes essential problems in East Kalimantan. Some previous research has conducted to assess the power plant systems and to examine the future electricity demand during the period. Moreover, there are some particular events such as national holidays that bring different impacts on the electricity demand. Forecasting electricity loads on national holidays will give significant information to the stakeholders in order to prevent the power outage of electricity. Two samples of hourly electricity loads in national holidays; New Year and Independence Day during 2015-2018 is analyzed in this research. Furthermore, the SARIMA model is used to estimate the suitable model to predict the future electricity loads. The result shows that SARIMA (2,2,0)(0,1,0)24 and SARIMA (1,1,0)(1,1,0)24 are the appropriate model for New Year and Independence Day, respectively. In addition, it is predicted that in 2020 the electricity demand on New Year will rise to 400 MegaWatt based on 95% of confidence intervals. On Independence Day, the electricity consumption will reach 300-450 MegaWatt in 2019 and 2020 with significant growth up to 10% each year.
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