Comparison of Short-Term Load Forecasting Based on Kalimantan Data - Dinata, Syalam Ali Wira; Azka, Muhammad; Hasanah, Primadina; Suhartono, Suhartono; Gamal, Moh Danil Hendry (Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI), 2021)
This paper investigates a case study on short term forecasting for East Kalimantan, with emphasis on special days, such as public holidays. A time series of load demand electricity recorded at hourly intervals contains more than one seasonal pattern. There is a great attraction in using a modelling time series method that is able to capture triple seasonalities. The Triple SARIMA model has been adapted for this purpose and competitive for modelling load. Using the least squares method to estimate the coefficients in a triple SARIMA model, followed by model building, model assumptions and comparing model criteria, we propose and demonstration the triple Seasonal Autoregressive Integrated Moving Average model with AIC 290631.9 and SBC 290674.2 as the best model for this study. The Triple seasonal ARIMA is one of the alternative strategy to propose accurate forecasts of electricity load Kalimantan data for planning, operation maintenance and market related activities.