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.
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.
Crop insurance provides a solution to economic losses experienced by farmers due to the risk of crop failure. The sample of crop survival data from Sukaratu Village in Cianjur-West Java had been analyzed. This research was analyzed the probability of failure based on the empirical study of the survival model. As a result, the probability of a crop failure studied in Sukaratu Village was calculated at 3.27%. Then, the exponential regression model was used to get the waiting time between crop failure events. The crop insurance product was simulated under the rate of premiums and benefits of insurance in several cases.
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