Electricity consumption always changes according to need. This pattern deserves serious attention. Where the electric power generation must be balanced with the demand for electric power on the load side. It is necessary to predict and classify loads to maintain reliable power generation stability. This research proposes a method of forecasting electric loads with double seasonal patterns and classifies electric loads as a cluster group. Double seasonal pattern forecasting fits perfectly with fluctuating loads. Meanwhile, the load cluster pattern is intended to classify seasonal trends in a certain period. The first objective of this research is to propose DSARIMA to predict electric load. Furthermore, the results of the load prediction are used as electrical load clustering data through a descriptive analytical approach. The best model DSARIMA forecasting is ([1, 2, 5, 6, 7, 11, 16, 18, 35, 46], 1, [1, 3, 13, 21, 27, 46]) (1, 1, 1)48 (0, 0, 1)336 with a MAPE of 1.56 percent. The cluster pattern consists of four groups with a range of intervals between the minimum and maximum data values divided by the quartile. The presentation of this research data is based on data on the consumption of electricity loads every half hour at the Generating Unit, the National Electricity Company in Gresik City, Indonesia.
Part of the book: Forecasting in Mathematics