Abstract
This paper discusses improving the accuracy of electrical load forecasting by imputation on empty load data. It is important to estimate the demand for electricity loads for the power plant operating system, fuel supply and maintenance of the power system. The forecast of the electrical load is carried out on the basis of the historical data of electrical load which is generally represented in the load curve. The load curves in research at the Singkarak substation Borang show that there are several load patterns, some missing data and data that is suddenly increasing. The percentage of blank data in 2015 was 1.8379%, while the highest in September at was 0.5137% or 45 hours. To fill in the missing data, three imputation techniques were used, i.e., filling in the data from the previous day's data at the same time ; perform regression analysis on the month the data was missing; and using the mean values from monthly data. The results of forecast using the moving average method provide a forecast of the electrical load on January 1, 2016 wll be 138 kW. The Mean Absolute Error (MAE) for the best load forecast is 9.59, using a data set equipped with the imputation of the mean.
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