Abstract
In this article, we analyze the energy consumption data of business customers registered by trading companies in Poland. We focus on estimating missing data in hourly series, as forecasts of this frequency are needed to determine the volume of electricity orders on the power exchange or the contract market. Our goal is to identify an appropriate method of imputation missing data for this type of data. Trading companies expect a specific solution, so we use a procedure that allows to choose the imputation method, which will consequently improve the accuracy of forecasting energy consumption. Using this procedure, a statistical analysis of the occurrence of missing values is performed. Then, three techniques for generating missing data are selected (missing data are generated in randomly selected series without missing values). The selected imputation methods are tested and the best method is chosen based on MAE and MAPE errors.