Koca, A.Erdem, Z.Aydin, M.N.2023-10-192023-10-1920229781665470100https://doi.org/10.1109/UBMK55850.2022.9919563https://hdl.handle.net/20.500.12469/49747th International Conference on Computer Science and Engineering, UBMK 2022 --14 September 2022 through 16 September 2022 -- --183844Forecasting electricity consumption in the most accurate way is crucial for purchase on the day-ahead market in steel manufacturing. This study is aimed to predict short-term electricity consumption regarding the day-ahead market purchase by employing important features of electricity consumption time-series data. We utilize Random Forest (RF), Gradient-Boosted Trees (GBT), and Generalized Linear Models (GLM), as they are appropriate for the given problem and widely used regression algorithms for prediction purposes. This study leverages the regression algorithms in the Apache Spark Machine Learning library. The performance of the prediction models is evaluated based on the standard deviation of the residuals (RMSE) and the proportion of variance explained (R-squared). We additionally discuss the distribution of prediction errors of the models. Experiments show that the RF model outperforms the GBT and GLM. It is considered that the results can contribute to accurate forecasting of short-term electricity demand for purchasing on the day-ahead. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessElectricity ConsumptionMachine LearningSteel ManufacturingTime SeriesTime Series ForecastingCommerceDecision treesElectric power utilizationForecastingMachine learningManufactureSalesDay ahead marketElectricity-consumptionForecasting electricityGeneralized linear modelImportant featuresMachine-learningRegression algorithmsSteel manufacturingTime series forecastingTimes seriesTime seriesConference Object21021510.1109/UBMK55850.2022.99195632-s2.0-851418640760