Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

Vanessa Zawodnik (), Florian Christian Schwaiger, Christoph Sorger and Thomas Kienberger
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Vanessa Zawodnik: Chair of Energy Network Technology, Montanuniversität Leoben, Franz Josef-Straße 18, 8700 Leoben, Austria
Florian Christian Schwaiger: Chair of Energy Network Technology, Montanuniversität Leoben, Franz Josef-Straße 18, 8700 Leoben, Austria
Christoph Sorger: Stahl- und Walzwerk Marienhütte GmbH, Südbahnstraße 11, 8020 Graz, Austria
Thomas Kienberger: Chair of Energy Network Technology, Montanuniversität Leoben, Franz Josef-Straße 18, 8700 Leoben, Austria

Energies, 2024, vol. 17, issue 6, 1-19

Abstract: The iron and steel industry significantly contributes to global energy use and greenhouse gas emissions. The rising deployment of volatile renewables and the resultant need for flexibility, coupled with specific challenges in electric steelmaking (e.g., operation optimization, optimized power purchasing, effective grid capacity monitoring), require accurate energy consumption and demand forecasts for electric steel mills to align with the energy transition. This study investigates diverse approaches to forecast the energy consumption and demand of an electric arc furnace—one of the largest consumers on the grid—considering various forecast horizons and objectives with limited knowledge on process parameters. The results are evaluated for accuracy, robustness, and costs. Two grid connection capacity monitoring approaches—a one-step and a multi-step Long Short-Term Memory neural network—are assessed for intra-hour energy demand forecasts. The one-step approach effectively models energy demand, while the multi-step approach encounters challenges in representing different operational phases of the furnace. By employing a combined statistic–stochastic model integrating a Seasonal Auto-Regressive Moving Average model and Markov chains, the study extends the forecast horizon for optimized day-ahead electricity procurement. However, the accuracy decreases as the forecast horizon lengthens. Nevertheless, the day-ahead forecast provides substantial benefits, including reduced energy balancing needs and potential cost savings.

Keywords: electric steel industry; electric arc furnace; forecast modelling; time series forecasting; neural network; Markov chain (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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