TY - JOUR

T1 - Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

AU - Zawodnik, Vanessa

AU - Schwaiger, Florian

AU - Sorger, Christoph

AU - Kienberger, Thomas

N1 - Publisher Copyright: © 2024 by the authors.

PY - 2024/3/10

Y1 - 2024/3/10

N2 - 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.

AB - 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.

KW - electric arc furnace

KW - electric steel industry

KW - forecast modelling

KW - Markov chain

KW - neural network

KW - time series forecasting

UR - http://www.scopus.com/inward/record.url?scp=85188699365&partnerID=8YFLogxK

U2 - 10.3390/en17061326

DO - 10.3390/en17061326

M3 - Article

VL - 17.2024

JO - Energies : open-access journal of related scientific research, technology development and studies in policy and management

JF - Energies : open-access journal of related scientific research, technology development and studies in policy and management

SN - 1996-1073

IS - 6

M1 - 1326

ER -