TY - THES

T1 - Time-Series Forecasting of an Electric Steel Mill's Power Demand

T2 - A Neural Network Approach

AU - Halbwirth, Sebastian

N1 - no embargo

PY - 2022

Y1 - 2022

N2 - When wanting to lower the atmospheric carbon dioxide concentration, it is of interest to look at the iron and steel industry, as especially the blast furnace process with its large coal consumption is a significant emittent. Electric steel mills, on the other hand, use electricity and natural gas to melt scrap metal into steel. Demand side management can thereby help to integrate renewable energy sources and increase energy efficiency. In this context, load forecasting is an important tool to know the future energy demand of such an industrial process. The purpose of this work is to develop a machine learning model that can predict the power demand of an electric steel mill’s primary aggregates as accurately as possible. Due to the high power demand of the electric arc furnace, a focus is laid on this aggregate. In the course of this thesis, literature research was conducted about machine learning algorithms, their advantages and disadvantages, and their use cases. In particular, machine learning methods used in energy system modelling were researched. A suitable method was then chosen, and based on this method, multiple models were created to forecast the aggregates’ power demand in a time-resolved manner. The method chosen for predicting the power demand was a neural network. Two types of neural networks were compared: long short-term memory networks and standard feedforward networks. Altogether six models were created, of which five are based on long short-term memory networks. The results show that long short-term memory networks can be used to predict the power demand of an electric arc furnace. By stochastically generating input parameters and realigning the predicted and actual batches of the electric arc furnace process, the model can be implemented in and used for demand side management applications.

AB - When wanting to lower the atmospheric carbon dioxide concentration, it is of interest to look at the iron and steel industry, as especially the blast furnace process with its large coal consumption is a significant emittent. Electric steel mills, on the other hand, use electricity and natural gas to melt scrap metal into steel. Demand side management can thereby help to integrate renewable energy sources and increase energy efficiency. In this context, load forecasting is an important tool to know the future energy demand of such an industrial process. The purpose of this work is to develop a machine learning model that can predict the power demand of an electric steel mill’s primary aggregates as accurately as possible. Due to the high power demand of the electric arc furnace, a focus is laid on this aggregate. In the course of this thesis, literature research was conducted about machine learning algorithms, their advantages and disadvantages, and their use cases. In particular, machine learning methods used in energy system modelling were researched. A suitable method was then chosen, and based on this method, multiple models were created to forecast the aggregates’ power demand in a time-resolved manner. The method chosen for predicting the power demand was a neural network. Two types of neural networks were compared: long short-term memory networks and standard feedforward networks. Altogether six models were created, of which five are based on long short-term memory networks. The results show that long short-term memory networks can be used to predict the power demand of an electric arc furnace. By stochastically generating input parameters and realigning the predicted and actual batches of the electric arc furnace process, the model can be implemented in and used for demand side management applications.

KW - Metallurgy

KW - Electric Steel Mill

KW - Electric Arc Furnace

KW - Artificial Intelligence

KW - Machine Learning

KW - Neural Network

KW - Forecast

KW - Energy Demand

KW - Power Demand

KW - Industrial Energy Systems

KW - Metallurgie

KW - Elektrostahlwerk

KW - Lichtbogenofen

KW - Künstliche Intelligenz

KW - Maschinelles Lernen

KW - Neuronales Netz

KW - Prognose

KW - Energiebedarf

KW - Leistungsbedarf

KW - Industrielle Energiesysteme

M3 - Master's Thesis

ER -