Predictive Modelling for Effective Energy Consumption in Industry 4.0 using machine learning techniques

Optimizing energy consumption has become an important challenge for the steel industry as it moves towards Industry 4.0. This paper develops a predictive modelling framework that employs machine learning methods to improve the energy efficiency of steel manufacturing processes. Our focus is on energy consumption patterns in relation to industry 4.0, and this highlights the need for accurate prediction models to guide decision making processes. Therefore, our research presents the following steps; data pre-processing, feature engineering and appropriate model selection for steel-specific purposes only. Comprising of regression and classification algorithms such as support vector machines, random forests and neural networks we prove that our method can be used to make accurate predictions on different operational scenarios for the energy needs of users. The integrated approach consisting of real-time data streams with sensor networks supporting adaptive modelling is investigated here in relation to dynamic production environments. Finally, we outline how forecasting models such as those developed can be used as foundation blocks in achieving sustainable energy practices in Industry 4.0 transition by employing machine learning facilitated proactive energy management strategies by stakeholders with a view to optimize resource utilization, minimize environmental impact, and enhance competitiveness in the evolving industrial landscape.