Razvoj energijskih digitalnih dvojčkov kosovne proizvodne linije : digitalni dvojčki

slovenian

2026

volume 32, issue 2

discrete manufacturing   discrete-event modelling   energy consumption   energy digital twin   energy management   forecasting   industrial energy systems   Monte Carlo simulation   production optimization   production processes   stochastic Petri nets

Univerza v Ljubljani, Fakulteta za strojništvo
(Obvezni izvod spletne publikacije)

The paper addresses the development of energy digital twins for discrete manufacturing processes with the aim of improving energy consumption management. Due to increasing price volatility of energy sources and the demands of the green transition, accurate energy consumption forecasting is becoming crucial for maintaining companies’ competitiveness. A data-driven approach is presented that combines discrete-event modelling of the production process with a statistical description of repeatable energy consumption profiles of individual operations. First, consumption prototypes for individual operations are identified based on measurements, and then the production flow is modelled using stochastic Petri nets. By applying Monte Carlo simulations, it is possible to predict total energy consumption and evaluate its variability. The approach was demonstrated on the LabTop demonstration production line, where the results show a good match between predicted and actual energy consumption. The developed model enables time-dependent forecasting of consumption for different production plans and supports the optimization of production scheduling. Energy digital twins thus represent an important tool for reducing peak loads, improving energy efficiency, detecting anomalies, and supporting sustainable and adaptive management of industrial processes.

13.10.2026