Predictive Analytics for Energy Consumption Profiles Using Digital Twins in Manufacturing Systems

  • Typ:Masterarbeit
  • Betreuung:

    Atieh Khodadadi

    Prof. Sanja Lazarova-Molnar


Problem: Accurate prediction of energy consumption for different manufacturing assets under varying tasks and operational conditions is necessary for optimizing energy use and planning. Existing methods often lack precision and adaptability to real-time changes.

Goal: This thesis will conduct a comprehensive literature review on different energy-consuming assets in manufacturing systems, focusing on their energy sources, energy profiles, and the existing literature on modeling, simulation, and digital twins. The review will explore how these models and technologies can be applied to analyze and predict energy consumption more accurately. The culmination of this review may lead to the development of a data-driven simulation model to analyze and predict the energy profile of assets in a simplified manufacturing system.

Required Skills and Knowledge

  • Modeling and simulation.
  • Knowledge of analytics and statistical modeling.
  • Familiarity with digital twin technologies and their implementation.
  • Understanding of energy systems and consumption metrics in manufacturing environments.