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The growing relevance of data science in engineering highlights the challenges of modelling complex systems using conventional methods, which are often time-consuming. This paper explores the application of various machine learning techniques to create energy models, aiming to minimize model deviation errors against real measurements. By comparing different approaches, this study seeks to optimize energy models, enhancing both accuracy and efficiency.

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Machine Learning Based Parameter Estimation of Energy Models in Digital Production Environments

  • 会议论文

  • 发表日期：

    2025年

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  • Fabian Fichtl,
  • Marco Ullrich,
  • Frieder Heieck,
  • Bernd Lüdemann-Ravit

摘要

The growing relevance of data science in engineering highlights the challenges of modelling complex systems using conventional methods, which are often time-consuming. This paper explores the application of various machine learning techniques to create energy models, aiming to minimize model deviation errors against real measurements. By comparing different approaches, this study seeks to optimize energy models, enhancing both accuracy and efficiency.