After successful completion of the course, students are able to...

• classify modeling concepts for industrial energy systems, name their advantages and limitations, and select them according to the application.
• understand the most important analytical, data-driven and NN modeling methods.
• validate models and quantitatively evaluate their accuracy and sensitivities using appropriate methods.
• apply simple numerical solution methods for ordinary and partial differential equations.
• evaluate space-time discretization dependent effects and their influence on the quality of the solution in FD/FV/FE.
• apply parameter estimation techniques such as linear and non-linear regression and data-driven modeling concepts to given data sets and apply them to specific modeling problems of industrial power systems.

The basis for almost all calculations on industrial energy systems are models that represent the behavior or certain properties of the components. In this course, a wide range of modeling methods is presented - from simple regression models to various data-driven approaches and machine learning methods. Aspects of data preparation and model validation/evaluation will be discussed.

Attendance of the UE offered accompanying to this VO is strongly recommended.

Contents are presented in lectures.

Written examination with theoretical questions and short calculation exercises.