Syllabus for Empirical Modelling

Empirisk modellering

  • 10 credits
  • Course code: 1RT890
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Technology A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle
    G1N: has only upper-secondary level entry requirements
    G1F: has less than 60 credits in first-cycle course/s as entry requirements
    G1E: contains specially designed degree project for Higher Education Diploma
    G2F: has at least 60 credits in first-cycle course/s as entry requirements
    G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    GXX: in-depth level of the course cannot be classified.

    Second cycle
    A1N: has only first-cycle course/s as entry requirements
    A1F: has second-cycle course/s as entry requirements
    A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    AXX: in-depth level of the course cannot be classified.

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2011-03-07
  • Established by:
  • Revised: 2018-08-30
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 24, 2019
  • Entry requirements: Automatic control I. Either of Probability and statistics or Basic mathematical statistics.
    English language proficiency that corresponds to English studies at upper secondary (high school) level in Sweden ("English 6").
  • Responsible department: Department of Information Technology

Content

Examples of models for signal and systems. Introduction to stochastic processes. Modelling of dynamic systems with physical and empirical modelling. Parameter estimation in dynamic models; linear regression, the least squares method and prediction error methods. Black box and grey box modelling. Methods for model validation. Possibilities and limitations with empirical modelling. Overview of some modelling problems in energy systems.
Project: There are several project proposals to choose from, e.g. empirical modelling of some systems with relevance to energy systems.

Instruction

Lectures, lessons, tutorials and laboratory work.
Project supervision.

Assessment

Passed mini project and passed laboratory course are required. Complementary written examination may occur. 
 
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Syllabus Revisions

Reading list

The reading list is missing. For further information, please contact the responsible department.