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Course syllabus - Digital Visualization and Simulation in Building Engineering

Scope

7.5 credits

Course code

BTA314

Valid from

Autumn semester 2026

Education level

Second cycle

Progressive Specialisation

A1N (Second cycle, has only first-cycle course/s as entry requirements)

Main area(s)

Energy Engineering, Building Engineering

Organisation

Department of Engineering Sciences

Ratified

2025-06-26

Revised

2025-12-18

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

Objectives

The aim of this course is to develop the students’ skills in using advanced simulation tools in building engineering. Students will learn to optimize building performance in terms of energy efficiency and sustainability through the application of simulation methods. The course also emphasizes the critical evaluation of simulation outcomes, encouraging continuous refinement of models for more effective engineering solutions.

Learning outcomes

After completing the course, the students should be able to:

  1. Construct and implement advanced simulation techniques to develop dynamic building simulation models.
  2. Analyze and synthesize simulation results to evaluate building efficiency and formulate strategies for improvement in both design and operation.
  3. Critically assess the validity, accuracy, and limitations of simulation outcomes, and recalibrate models to enhance decision-making processes.
  4. Demonstrate in-depth understanding of the assumptions, constraints, and applicability of various simulation tools, and justify their impact on engineering and design choices.

Course content

  • Introduction to data sources and their application in BEM and BIM
  • Dynamic building energy simulation
  • Model validation, calibration, parametric studies and uncertainty analysis
  • Modeling of building fabric, spaces, systems and occupants

Specific requirements

At least 5 credits in energy technology, energy-efficient buildings and 5 credits in a combination of programming and basic machine learning or equivalent. In addition Swedish course 3 or Swedish level 3 and English course 6 or English level 2 are required. For courses given entirely in English exemption is made from the requirement in Swedish course 3 or Swedish level 3.

Examination

INL3, Written assignment, 3 hp, grades: Excellent (A), Very good (B), Good (C), Satisfactory (D), Sufficient (E), Insufficient, complementary work possible (Fx), Insufficient (F). Learning outcome 1-2.

INL4, Written assignment, 3 hp, grades: Excellent (A), Very good (B), Good (C), Satisfactory (D), Sufficient (E), Insufficient, complementary work possible (Fx), Insufficient (F). Learning outcome: 3-4.

SEM1, Seminar, 1.5 hp, grades: Excellent (A), Very good (B), Good (C), Satisfactory (D), Sufficient (E), Insufficient, complementary work possible (Fx), Insufficient (F). Learning outcome 1-4.

A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.

Grade

AF-skala