PhD defence: Ole Magnus Brastein

Ole Magnus Brastein, candidate in the PhD programme Process, Energy and Automation Engineering at Faculty of Technology, Natural Sciences and Maritime Sciences (TNM), will be defending his thesis for the degree of philosophiae doctor (PhD). Title of thesis is “Parameter estimation and analysis for grey-box models of building thermal behavior”

12 Oct

Practical information

  • Date: 12. October 2020
  • Time: 09.30 - 15.00
  • Location: Porsgrunn
  • Download calendar file

  • Both the trial lecture and the defence will be held on Zoom due to the coronavirus situation.

    Follow the dissertation live.
    (Link will be activated when the program starts.)


    Kl. 09.30: Trial lecture “Overview of different filtering techniques and their implications for implementations in the grey-box modelling framework”

    Kl. 12.00: PhD defence “Parameter estimation and analysis for grey-box models of building thermal behavior”

    Questions to the PhD candidate can be sent to the administrator of the assessment committee Marianne Sørflaten Eikeland prior to the doctoral dissertation.

    Evaluation committee:

    • First opponent: Professor Henrik Madsen, Danmarks Tekniske Universitet (DTU)
    • Second opponent: Professor Alexey Pavlov, NTNU
    • Third opponent and administrator: Associate professor Marianne Sørflaten Eikeland, USN

    Principal supervisor:

    • Professor Nils-Olav Skeie, USN


    • Associate professor Roshan Sharma, USN
    • Professor Carlos Pfeiffer, USN

    Read the PhD thesis here.


Ole Magnus BrasteinWe consume to much energy, which in turn causes excessive emissions of CO2. Reducing the consumption of energy is therefore one of the great challenges of our time. Heating and cooling of buildings account for approximately one fifth of the total energy consumption within the EU. Hence, the potential for financial and environmental savings is large.

By using mathematical models, which describes the thermal properties of a building, we can reduce the consumption of energy by optimizing the comfort temperature only when the building is in use. In addition, such models can be used to estimate and compare the thermal properties of a building in a more precise manor then the current energy classification systems.

The challenge of modeling buildings is their inherent complexity, being made from a large number of different materials and construction techniques. Additionally, heating of buildings depends on the weather conditions and the human occupants. This makes modeling of buildings a complex task. A possible solution is the use of simplified models, so called "thermal networks". These are simplified mathematical models that must be adapted (calibrated) to each specific building.

The thesis has studied the challenges linked with the use of these simplified models, and how measurement data from sensors can be used in the calibration of these models. The result of the work includes studying how the calibration of models can be quality ensured, such that they can be used to estimate the thermal properties of a given building. This in turn makes it possible to use the calibrated models as tools for more optimal control of temperature, thereby saving energy, money and reducing emissions of CO2. In addition, the thesis provides some answers as to what it takes for this kind of models to be usable for performing energy classification of an existing buildings.