Projekt

Models for the heat dynamics of buildings fitted to data from thermostatic controlled conditions

Udbyder

Vejleder

Sted

København og omegn

In this project models for the heat dynamics of buildings which can be fitted to data
from thermostatic controlled conditions. Usually, the models are setup such that the
heat released (by the heating system) inside the building is the input to the model
together with the climate (ambient temperature and solar radiation, and wind) and
the indoor temperature is the model output. However, in most conditions a ther-
mostatic control is activated to keep a constant temperature. In this case the model
output should be the heat which is released by the heating system, and in then the
model needs to include a description of the thermostatic control. This leads to non-
linear models. The application of such models would be to enable the temperature
set-point of the thermostat to be used as a control input, enabling the use of the
building thermal mass as energy storage, which is needed for integration of more
wind and solar into the energy system.
In the project statistical models should be formulated and fitted to data from exper-
iments with thermostatic controlled indoor temperature. The models can both be
fitted using local climate measurements, as well as weather forecasts, depending on
the application.

Design of experiments for dynamical systems and optimal learning sequences
As an option it can be investigated how to design optimal experiments for system
identification. A lot has been done in this area, however the are plenty of aspects
which needs to be studied e.g. design of optimal learning sequences for inhabited
buildings.

More buildings heat dynamics modelling: models with non-parametric solar radiation part
Additionally, there is a need to improve the solar radiation part of the currently
used models. This can be done very elegantly by formulating the part of the model,
which describe how the solar radiation enters the building as a non-parametric
model part.

Forudsætninger

Good R programming experience (or similar programming expe- rience) and some advanced courses in statistical modeling, preferably at least 02427 Time Series Analysis.

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Kontakt

Virksomhed/organisation

DTU Compute

Navn

Peder Bacher

Stilling

Lektor

Mail

pbac@dtu.dk

Vejleder-info

Bachelor i Matematik og Teknologi

Vejleder

Peder Bacher

Medvejledere

Henrik Madsen

ECTS-point

15 - 30

Type

Bachelorprojekt, Kandidatspeciale

Kandidatuddannelsen i Matematisk Modellering og Computing

Vejleder

Peder Bacher

Medvejledere

Henrik Madsen

ECTS-point

15 - 30

Type

Bachelorprojekt, Kandidatspeciale

OM DTU

DTU er et teknisk eliteuniversitet med international rækkevidde og standard. Vores mission er at udvikle og nyttiggøre naturvidenskab og teknisk videnskab til gavn for samfundet. 10.000 studerende uddanner sig her til fremtiden, og 5.700 medarbejdere har hver dag fokus på uddannelse, forskning, myndighedsrådgivning og innovation, som bidrager til øget vækst og velfærd.

Find os her

Anker Engelunds Vej 1
Bygning 101A
2800 Kgs. Lyngby


45 25 25 25

dtu@dtu.dk

CVR-nr. 30 06 09 46

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