radu/Master-Thesis
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

WIP: Thesis update

Browse source
This commit is contained in:
Radu C. Martin 2021-06-21 22:32:43 +02:00
parent 3b4e35900f
commit 4bdc12f802
5 changed files with 83 additions and 58 deletions
Download patch file Download diff file Expand all files Collapse all files

27
70_Implementation.tex
View file

@ -20,21 +20,17 @@ in the \acrshort{wdb} object is given in Section~\ref{sec:CARNOT_WDB}.
\subsection{Simulink Model}
% TODO: [Implementation] Move the simulink schema here, with explanations of tcp
The final Simulink schema is presented in Figure~\ref{fig:CARNOT_complete}:
The secondary functions of the Simulink model is the weather prediction, as well
as communication with the Python controller. A complete schema of the Simulink
setup is presented in Figure~\ref{fig:Simulink_complete}.
\begin{figure}[ht]
\centering
\includegraphics[width = \textwidth]{Images/polydome_python.pdf}
\includegraphics[width = 0.75\textwidth]{Images/polydome_python.pdf}
\caption{Simulink Schema of the Complete Simulation}
\label{fig:Simulink_complete}
\end{figure}
The secondary functions of the Simulink model is the weather prediction, as well
as communication with the Python controller.
The communication between Simulink and the controller is done using three
separate TCP/IP sockets: one for sending the control signal, one for reading the
temperature measurement, and one for reading the weather forecast. This is
@ -185,5 +181,20 @@ delegates which controller is active, is responsible for training and updating
the \acrshort{gp} and \acrshort{svgp} models, as well as keeping track of all
the intermediate results for analysis.
In the beginning of the experiment there is no information available on the
building's thermal behaviour. For this part of the simulation, the controller
switches to a \acrshort{pi} controller until it gathers enough data to train a
\acrshort{gp} model. The signal is then disturbed by a random signal before
being applied to the CARNOT building. This ensured that the building is
sufficiently excited to capture its dynamics, while maintaining the
temperature within an acceptable range (~15 --- 25 $\degree$C).
Once enough data has been captured, the Python controller trains the
\acrshort{gp} model and switches to tracking the appropriate SIA 180:2014
reference temperature (cf. Section~\ref{sec:reference_temperature}).
For the case of the \acrshort{svgp}, a new model is trained once enough data is
gathered. The implementations tested were updated once a day, either on the
whole historical set of data, or on a window of the last five days of data.
\clearpage