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70_Implementation.tex
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@ -21,13 +21,13 @@ in the \acrshort{wdb} object is given in Section~\ref{sec:CARNOT_WDB}.
\subsection{Simulink Model}
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}.
as communication with the Python controller. A complete schematic of the
Simulink setup is presented in Figure~\ref{fig:Simulink_complete}.
\begin{figure}[ht]
\centering
\includegraphics[width = 0.75\textwidth]{Images/polydome_python.pdf}
\caption{Simulink Schema of the Complete Simulation}
\caption{Simulink diagram of the Complete Simulation}
\label{fig:Simulink_complete}
\end{figure}
@ -158,7 +158,7 @@ Let $w_l$, $u_l$, and $y_l$ be the lengths of the state vector components
$\mathbf{w}$, $\mathbf{u}$, $\mathbf{y}$ (cf. Equation~\ref{eq:components}).
Also, let X be the matrix of all the system states over the optimization horizon
and W be the matrix of the predicted disturbances for all the future steps. The
original \acrlong{ocp} can be rewritten as:
original \acrlong{ocp} can be rewritten using index notation as:
\begin{subequations}\label{eq:sparse_optimal_control_problem}
\begin{align}
@ -174,7 +174,7 @@ original \acrlong{ocp} can be rewritten as:
\end{align}
\end{subequations}
\subsection{Python server}
\subsection{Model Identification and Update using a Python server}
The Python server is responsible for the control part of the simulation. It
delegates which controller is active, is responsible for training and updating
@ -182,16 +182,16 @@ 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
building's thermal behaviour. For this part of the simulation, the server
switches to a \acrshort{pi} controller with random disturbances until it gathers
enough data to train a \acrshort{gp} model. This ensured that the building is
enough data to train a \acrshort{gp} model. This ensures 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 all the features are first scaled to the
range [-1, 1] in order to reduce the possibility of numerical instabilities. The
Python controller then trains the \acrshort{gp} model and switches to tracking
the appropriate SIA 180:2014 reference temperature (cf.
Python server then 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