Fixed unconsistent use of acronyms
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@ -7,7 +7,7 @@ analyzed in this Section have used a sampling time of 15 minutes and a control
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horizon of 8 steps.
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Section~\ref{sec:GP_results} analyzes the results of a conventional
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\acrlong{gp} Model trained on the first five days of gathered data. The model
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\acrshort{gp} Model trained on the first five days of gathered data. The model
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is then used for the rest of the year, with the goal of tracking the defined
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reference temperature.
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@ -131,7 +131,7 @@ performance, but are more complex in implementation.
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\subsection{Sparse and Variational Gaussian Process}\label{sec:SVGP_results}
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The \acrlong{svgp} models are setup in a similar way as described before. The
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The \acrshort{svgp} models are setup in a similar way as described before. The
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model is first identified using 5 days worth of experimental data collected
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using a \acrshort{pi} controller and a random disturbance signal. The difference
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lies in the fact than the \acrshort{svgp} model gets re-identified every night
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@ -143,7 +143,7 @@ setup performs much better than the initial one. The only large deviations from
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the reference temperature are due to cold weather, when the \acrshort{hvac}'s
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limited heat capacity is unable to maintain the proper temperature.
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Additionnaly, the \acrshort{svgp} controller takes around 250 - 300ms of
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computation time for each simulation time, decreasing the computational cost of
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computation time for each simulation step, decreasing the computational cost of
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the original \acrshort{gp} by a factor of six.
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@ -293,7 +293,7 @@ As seen in Figure~\ref{fig:SVGP_evol_importance}, the variance of the
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signifies the increase in confidence of the model. The hyperparameters
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corresponding to the exogenous inputs --- $w1,1$ and $w1,2$ --- become generally
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less important for future predictions over the course of the year, with the
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importance of $w1,1$, the \acrlong{ghi}, climbing back up over the last, colder
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importance of $w1,1$, the \acrshort{ghi}, climbing back up over the last, colder
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months. This might be due to the fact that during the colder months, the
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\acrshort{ghi} is the only way for the exogenous inputs to \textit{provide}
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additional heat to the system.
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@ -361,7 +361,7 @@ simulation data (cf. Figures~\ref{fig:SVGP_96pts_fullyear_simulation}
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and~\ref{fig:SVGP_96pts_abserr}) it is very notable that the model performs
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almost identically to the one identified in the previous sections. This
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highlights one of the practical benefits of the \acrshort{svgp} implementations
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compared to the classical \acrlong{gp} -- it is possible to start with a rougher
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compared to the classical \acrshort{gp} -- it is possible to start with a rougher
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controller trained on less data and refine it over time, reducing the need for
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cumbersome and potentially costly initial experiments for gathering data.
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@ -473,7 +473,7 @@ models can be deployed with less explicit identification data, but they will
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continue to improve over the course of the year, as the building passes through
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different regions of the state space and more data is collected.
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However, these results do not discredit the use of \acrlong{gp} for employment
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However, these results do not discredit the use of \acrshort{gp} for employment
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in a multi-seasonal situation. As shown before, given the same amount of data
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and ignoring the computational cost, they perform better than the alternative
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\acrshort{svgp} models. The bad initial performance could be mitigated by
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