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Sections/90_Further_Research.tex

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-\section{Further Research}
-
-Section~\ref{sec:results} has presented and compared the results of a full-year
-simulation for a classical \acrshort{gp} model, as well as a few incarnations of
-\acrshort{svgp} models. The results show that the \acrshort{svgp} have much
-better performance, mainly due to the possibility of updating the model
-throughout the year. The \acrshort{svgp} models also present a computational
-cost advantage both in training and in evaluation, due to several approximations
-shown in Section~\ref{sec:gaussian_processes}.
-
-Focusing on the \acrlong{gp} models, there could be several ways of improving
-its performance, as noted previously: a more varied identification dataset and
-smart update of a fixed-size data dictionary according to information gain,
-could mitigate the present problems.
-
-Using a Sparse \acrshort{gp} without replacing the maximum log likelihood
-with the \acrshort{elbo} could improve performance of the \acrshort{gp} model at
-the expense of training time.
-
-An additional change that could be made is inclusion of the most amount of prior
-information possible through setting a more refined kernel, as well as adding
-prior information on all the model hyperparameters when available. This approach
-however goes against the "spirit" of black-box approaches, since significant
-insight into the physics of the plant is required in order to properly model and
-implement this information.
-
-On the \acrshort{svgp} side, several changes could also be proposed, which were
-not properly addressed in this work. First, the size of the inducing dataset was
-chosen experimentally until it was found to accurately reproduce the manually
-collected experimental data. In order to better use the available computational
-resources, this value could be found programmatically in a way to minimize
-evaluation time, while still providing good performance. Another possibility is
-the periodic re-evaluation of this value when new data comes in, since as more
-and more data is collected the model becomes more complex, and in general more
-inducing locations could be necessary to properly reproduce the training data.
-
-Finally, none of the presented controllers take into account occupancy rates or
-adapt to possible changes in the real building, such as adding or removing
-furniture, deteriorating insulation and so on. The presented update methods only
-deals with adding information on behaviour in different state space regions, i.e
-\textit{learning}, and their ability to \textit{adapt} to changes in the actual
-plant's behaviour should be further addressed.