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95_Conclusion.tex

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-\section{Conclusion}
-
-The aim of this project was to analyse the performance of \acrshort{gp} based
-controllers for use in longer lasting implementations, where differences in
-building behaviour become important compared to the initially available data.
-
-First, the performance of a classical \acrshort{gp} model trained on 5 days
-worth of experimental data was analysed. This model turned out to be unable to
-correctly extrapolate building behaviour as the weather changed throughout the
-year.
-
-Several \acrshort{svgp} implementations were then analysed. They turned out to
-provide important benefits over the classical models, such as the ability to
-easily scale when new data is being added and the much reduced computational
-effort required. They do however present some downsides, namely increasing the
-number of hyperparameters by having to choose the number of inducing locations,
-as well as performing worse than then classical \acrshort{gp} implementation
-given the same amount of data.
-
-Finally, the possible improvements to the current implementations have been
-addressed, noting that classical \acrshort{gp} implementations could also be
-adapted to the \textit{learning control} paradigm, even if their implementation
-could turn out to be much more involved and more computationally expensive than
-the \acrshort{svgp} alternative.
-
-\section*{Acknowledgements}
-
-I would like to thank Koch Manuel Pascal for the great help provided during the
-course of the project starting from the basics on CARNOT modelling, to helping
-me better compare the performance of different controllers, as well as Professor
-Jones, whose insights were always very guiding, while still allowing me to
-discover everything on my own.
-
-\clearpage