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