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