From 74e80594e64f5bd5d3210fb9a12e3634d76f7e43 Mon Sep 17 00:00:00 2001 From: "Radu C. Martin" Date: Tue, 20 Jul 2021 22:57:25 +0200 Subject: [PATCH] Removed unused section file --- Sections/90_Further_Research.tex | 42 -------------------------------- 1 file changed, 42 deletions(-) delete mode 100644 Sections/90_Further_Research.tex diff --git a/Sections/90_Further_Research.tex b/Sections/90_Further_Research.tex deleted file mode 100644 index 77d6a30..0000000 --- a/Sections/90_Further_Research.tex +++ /dev/null @@ -1,42 +0,0 @@ -\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.