WIP: Pre-final version

20_Previous_Research.tex

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-\section{Previous Research}
-With the increase in computational power and availability of data  over time,
-the accessibility of data-driven methods for System Identification and Control
-has also risen significantly. 
-
-The idea of using Gaussian Processes as regression models for control of dynamic
-systems is not new, and has already been explored a number of times. A general
-description of their use, along with the necessary theory and some example
-implementations is given in~\cite{kocijanModellingControlDynamic2016}.
-In~\cite{pleweSupervisoryModelPredictive2020} a \acrlong{gp} Model with a
-\acrlong{rq} Kernel is used for temperature set point optimization.
-
-Gaussian Processes for building control have also been studied before in the
-context of Demand Response~\cite{nghiemDatadrivenDemandResponse2017,
-jainLearningControlUsing2018}, where the buildings are used for their heat
-capacity in order to reduce the stress on energy providers during peak load
-times.
-
-There are, however, multiple limitations with these approaches. 
-In~\cite{nghiemDatadrivenDemandResponse2017} the model is only identified once,
-ignoring changes in weather or plant parameters, which could lead to different
-dynamics. This is addressed in \cite{jainLearningControlUsing2018} by
-re-identifying the model every two weeks using new information. Another
-limitation is that of the scalability of the \acrshort{gp}s, which become
-prohibitively expensive from a computational point of view when too much data is
-added.
-
-Outside of the context of building control, Sparse \acrlong{gp}es have been used
-in autonomous racing in order to complement the physics-based model by fitting
-the unmodeled dynamics of the
-system~\cite{kabzanLearningBasedModelPredictive2019}.
-
-The ability to learn the plant's behaviour in new regions is very helpful in
-maintaining model performance over time, as its behaviour starts deviating and
-the original identified model goes further and further into the extrapolated
-regions.
-
-
-This project will therefore try to combine the use of online learning schemes
-with \acrlong{gp}es by implementing \acrlong{svgp}es, which provide means of
-employing \acrshort{gp} Models on larger datasets, and re-training the models
-every day at midnight to include all the historically available data.
-
-\clearpage