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20_Previous_Research.tex
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\section{Previous Research}
With the increase in computational power and availability of data over time the
accesibility of data-driven methods for System Identfication and Control has
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 {\color{red} Add citation to the Gaussian Process
for dynamic models textbook}
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 been studied before in the context
of Demand Response, {\color{orange} where the buildings are used for their heat
capacity in order to reduce the stress on energy provides during peak load times}
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.
% TODO: [Previous Research] Finish with need for adaptive schemes
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.
The ability to learn the plant's behaviour in new regions is very helpful in
maintaining model performance over time as the behaviour of the plants 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 using \acrlong{svgp}es, which provide means of using
\acrshort{gp} Models on larger datasets, and re-training the models every day at
midnight to include all the historically available data.
\clearpage