Final version of the report
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\section{Previous Research}
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With the increase in computational power and availability of data over time the
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accessibility of data-driven methods for System Identification and Control has
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also risen significantly.
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With the increase in computational power and availability of data over time,
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the accessibility of data-driven methods for System Identification and Control
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has also risen significantly.
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The idea of using Gaussian Processes as regression models for control of dynamic
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systems is not new, and has already been explored a number of times. A general
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@ -16,25 +16,29 @@ jainLearningControlUsing2018}, where the buildings are used for their heat
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capacity in order to reduce the stress on energy providers during peak load
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times.
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There are, however multiple limitations with these approaches.
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There are, however, multiple limitations with these approaches.
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In~\cite{nghiemDatadrivenDemandResponse2017} the model is only identified once,
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ignoring changes in weather or plant parameters which could lead to different
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ignoring changes in weather or plant parameters, which could lead to different
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dynamics. This is addressed in \cite{jainLearningControlUsing2018} by
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re-identifying the model every two weeks using new information. Another
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limitation is that of the scalability of the \acrshort{gp}s, which become
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prohibitively expensive from a computational point of view when too much data is
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added.
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Outside of the context of building control, Sparse \acrlong{gp}es have been used
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in autonomous racing in order to complement the physics-based model by fitting
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the unmodeled dynamics of the
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system~\cite{kabzanLearningBasedModelPredictive2019}.
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The ability to learn the plant's behaviour in new regions is very helpful in
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maintaining model performance over time as the behaviour of the plants starts
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deviating and the original identified model goes further and further into the
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extrapolated regions.
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maintaining model performance over time, as its behaviour starts deviating and
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the original identified model goes further and further into the extrapolated
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regions.
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This project will therefore try to combine the use of online learning schemes
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with \acrlong{gp}es by using \acrlong{svgp}es, which provide means of using
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\acrshort{gp} Models on larger datasets, and re-training the models every day at
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midnight to include all the historically available data.
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with \acrlong{gp}es by implementing \acrlong{svgp}es, which provide means of
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employing \acrshort{gp} Models on larger datasets, and re-training the models
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every day at midnight to include all the historically available data.
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\clearpage
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