108 lines
5.5 KiB
TeX
108 lines
5.5 KiB
TeX
\section{Introduction}
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\subsection{Motivation}
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Buildings are a major consumer of energy, with more than 25\% of the total
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energy consumed in the EU coming from residential
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buildings~\cite{tsemekiditzeiranakiAnalysisEUResidential2019}. Combined with a
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steady increase in energy demand and stricter requirements on energy
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efficiency~\cite{europeancommission.jointresearchcentre.EnergyConsumptionEnergy2018},
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this amplifies the need for more accessible means of regulating energy usage of
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new and existing buildings.
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Data-driven methods of building identification and control prove very useful
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through their ease of implementation, foregoing the need of more complex
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physics-based models. On the flip side, additional attention is required to the
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design of these control schemes, as the results could vary greatly from one
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implementation to another.
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Gaussian Processes have been previously used to model building dynamics, but
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they are usually limited by a fixed computational budget. This limits the
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approaches that can be taken for identification and update of said models.
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Learning \acrshort{gp} models have also been previously used in the context of
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autonomous racing cars \cite{kabzanLearningBasedModelPredictive2019}, but there
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the Sparse \acrshort{gp} model was built on top of a white-box model and only
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responsible for fitting the unmodeled dynamics.
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\subsection{Previous Research}
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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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description of their use, along with the necessary theory and some example
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implementations is given in~\cite{kocijanModellingControlDynamic2016}.
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In~\cite{pleweSupervisoryModelPredictive2020}, a \acrlong{gp} Model with a
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\acrlong{rq} Kernel is used for temperature set point optimization.
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Gaussian Processes for building control have also been studied before in the
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context of Demand Response~\cite{nghiemDatadrivenDemandResponse2017,
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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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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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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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\subsection{Contribution}
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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 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 tries to combine the use of online learning control schemes with
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\acrlong{gp} Models through implementing \acrlong{svgp} Models. \acrshort{svgp}s
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provide means of extending the use of \acrshort{gp}s to larger datasets, thus
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enabling the periodic re-training of the model to include all the historically
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available data.
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\subsection{Project Outline}
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The main body of work can be divided in two parts: the development of a computer
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model of the \pdome\ building and the synthesis, validation and comparison of
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multiple control schemes using both classical \acrshort{gp}s, as well as
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\acrshort{svgp}s.
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Section~\ref{sec:gaussian_processes} provides the mathematical background for
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understanding \acrshort{gp}s, as well as the definition in very broad strokes of
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\acrshort{svgp}s and their differences from the classical implementation of
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\acrlong{gp}es. This information is later used for comparing their performances
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and outlining their respective pros and cons.
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Section~\ref{sec:CARNOT} goes into the details of the implementation of the
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\pdome\ computer model. The structure of the CARNOT model is described, and all
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the parameters required for the simulation are either directly sourced from
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existing literature, computed from available values or estimated using publicly
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available data from \textit{Google Maps}~\cite{GoogleMaps}.
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Following that, Section~\ref{sec:hyperparameters} discusses the choice of the
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hyperparameters for the \acrshort{gp} and \acrshort{svgp} models respectively in
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the context of their multi-step ahead simulation performance.
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The \acrlong{ocp} implemented in the \acrshort{mpc} controller is described in
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Section~\ref{sec:mpc_problem}, followed by a description of the complete
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controller implementation in Python in Section~\ref{sec:implementation}.
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Section~\ref{sec:results} presents and analyzes the results of full-year
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simulations for both \acrshort{gp} and \acrshort{svgp} models. A few variations
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on the initial \acrshort{svgp} model are further analyzed in order to identify
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the most important parameteres for long time operation.
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Finally, Section~\ref{sec:conclusion} provides a review of all the different
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implementation and discusses the possible improvements to both the current
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\acrshort{gp} and \acrshort{svgp} models.
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\clearpage
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