radu/Master-Thesis
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Final version of the report

Browse source
This commit is contained in:
Radu C. Martin 2021-06-25 11:27:25 +02:00
parent c213d3064e
commit 7def536787
14 changed files with 343 additions and 242 deletions
Download patch file Download diff file Expand all files Collapse all files

26
20_Previous_Research.tex
View file

@ -1,7 +1,7 @@
\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.
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
@ -16,25 +16,29 @@ 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.
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
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 the behaviour of the plants starts
deviating and the original identified model goes further and further into the
extrapolated regions.
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 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.
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