Updated thesis
This commit is contained in:
Binary file not shown.
|
After Width: | Height: | Size: 74 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 71 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 70 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 70 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 74 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 74 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 69 KiB |
@@ -7,7 +7,7 @@ After training the different models and experimenting with various hyperparamete
|
||||
\begin{adjustbox}{width=\textwidth}
|
||||
\begin{tabular}{@{}clcccccc@{}}
|
||||
\toprule
|
||||
Features & Method & Model & \ac{MSE} & \ac{MAE} & \ac{CRPS} & Parameters \\
|
||||
Features & Method & Model & \acs{MSE} & \acs{MAE} & \acs{CRPS} & Parameters \\
|
||||
\midrule
|
||||
NRV & & & & & \\
|
||||
& \acs{AQR} & Linear & 39222.41 & 152.49 & 91.56 & 1,261 \\
|
||||
|
||||
@@ -83,3 +83,53 @@ In Figure \ref{fig:diffusion_intermediates}, multiple intermediate steps of the
|
||||
In Table \ref{tab:diffusion_results}, the results of the experiments for the diffusion model can be seen. The diffusion model that was used is a simple implementation of the Denoising Diffusion Probabilistic Model (DDPM). The model itself exists of multiple linear layers with ReLU activation functions. The diffusion steps were set to 300 for the experiments. This number was determined by doing a few experiments with more and fewer steps. The model performance did not improve when more steps were used. This parameter could be further optimized together with the other parameters to find the best-performing model. This would take a lot of time and is not the goal of this thesis.
|
||||
|
||||
The first observation that can be made is the higher error metrics when more input features are used. This is counterintuitive because the model has more information to generate the samples. The reason for this behavior is not immediately clear. One reason could be that the model conditioning is not optimal. Now the input features are passed to every layer of the model together with the time series that needs to be denoised. The model could be improved by using a more advanced conditioning mechanism like classifier guidance and classifier-free guidance.
|
||||
\\
|
||||
\begin{figure}[ht]
|
||||
\centering
|
||||
\begin{subfigure}[b]{0.49\textwidth}
|
||||
\includegraphics[width=\textwidth]{images/diffusion/results/samples/Diffusion_Test_Example_864.jpeg}
|
||||
\end{subfigure}
|
||||
\hfill
|
||||
\begin{subfigure}[b]{0.49\textwidth}
|
||||
\includegraphics[width=\textwidth]{images/diffusion/results/samples/Diffusion_Test_Example_4320.jpeg}
|
||||
\end{subfigure}
|
||||
|
||||
\begin{subfigure}[b]{0.49\textwidth}
|
||||
\includegraphics[width=\textwidth]{images/diffusion/results/samples/Diffusion_Test_Example_6336.jpeg}
|
||||
\end{subfigure}
|
||||
\hfill
|
||||
\begin{subfigure}[b]{0.49\textwidth}
|
||||
\includegraphics[width=\textwidth]{images/diffusion/results/samples/Diffusion_Test_Example_7008.jpeg}
|
||||
\end{subfigure}
|
||||
\caption{The plots show the generations for the examples from the test set. The diffusion model used to generate the samples consists of 2 layers with a hidden size of 1024. The number of denoising steps is set to 300. The confidence intervals shown in the plots are made using 100 samples. All the available input features are used which includes the \acs{NRV}, Load, Wind, \acs{PV} and \acs{NP} data.}
|
||||
\label{fig:diffusion_test_set_examples}
|
||||
\end{figure}
|
||||
|
||||
The examples of the test dataset are shown in Figure \ref{fig:diffusion_test_set_examples} using the diffusion model. The first observation that can be made from these plots is the narrow confidence intervals. The real NRV values are not always captured in the confidence intervals. Not enough variance is present in the generated samples. This issue originates from the overfitting during the training of the model. The model is, however, capable of capturing the general trend of the NRV data. In some cases, the peaks in the generated samples are very close to the real NRV values. This can be seen in the first example in the figure.
|
||||
|
||||
\begin{figure}[ht]
|
||||
\centering
|
||||
\begin{subfigure}[b]{0.49\textwidth}
|
||||
\includegraphics[width=\textwidth]{images/diffusion/results/samples/Diffusion_Test_Example_864_Only_NRV.jpeg}
|
||||
\end{subfigure}
|
||||
\hfill
|
||||
\begin{subfigure}[b]{0.49\textwidth}
|
||||
\includegraphics[width=\textwidth]{images/diffusion/results/samples/Diffusion_Test_Example_864.jpeg}
|
||||
\end{subfigure}
|
||||
|
||||
\begin{subfigure}[b]{0.49\textwidth}
|
||||
\includegraphics[width=\textwidth]{images/diffusion/results/samples/Diffusion_Test_Example_4320_Only_NRV.jpeg}
|
||||
\caption{Only NRV}
|
||||
\end{subfigure}
|
||||
\hfill
|
||||
\begin{subfigure}[b]{0.49\textwidth}
|
||||
\includegraphics[width=\textwidth]{images/diffusion/results/samples/Diffusion_Test_Example_4320.jpeg}
|
||||
\caption{NRV + Load + Wind + PV + NP}
|
||||
\end{subfigure}
|
||||
|
||||
|
||||
\caption{The plots show the generations for the first examples from the test set. Two diffusion models with 2 layers and 1024 hidden units are used. The first one is only conditioned on the NRV of the previous day while the second one uses all available input features.}
|
||||
\label{fig:diffusion_test_set_example_only_nrv_vs_all}
|
||||
\end{figure}
|
||||
|
||||
The plots in Figure \ref{fig:diffusion_test_set_example_only_nrv_vs_all} show the difference in generated samples when only the NRV data is used as input and when all available input features are used. The model that is only conditioned on the NRV data generates samples that do not have much variance. The confidence intervals are quite smooth and do not contain many peaks. The model trained using all available input features, on the other hand, has another behavior. The confidence intervals contain more peaks and the generated samples have more variance. This proves the model does indeed take the other input features into account when generating the samples. When looking at the metrics, the performance of the model that uses all input features is worse than the model that only uses the NRV data. The most obvious reason for this behavior is overfitting.
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -61,4 +61,89 @@
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces Comparison of the autoregressive and non-autoregressive linear model samples.\relax }}{26}{figure.caption.12}\protected@file@percent }
|
||||
\newlabel{fig:linear_model_sample_comparison}{{7}{26}{Comparison of the autoregressive and non-autoregressive linear model samples.\relax }{figure.caption.12}{}}
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Samples for two examples from the test set for the autoregressive and non-autoregressive linear model. The real NRV is shown in orange.\relax }}{27}{figure.caption.13}\protected@file@percent }
|
||||
\newlabel{fig:linear_model_samples_comparison}{{8}{27}{Samples for two examples from the test set for the autoregressive and non-autoregressive linear model. The real NRV is shown in orange.\relax }{figur
|
||||
\newlabel{fig:linear_model_samples_comparison}{{8}{27}{Samples for two examples from the test set for the autoregressive and non-autoregressive linear model. The real NRV is shown in orange.\relax }{figure.caption.13}{}}
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces Over/underestimation of the quantiles for the autoregressive and non-autoregressive linear models. Both the quantile performance for the training and test set are shown. The plots are generated using the input features NRV, Load, Wind, PV, Net Position, and the quarter embedding (only for the autoregressive model).\relax }}{28}{figure.caption.14}\protected@file@percent }
|
||||
\newlabel{fig:linear_model_quantile_over_underestimation}{{9}{28}{Over/underestimation of the quantiles for the autoregressive and non-autoregressive linear models. Both the quantile performance for the training and test set are shown. The plots are generated using the input features NRV, Load, Wind, PV, Net Position, and the quarter embedding (only for the autoregressive model).\relax }{figure.caption.14}{}}
|
||||
\@writefile{toc}{\contentsline {subsubsection}{\numberline {6.2.2}Non-Linear Model}{29}{subsubsection.6.2.2}\protected@file@percent }
|
||||
\@writefile{lot}{\contentsline {table}{\numberline {5}{\ignorespaces Non-linear Quantile Regression Model Architecture\relax }}{29}{table.caption.15}\protected@file@percent }
|
||||
\newlabel{tab:non_linear_model_architecture}{{5}{29}{Non-linear Quantile Regression Model Architecture\relax }{table.caption.15}{}}
|
||||
\@writefile{lot}{\contentsline {table}{\numberline {6}{\ignorespaces Non-linear quantile regression model results. All the models used a dropout of 0.2 .\relax }}{30}{table.caption.16}\protected@file@percent }
|
||||
\newlabel{tab:non_linear_model_results}{{6}{30}{Non-linear quantile regression model results. All the models used a dropout of 0.2 .\relax }{table.caption.16}{}}
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces Comparison of the autoregressive and non-autoregressive non-linear model examples.\relax }}{31}{figure.caption.17}\protected@file@percent }
|
||||
\newlabel{fig:non_linear_model_examples}{{10}{31}{Comparison of the autoregressive and non-autoregressive non-linear model examples.\relax }{figure.caption.17}{}}
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces Over/underestimation of the quantiles for the autoregressive and non-autoregressive non-linear models. Both the quantile performance for the training and test set are shown. The plots are generated using the input features NRV, Load, Wind, PV, Net Position, and the quarter embedding (only for the autoregressive model).\relax }}{32}{figure.caption.18}\protected@file@percent }
|
||||
\newlabel{fig:non-linear_model_quantile_over_underestimation}{{11}{32}{Over/underestimation of the quantiles for the autoregressive and non-autoregressive non-linear models. Both the quantile performance for the training and test set are shown. The plots are generated using the input features NRV, Load, Wind, PV, Net Position, and the quarter embedding (only for the autoregressive model).\relax }{figure.caption.18}{}}
|
||||
\@writefile{toc}{\contentsline {subsubsection}{\numberline {6.2.3}GRU Model}{32}{subsubsection.6.2.3}\protected@file@percent }
|
||||
\@writefile{lot}{\contentsline {table}{\numberline {7}{\ignorespaces GRU Model Architecture\relax }}{33}{table.caption.19}\protected@file@percent }
|
||||
\newlabel{tab:gru_model_architecture}{{7}{33}{GRU Model Architecture\relax }{table.caption.19}{}}
|
||||
\@writefile{lot}{\contentsline {table}{\numberline {8}{\ignorespaces Autoregressive GRU quantile regression model results. All the models used a dropout of 0.2 .\relax }}{34}{table.caption.20}\protected@file@percent }
|
||||
\newlabel{tab:autoregressive_gru_model_results}{{8}{34}{Autoregressive GRU quantile regression model results. All the models used a dropout of 0.2 .\relax }{table.caption.20}{}}
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {12}{\ignorespaces Comparison of the autoregressive and non-autoregressive GRU model examples.\relax }}{35}{figure.caption.21}\protected@file@percent }
|
||||
\newlabel{fig:gru_model_sample_comparison}{{12}{35}{Comparison of the autoregressive and non-autoregressive GRU model examples.\relax }{figure.caption.21}{}}
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {13}{\ignorespaces Over/underestimation of the quantiles for the autoregressive and non-autoregressive GRU models. Both the quantile performance for the training and test set are shown. The plots are generated using the input features NRV, Load, Wind, PV, Net Position, and the quarter embedding (only for the autoregressive model).\relax }}{36}{figure.caption.22}\protected@file@percent }
|
||||
\newlabel{fig:gru_model_quantile_over_underestimation}{{13}{36}{Over/underestimation of the quantiles for the autoregressive and non-autoregressive GRU models. Both the quantile performance for the training and test set are shown. The plots are generated using the input features NRV, Load, Wind, PV, Net Position, and the quarter embedding (only for the autoregressive model).\relax }{figure.caption.22}{}}
|
||||
\@writefile{toc}{\contentsline {subsection}{\numberline {6.3}Diffusion}{36}{subsection.6.3}\protected@file@percent }
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces Intermediate steps of the diffusion model for example 864 from the test set. The confidence intervals shown in the plots are made using 100 samples.\relax }}{38}{figure.caption.23}\protected@file@percent }
|
||||
\newlabel{fig:diffusion_intermediates}{{14}{38}{Intermediate steps of the diffusion model for example 864 from the test set. The confidence intervals shown in the plots are made using 100 samples.\relax }{figure.caption.23}{}}
|
||||
\@writefile{lot}{\contentsline {table}{\numberline {9}{\ignorespaces Simple diffusion model results.\relax }}{38}{table.caption.24}\protected@file@percent }
|
||||
\newlabel{tab:diffusion_results}{{9}{38}{Simple diffusion model results.\relax }{table.caption.24}{}}
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {15}{\ignorespaces The plots show the generations for the examples from the test set. The diffusion model used to generate the samples consists of 2 layers with a hidden size of 1024. The number of denoising steps is set to 300. The confidence intervals shown in the plots are made using 100 samples. All the available input features are used which includes the \acs {NRV}, Load, Wind, \acs {PV} and \acs {NP} data.\relax }}{39}{figure.caption.25}\protected@file@percent }
|
||||
\newlabel{fig:diffusion_test_set_examples}{{15}{39}{The plots show the generations for the examples from the test set. The diffusion model used to generate the samples consists of 2 layers with a hidden size of 1024. The number of denoising steps is set to 300. The confidence intervals shown in the plots are made using 100 samples. All the available input features are used which includes the \acs {NRV}, Load, Wind, \acs {PV} and \acs {NP} data.\relax }{figure.caption.25}{}}
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {16}{\ignorespaces The plots show the generations for the first examples from the test set. Two diffusion models with 2 layers and 1024 hidden units are used. The first one is only conditioned on the NRV of the previous day while the second one uses all available input features.\relax }}{40}{figure.caption.26}\protected@file@percent }
|
||||
\newlabel{fig:diffusion_test_set_example_only_nrv_vs_all}{{16}{40}{The plots show the generations for the first examples from the test set. Two diffusion models with 2 layers and 1024 hidden units are used. The first one is only conditioned on the NRV of the previous day while the second one uses all available input features.\relax }{figure.caption.26}{}}
|
||||
\@writefile{toc}{\contentsline {subsection}{\numberline {6.4}Comparison}{40}{subsection.6.4}\protected@file@percent }
|
||||
\ACRO{recordpage}{MSE}{41}{1}{40}
|
||||
\ACRO{recordpage}{MAE}{41}{1}{40}
|
||||
\ACRO{recordpage}{CRPS}{41}{1}{40}
|
||||
\@writefile{lot}{\contentsline {table}{\numberline {10}{\ignorespaces Comparison of the different models using the \ac {MSE}, \ac {MAE} and \ac {CRPS} metrics. The best-performing models for a certain type are selected based on the \ac {CRPS}.\relax }}{41}{table.caption.27}\protected@file@percent }
|
||||
\newlabel{tab:model_comparison}{{10}{41}{Comparison of the different models using the \ac {MSE}, \ac {MAE} and \ac {CRPS} metrics. The best-performing models for a certain type are selected based on the \ac {CRPS}.\relax }{table.caption.27}{}}
|
||||
\ACRO{recordpage}{NAQR}{42}{1}{41}
|
||||
\ACRO{recordpage}{MSE}{42}{1}{41}
|
||||
\ACRO{recordpage}{MAE}{42}{1}{41}
|
||||
\ACRO{recordpage}{CRPS}{42}{1}{41}
|
||||
\ACRO{recordpage}{MSE}{42}{1}{41}
|
||||
\ACRO{recordpage}{MAE}{42}{1}{41}
|
||||
\ACRO{recordpage}{MSE}{42}{1}{41}
|
||||
\ACRO{recordpage}{MAE}{42}{1}{41}
|
||||
\@writefile{lof}{\contentsline {figure}{\numberline {17}{\ignorespaces Comparison of the autoregressive linear and GRU model\relax }}{42}{figure.caption.28}\protected@file@percent }
|
||||
\newlabel{fig:ar_linear_gru_comparison}{{17}{42}{Comparison of the autoregressive linear and GRU model\relax }{figure.caption.28}{}}
|
||||
\@writefile{toc}{\contentsline {section}{\numberline {7}Policies for battery optimization}{43}{section.7}\protected@file@percent }
|
||||
\@writefile{toc}{\contentsline {subsection}{\numberline {7.1}Baselines}{43}{subsection.7.1}\protected@file@percent }
|
||||
\ACRO{recordpage}{NRV}{44}{1}{43}
|
||||
\ACRO{recordpage}{NRV}{44}{1}{43}
|
||||
\ACRO{recordpage}{NRV}{45}{1}{44}
|
||||
\ACRO{recordpage}{NRV}{45}{1}{44}
|
||||
\ACRO{recordpage}{NRV}{45}{1}{44}
|
||||
\ACRO{recordpage}{NRV}{45}{1}{44}
|
||||
\ACRO{recordpage}{NRV}{45}{1}{44}
|
||||
\ACRO{recordpage}{NRV}{45}{1}{44}
|
||||
\ACRO{recordpage}{NRV}{45}{1}{44}
|
||||
\@writefile{lot}{\contentsline {table}{\numberline {11}{\ignorespaces Results of the baseline policies on the test set. \relax }}{44}{table.caption.29}\protected@file@percent }
|
||||
\newlabel{tab:fixed_thresholds}{{11}{44}{Results of the baseline policies on the test set. \relax }{table.caption.29}{}}
|
||||
\@writefile{toc}{\contentsline {subsection}{\numberline {7.2}Policy using generated NRV samples}{44}{subsection.7.2}\protected@file@percent }
|
||||
\ACRO{total-barriers}{1}
|
||||
\abx@aux@page{6}{46}
|
||||
\abx@aux@page{7}{46}
|
||||
\abx@aux@page{8}{46}
|
||||
\abx@aux@page{9}{46}
|
||||
\ACRO{usage}{QR=={0}}
|
||||
\ACRO{usage}{AQR=={0}}
|
||||
\ACRO{usage}{NAQR=={1}}
|
||||
\ACRO{usage}{MSE=={4}}
|
||||
\ACRO{usage}{MAE=={4}}
|
||||
\ACRO{usage}{CRPS=={2}}
|
||||
\ACRO{usage}{NRV=={9}}
|
||||
\ACRO{usage}{PV=={0}}
|
||||
\ACRO{usage}{NP=={0}}
|
||||
\ACRO{pages}{NAQR=={42@1@41}}
|
||||
\ACRO{pages}{CRPS=={41@1@40|42@1@41}}
|
||||
\ACRO{pages}{MSE=={41@1@40|42@1@41}}
|
||||
\ACRO{pages}{MAE=={41@1@40|42@1@41}}
|
||||
\ACRO{pages}{NRV=={44@1@43|45@1@44}}
|
||||
\abx@aux@read@bbl@mdfivesum{5DC935CC8C8FAB8A3CAF97A486ED2386}
|
||||
\abx@aux@read@bblrerun
|
||||
\abx@aux@defaultrefcontext{0}{dumas_deep_2022}{nyt/global//global/global}
|
||||
\abx@aux@defaultrefcontext{0}{lu_scenarios_2022}{nyt/global//global/global}
|
||||
\abx@aux@defaultrefcontext{0}{poggi_electricity_2023}{nyt/global//global/global}
|
||||
\abx@aux@defaultrefcontext{0}{weron_electricity_2014}{nyt/global//global/global}
|
||||
\gdef \@abspage@last{47}
|
||||
|
||||
@@ -2818,4 +2818,70 @@
|
||||
<bcf:entrytype>article</bcf:entrytype>
|
||||
<bcf:entrytype>report</bcf:entrytype>
|
||||
<bcf:constraint type="mandatory">
|
||||
<bcf:field>
|
||||
<bcf:field>author</bcf:field>
|
||||
<bcf:field>title</bcf:field>
|
||||
</bcf:constraint>
|
||||
</bcf:constraints>
|
||||
</bcf:datamodel>
|
||||
<!-- CITATION DATA -->
|
||||
<!-- SECTION 0 -->
|
||||
<bcf:bibdata section="0">
|
||||
<bcf:datasource type="file" datatype="bibtex" glob="false">./references.bib</bcf:datasource>
|
||||
</bcf:bibdata>
|
||||
<bcf:section number="0">
|
||||
<bcf:citekey order="1" intorder="1">weron_electricity_2014</bcf:citekey>
|
||||
<bcf:citekey order="2" intorder="1">poggi_electricity_2023</bcf:citekey>
|
||||
<bcf:citekey order="3" intorder="1">lu_scenarios_2022</bcf:citekey>
|
||||
<bcf:citekey order="4" intorder="1">dumas_deep_2022</bcf:citekey>
|
||||
<bcf:citekey order="5" intorder="1">rasul_autoregressive_2021</bcf:citekey>
|
||||
<bcf:citekey order="6" intorder="1">dumas_deep_2022</bcf:citekey>
|
||||
</bcf:section>
|
||||
<!-- SORTING TEMPLATES -->
|
||||
<bcf:sortingtemplate name="nyt">
|
||||
<bcf:sort order="1">
|
||||
<bcf:sortitem order="1">presort</bcf:sortitem>
|
||||
</bcf:sort>
|
||||
<bcf:sort order="2" final="1">
|
||||
<bcf:sortitem order="1">sortkey</bcf:sortitem>
|
||||
</bcf:sort>
|
||||
<bcf:sort order="3">
|
||||
<bcf:sortitem order="1">sortname</bcf:sortitem>
|
||||
<bcf:sortitem order="2">author</bcf:sortitem>
|
||||
<bcf:sortitem order="3">editor</bcf:sortitem>
|
||||
<bcf:sortitem order="4">translator</bcf:sortitem>
|
||||
<bcf:sortitem order="5">sorttitle</bcf:sortitem>
|
||||
<bcf:sortitem order="6">title</bcf:sortitem>
|
||||
</bcf:sort>
|
||||
<bcf:sort order="4">
|
||||
<bcf:sortitem order="1">sortyear</bcf:sortitem>
|
||||
<bcf:sortitem order="2">year</bcf:sortitem>
|
||||
</bcf:sort>
|
||||
<bcf:sort order="5">
|
||||
<bcf:sortitem order="1">sorttitle</bcf:sortitem>
|
||||
<bcf:sortitem order="2">title</bcf:sortitem>
|
||||
</bcf:sort>
|
||||
<bcf:sort order="6">
|
||||
<bcf:sortitem order="1">volume</bcf:sortitem>
|
||||
<bcf:sortitem literal="1" order="2">0</bcf:sortitem>
|
||||
</bcf:sort>
|
||||
</bcf:sortingtemplate>
|
||||
<!-- DATALISTS -->
|
||||
<bcf:datalist section="0"
|
||||
name="nyt/apasortcite//global/global"
|
||||
type="entry"
|
||||
sortingtemplatename="nyt"
|
||||
sortingnamekeytemplatename="apasortcite"
|
||||
labelprefix=""
|
||||
uniquenametemplatename="global"
|
||||
labelalphanametemplatename="global">
|
||||
</bcf:datalist>
|
||||
<bcf:datalist section="0"
|
||||
name="nyt/global//global/global"
|
||||
type="entry"
|
||||
sortingtemplatename="nyt"
|
||||
sortingnamekeytemplatename="global"
|
||||
labelprefix=""
|
||||
uniquenametemplatename="global"
|
||||
labelalphanametemplatename="global">
|
||||
</bcf:datalist>
|
||||
</bcf:controlfile>
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) (preloaded format=pdflatex 2023.9.17) 11 MAY 2024 02:10
|
||||
This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) (preloaded format=pdflatex 2023.9.17) 11 MAY 2024 19:10
|
||||
entering extended mode
|
||||
restricted \write18 enabled.
|
||||
file:line:error style messages enabled.
|
||||
@@ -1557,4 +1557,288 @@ Package pdftex.def Info: images/quantile_regression/quantile_performance/AQR_Qua
|
||||
<images/quantile_regression/quantile_performance/AQR_Quantile_Performance_Test.jpeg, id=373, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/AQR_Quantile_Performance_Test.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/AQR_Quantile_Performance_Test.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/AQR_Quantile_Pe
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/AQR_Quantile_Performance_Test.jpeg used on input line 156.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
<images/quantile_regression/quantile_performance/NAQR_Quantile_Performance_Training.jpeg, id=374, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/NAQR_Quantile_Performance_Training.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/NAQR_Quantile_Performance_Training.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/NAQR_Quantile_Performance_Training.jpeg used on input line 161.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
<images/quantile_regression/quantile_performance/NAQR_Quantile_Performance_Test.jpeg, id=375, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/NAQR_Quantile_Performance_Test.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/NAQR_Quantile_Performance_Test.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/NAQR_Quantile_Performance_Test.jpeg used on input line 166.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
[27 <./images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP-QE-Example_864_samples.png> <./images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Example_864_samples.png> <./images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP-QE-Example_4320_samples.png> <./images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Example_4320_samples.png>]) (./sections/results/models/non-linear.tex [28 <./images/quantile_regression/quantile_performance/AQR_Quantile_Performance_Training.jpeg> <./images/quantile_regression/quantile_performance/AQR_Quantile_Performance_Test.jpeg> <./images/quantile_regression/quantile_performance/NAQR_Quantile_Performance_Training.jpeg> <./images/quantile_regression/quantile_performance/NAQR_Quantile_Performance_Test.jpeg>] [29{/usr/local/texlive/2023/texmf-dist/fonts/enc/dvips/libertine/lbtn_7grukw.enc}]
|
||||
<images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_864.png, id=399, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_864.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_864.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_864.png used on input line 78.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_864.png, id=400, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_864.png Graphic file (type png)
|
||||
<use images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_864.png>
|
||||
Package pdftex.def Info: images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_864.png used on input line 82.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_4320.png, id=401, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_4320.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_4320.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_4320.png used on input line 85.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_4320.png, id=402, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_4320.png Graphic file (type png)
|
||||
<use images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_4320.png>
|
||||
Package pdftex.def Info: images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_4320.png used on input line 89.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_6336.png, id=403, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_6336.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_6336.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_6336.png used on input line 92.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_6336.png, id=404, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_6336.png Graphic file (type png)
|
||||
<use images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_6336.png>
|
||||
Package pdftex.def Info: images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_6336.png used on input line 96.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_7008.png, id=405, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_7008.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_7008.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_7008.png used on input line 99.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_7008.png, id=406, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_7008.png Graphic file (type png)
|
||||
<use images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_7008.png>
|
||||
Package pdftex.def Info: images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_7008.png used on input line 104.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
[30]
|
||||
<images/quantile_regression/quantile_performance/AQR_NL_Quantile_Performance_Training.jpeg, id=414, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/AQR_NL_Quantile_Performance_Training.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/AQR_NL_Quantile_Performance_Training.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/AQR_NL_Quantile_Performance_Training.jpeg used on input line 117.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
<images/quantile_regression/quantile_performance/AQR_NL_Quantile_Performance_Test.jpeg, id=415, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/AQR_NL_Quantile_Performance_Test.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/AQR_NL_Quantile_Performance_Test.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/AQR_NL_Quantile_Performance_Test.jpeg used on input line 122.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
<images/quantile_regression/quantile_performance/NAQR_NL_Quantile_Performance_Training.jpeg, id=416, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/NAQR_NL_Quantile_Performance_Training.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/NAQR_NL_Quantile_Performance_Training.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/NAQR_NL_Quantile_Performance_Training.jpeg used on input line 127.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
<images/quantile_regression/quantile_performance/NAQR_NL_Quantile_Performance_Test.jpeg, id=417, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/NAQR_NL_Quantile_Performance_Test.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/NAQR_NL_Quantile_Performance_Test.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/NAQR_NL_Quantile_Performance_Test.jpeg used on input line 132.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
|
||||
Underfull \hbox (badness 4582) in paragraph at lines 135--135
|
||||
[]\T1/LinuxLibertineT-TLF/m/n/12 Figure 11: |Over/underestimation of the quan-tiles for the au-tore-gres-sive and non-
|
||||
[]
|
||||
|
||||
) [31 <./images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_864.png> <./images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_864.png> <./images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_4320.png> <./images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_4320.png> <./images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_6336.png> <./images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_6336.png> <./images/quantile_regression/aqr_non_linear_model_samples/AQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_7008.png> <./images/quantile_regression/naqr_non_linear_model_samples/NAQR_NL_NRV_Load_Wind_PV_NP_QE-Sample_7008.png>] (./sections/results/models/gru.tex [32 <./images/quantile_regression/quantile_performance/AQR_NL_Quantile_Performance_Training.jpeg> <./images/quantile_regression/quantile_performance/AQR_NL_Quantile_Performance_Test.jpeg> <./images/quantile_regression/quantile_performance/NAQR_NL_Quantile_Performance_Training.jpeg> <./images/quantile_regression/quantile_performance/NAQR_NL_Quantile_Performance_Test.jpeg>] [33]
|
||||
<images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png, id=445, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png used on input line 75.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png, id=446, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png Graphic file (type png)
|
||||
<use images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png>
|
||||
Package pdftex.def Info: images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png used on input line 79.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png, id=447, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png used on input line 82.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png, id=448, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png Graphic file (type png)
|
||||
<use images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png>
|
||||
Package pdftex.def Info: images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png used on input line 86.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png, id=449, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png used on input line 89.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png, id=450, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png Graphic file (type png)
|
||||
<use images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png>
|
||||
Package pdftex.def Info: images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png used on input line 93.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png, id=451, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png used on input line 96.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
<images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png, id=452, 1180.8918pt x 595.5048pt>
|
||||
File: images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png Graphic file (type png)
|
||||
<use images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png>
|
||||
Package pdftex.def Info: images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png used on input line 101.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
[34]
|
||||
<images/quantile_regression/quantile_performance/AQR_GRU_QP_Train.jpeg, id=458, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/AQR_GRU_QP_Train.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/AQR_GRU_QP_Train.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/AQR_GRU_QP_Train.jpeg used on input line 114.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
<images/quantile_regression/quantile_performance/AQR_GRU_QP_Test.jpeg, id=459, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/AQR_GRU_QP_Test.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/AQR_GRU_QP_Test.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/AQR_GRU_QP_Test.jpeg used on input line 119.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
<images/quantile_regression/quantile_performance/NAQR_GRU_QP_Train.jpeg, id=460, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/NAQR_GRU_QP_Train.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/NAQR_GRU_QP_Train.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/NAQR_GRU_QP_Train.jpeg used on input line 124.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
<images/quantile_regression/quantile_performance/NAQR_GRU_QP_Test.jpeg, id=461, 883.8621pt x 609.9588pt>
|
||||
File: images/quantile_regression/quantile_performance/NAQR_GRU_QP_Test.jpeg Graphic file (type jpg)
|
||||
<use images/quantile_regression/quantile_performance/NAQR_GRU_QP_Test.jpeg>
|
||||
Package pdftex.def Info: images/quantile_regression/quantile_performance/NAQR_GRU_QP_Test.jpeg used on input line 129.
|
||||
(pdftex.def) Requested size: 223.07211pt x 153.94125pt.
|
||||
|
||||
Underfull \hbox (badness 4582) in paragraph at lines 132--132
|
||||
[]\T1/LinuxLibertineT-TLF/m/n/12 Figure 13: |Over/underestimation of the quan-tiles for the au-tore-gres-sive and non-
|
||||
[]
|
||||
|
||||
) [35 <./images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png> <./images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png> <./images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png> <./images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png> <./images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png> <./images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png> <./images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png> <./images/quantile_regression/naqr_gru_model_examples/NAQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png>] (./sections/results/models/diffusion.tex [36 <./images/quantile_regression/quantile_performance/AQR_GRU_QP_Train.jpeg> <./images/quantile_regression/quantile_performance/AQR_GRU_QP_Test.jpeg> <./images/quantile_regression/quantile_performance/NAQR_GRU_QP_Train.jpeg> <./images/quantile_regression/quantile_performance/NAQR_GRU_QP_Test.jpeg>]
|
||||
<images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 1_00000000.jpeg, id=483, 1166.4378pt x 581.0508pt>
|
||||
File: images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 1_00000000.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 1_00000000.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 1_00000000.jpeg used on input line 20.
|
||||
(pdftex.def) Requested size: 204.85846pt x 102.04892pt.
|
||||
<images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 2_00000000.jpeg, id=484, 1166.4378pt x 581.0508pt>
|
||||
File: images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 2_00000000.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 2_00000000.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 2_00000000.jpeg used on input line 22.
|
||||
(pdftex.def) Requested size: 204.85846pt x 102.04892pt.
|
||||
<images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 3_00000000.jpeg, id=485, 1166.4378pt x 581.0508pt>
|
||||
File: images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 3_00000000.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 3_00000000.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 3_00000000.jpeg used on input line 27.
|
||||
(pdftex.def) Requested size: 204.85846pt x 102.04892pt.
|
||||
<images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 4_00000000.jpeg, id=486, 1166.4378pt x 581.0508pt>
|
||||
File: images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 4_00000000.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 4_00000000.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 4_00000000.jpeg used on input line 29.
|
||||
(pdftex.def) Requested size: 204.85846pt x 102.04892pt.
|
||||
|
||||
|
||||
LaTeX Warning: `h' float specifier changed to `ht'.
|
||||
|
||||
[37]
|
||||
Overfull \vbox (38.77904pt too high) has occurred while \output is active []
|
||||
|
||||
[38 <./images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 1_00000000.jpeg> <./images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 2_00000000.jpeg> <./images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 3_00000000.jpeg> <./images/diffusion/results/intermediates/Testing Intermediates 864_Sample intermediate 4_00000000.jpeg>]
|
||||
<images/diffusion/results/samples/Diffusion_Test_Example_864.jpeg, id=500, 1166.4378pt x 581.0508pt>
|
||||
File: images/diffusion/results/samples/Diffusion_Test_Example_864.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/samples/Diffusion_Test_Example_864.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/samples/Diffusion_Test_Example_864.jpeg used on input line 90.
|
||||
(pdftex.def) Requested size: 223.07211pt x 111.11894pt.
|
||||
<images/diffusion/results/samples/Diffusion_Test_Example_4320.jpeg, id=501, 1166.4378pt x 581.0508pt>
|
||||
File: images/diffusion/results/samples/Diffusion_Test_Example_4320.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/samples/Diffusion_Test_Example_4320.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/samples/Diffusion_Test_Example_4320.jpeg used on input line 94.
|
||||
(pdftex.def) Requested size: 223.07211pt x 111.11894pt.
|
||||
<images/diffusion/results/samples/Diffusion_Test_Example_6336.jpeg, id=502, 1166.4378pt x 581.0508pt>
|
||||
File: images/diffusion/results/samples/Diffusion_Test_Example_6336.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/samples/Diffusion_Test_Example_6336.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/samples/Diffusion_Test_Example_6336.jpeg used on input line 98.
|
||||
(pdftex.def) Requested size: 223.07211pt x 111.11894pt.
|
||||
<images/diffusion/results/samples/Diffusion_Test_Example_7008.jpeg, id=503, 1166.4378pt x 581.0508pt>
|
||||
File: images/diffusion/results/samples/Diffusion_Test_Example_7008.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/samples/Diffusion_Test_Example_7008.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/samples/Diffusion_Test_Example_7008.jpeg used on input line 102.
|
||||
(pdftex.def) Requested size: 223.07211pt x 111.11894pt.
|
||||
[39 <./images/diffusion/results/samples/Diffusion_Test_Example_864.jpeg> <./images/diffusion/results/samples/Diffusion_Test_Example_4320.jpeg> <./images/diffusion/results/samples/Diffusion_Test_Example_6336.jpeg> <./images/diffusion/results/samples/Diffusion_Test_Example_7008.jpeg>]
|
||||
<images/diffusion/results/samples/Diffusion_Test_Example_864_Only_NRV.jpeg, id=510, 1166.4378pt x 581.0508pt>
|
||||
File: images/diffusion/results/samples/Diffusion_Test_Example_864_Only_NRV.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/samples/Diffusion_Test_Example_864_Only_NRV.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/samples/Diffusion_Test_Example_864_Only_NRV.jpeg used on input line 113.
|
||||
(pdftex.def) Requested size: 223.07211pt x 111.11894pt.
|
||||
File: images/diffusion/results/samples/Diffusion_Test_Example_864.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/samples/Diffusion_Test_Example_864.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/samples/Diffusion_Test_Example_864.jpeg used on input line 117.
|
||||
(pdftex.def) Requested size: 223.07211pt x 111.11894pt.
|
||||
<images/diffusion/results/samples/Diffusion_Test_Example_4320_Only_NRV.jpeg, id=511, 1166.4378pt x 581.0508pt>
|
||||
File: images/diffusion/results/samples/Diffusion_Test_Example_4320_Only_NRV.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/samples/Diffusion_Test_Example_4320_Only_NRV.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/samples/Diffusion_Test_Example_4320_Only_NRV.jpeg used on input line 121.
|
||||
(pdftex.def) Requested size: 223.07211pt x 111.11894pt.
|
||||
File: images/diffusion/results/samples/Diffusion_Test_Example_4320.jpeg Graphic file (type jpg)
|
||||
<use images/diffusion/results/samples/Diffusion_Test_Example_4320.jpeg>
|
||||
Package pdftex.def Info: images/diffusion/results/samples/Diffusion_Test_Example_4320.jpeg used on input line 126.
|
||||
(pdftex.def) Requested size: 223.07211pt x 111.11894pt.
|
||||
) (./sections/results/models/comparison.tex [40 <./images/diffusion/results/samples/Diffusion_Test_Example_864_Only_NRV.jpeg> <./images/diffusion/results/samples/Diffusion_Test_Example_4320_Only_NRV.jpeg>] [41]
|
||||
File: images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_864.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_864.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_864.png used on input line 46.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
File: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_864.png used on input line 50.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
File: images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_4320.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_4320.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_4320.png used on input line 53.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
File: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_4320.png used on input line 57.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
File: images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_6336.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_6336.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_6336.png used on input line 60.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
File: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_6336.png used on input line 64.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
File: images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_7008.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_7008.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_7008.png used on input line 67.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
File: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png Graphic file (type png)
|
||||
<use images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png>
|
||||
Package pdftex.def Info: images/quantile_regression/aqr_gru_model_examples/AQR_GRU_NRV_Load_Wind_PV_NP_QE-Sample_7008.png used on input line 72.
|
||||
(pdftex.def) Requested size: 223.07211pt x 112.49284pt.
|
||||
) [42] (./sections/results/policies/baselines.tex [43]
|
||||
Underfull \hbox (badness 10000) in paragraph at lines 6--7
|
||||
|
||||
[]
|
||||
|
||||
LaTeX Font Info: Font shape `TS1/LinuxLibertineT-TLF/b/n' will be
|
||||
(Font) scaled to size 12.0pt on input line 12.
|
||||
) (./sections/results/policies/nrv_samples_policy.tex)) [44{/usr/local/texlive/2023/texmf-dist/fonts/enc/dvips/libertine/lbtn_7f4ce4.enc}] [45] [46] (./verslag.aux (./sections/introduction.aux) (./sections/background.aux) (./sections/policies.aux) (./sections/literature_study.aux))
|
||||
|
||||
LaTeX Warning: There were undefined references.
|
||||
|
||||
Package rerunfilecheck Info: File `verslag.out' has not changed.
|
||||
(rerunfilecheck) Checksum: DDC6B7AA614B7FD7023FF18C36539DD6;4636.
|
||||
|
||||
Package biblatex Warning: Please (re)run Biber on the file:
|
||||
(biblatex) verslag
|
||||
(biblatex) and rerun LaTeX afterwards.
|
||||
|
||||
Package logreq Info: Writing requests to 'verslag.run.xml'.
|
||||
\openout1 = `verslag.run.xml'.
|
||||
|
||||
)
|
||||
Here is how much of TeX's memory you used:
|
||||
41847 strings out of 476025
|
||||
871711 string characters out of 5790017
|
||||
1884388 words of memory out of 5000000
|
||||
61434 multiletter control sequences out of 15000+600000
|
||||
606967 words of font info for 100 fonts, out of 8000000 for 9000
|
||||
1141 hyphenation exceptions out of 8191
|
||||
84i,16n,131p,2100b,5180s stack positions out of 10000i,1000n,20000p,200000b,200000s
|
||||
</Users/victormylle/Library/texlive/2023/texmf-var/fonts/pk/ljfour/public/bbm/bbm12.600pk></usr/local/texlive/2023/texmf-dist/fonts/type1/public/libertinust1math/LibertinusT1Math.pfb></usr/local/texlive/2023/texmf-dist/fonts/type1/public/libertine/LinBiolinumT.pfb></usr/local/texlive/2023/texmf-dist/fonts/type1/public/libertine/LinBiolinumTB.pfb></usr/local/texlive/2023/texmf-dist/fonts/type1/public/libertine/LinLibertineT.pfb></usr/local/texlive/2023/texmf-dist/fonts/type1/public/libertine/LinLibertineTB.pfb></usr/local/texlive/2023/texmf-dist/fonts/type1/public/libertine/LinLibertineTI.pfb></usr/local/texlive/2023/texmf-dist/fonts/type1/public/stix/stix-mathcal.pfb>
|
||||
Output written on verslag.pdf (47 pages, 8675131 bytes).
|
||||
PDF statistics:
|
||||
646 PDF objects out of 1000 (max. 8388607)
|
||||
475 compressed objects within 5 object streams
|
||||
119 named destinations out of 1000 (max. 500000)
|
||||
556 words of extra memory for PDF output out of 10000 (max. 10000000)
|
||||
|
||||
|
||||
@@ -0,0 +1,31 @@
|
||||
\BOOKMARK [1][-]{section.1}{\376\377\000I\000n\000t\000r\000o\000d\000u\000c\000t\000i\000o\000n}{}% 1
|
||||
\BOOKMARK [1][-]{section.2}{\376\377\000E\000l\000e\000c\000t\000r\000i\000c\000i\000t\000y\000\040\000m\000a\000r\000k\000e\000t}{}% 2
|
||||
\BOOKMARK [1][-]{section.3}{\376\377\000G\000e\000n\000e\000r\000a\000t\000i\000v\000e\000\040\000m\000o\000d\000e\000l\000i\000n\000g}{}% 3
|
||||
\BOOKMARK [2][-]{subsection.3.1}{\376\377\000Q\000u\000a\000n\000t\000i\000l\000e\000\040\000R\000e\000g\000r\000e\000s\000s\000i\000o\000n}{section.3}% 4
|
||||
\BOOKMARK [2][-]{subsection.3.2}{\376\377\000A\000u\000t\000o\000r\000e\000g\000r\000e\000s\000s\000i\000v\000e\000\040\000v\000s\000\040\000N\000o\000n\000-\000A\000u\000t\000o\000r\000e\000g\000r\000e\000s\000s\000i\000v\000e\000\040\000m\000o\000d\000e\000l\000s}{section.3}% 5
|
||||
\BOOKMARK [2][-]{subsection.3.3}{\376\377\000M\000o\000d\000e\000l\000\040\000T\000y\000p\000e\000s}{section.3}% 6
|
||||
\BOOKMARK [3][-]{subsubsection.3.3.1}{\376\377\000L\000i\000n\000e\000a\000r\000\040\000M\000o\000d\000e\000l}{subsection.3.3}% 7
|
||||
\BOOKMARK [3][-]{subsubsection.3.3.2}{\376\377\000N\000o\000n\000-\000L\000i\000n\000e\000a\000r\000\040\000M\000o\000d\000e\000l}{subsection.3.3}% 8
|
||||
\BOOKMARK [3][-]{subsubsection.3.3.3}{\376\377\000R\000e\000c\000u\000r\000r\000e\000n\000t\000\040\000N\000e\000u\000r\000a\000l\000\040\000N\000e\000t\000w\000o\000r\000k\000\040\000\050\000R\000N\000N\000\051}{subsection.3.3}% 9
|
||||
\BOOKMARK [2][-]{subsection.3.4}{\376\377\000D\000i\000f\000f\000u\000s\000i\000o\000n\000\040\000m\000o\000d\000e\000l\000s}{section.3}% 10
|
||||
\BOOKMARK [3][-]{subsubsection.3.4.1}{\376\377\000O\000v\000e\000r\000v\000i\000e\000w}{subsection.3.4}% 11
|
||||
\BOOKMARK [3][-]{subsubsection.3.4.2}{\376\377\000A\000p\000p\000l\000i\000c\000a\000t\000i\000o\000n\000s}{subsection.3.4}% 12
|
||||
\BOOKMARK [3][-]{subsubsection.3.4.3}{\376\377\000G\000e\000n\000e\000r\000a\000t\000i\000o\000n\000\040\000p\000r\000o\000c\000e\000s\000s}{subsection.3.4}% 13
|
||||
\BOOKMARK [2][-]{subsection.3.5}{\376\377\000E\000v\000a\000l\000u\000a\000t\000i\000o\000n}{section.3}% 14
|
||||
\BOOKMARK [1][-]{section.4}{\376\377\000P\000o\000l\000i\000c\000i\000e\000s}{}% 15
|
||||
\BOOKMARK [2][-]{subsection.4.1}{\376\377\000B\000a\000s\000e\000l\000i\000n\000e\000s}{section.4}% 16
|
||||
\BOOKMARK [2][-]{subsection.4.2}{\376\377\000P\000o\000l\000i\000c\000i\000e\000s\000\040\000b\000a\000s\000e\000d\000\040\000o\000n\000\040\000N\000R\000V\000\040\000g\000e\000n\000e\000r\000a\000t\000i\000o\000n\000s}{section.4}% 17
|
||||
\BOOKMARK [1][-]{section.5}{\376\377\000L\000i\000t\000e\000r\000a\000t\000u\000r\000e\000\040\000S\000t\000u\000d\000y}{}% 18
|
||||
\BOOKMARK [2][-]{subsection.5.1}{\376\377\000E\000l\000e\000c\000t\000r\000i\000c\000i\000t\000y\000\040\000P\000r\000i\000c\000e\000\040\000F\000o\000r\000e\000c\000a\000s\000t\000i\000n\000g}{section.5}% 19
|
||||
\BOOKMARK [2][-]{subsection.5.2}{\376\377\000P\000o\000l\000i\000c\000i\000e\000s\000\040\000f\000o\000r\000\040\000B\000a\000t\000t\000e\000r\000y\000\040\000O\000p\000t\000i\000m\000i\000z\000a\000t\000i\000o\000n}{section.5}% 20
|
||||
\BOOKMARK [1][-]{section.6}{\376\377\000R\000e\000s\000u\000l\000t\000s\000\040\000\046\000\040\000D\000i\000s\000c\000u\000s\000s\000i\000o\000n}{}% 21
|
||||
\BOOKMARK [2][-]{subsection.6.1}{\376\377\000D\000a\000t\000a}{section.6}% 22
|
||||
\BOOKMARK [2][-]{subsection.6.2}{\376\377\000Q\000u\000a\000n\000t\000i\000l\000e\000\040\000R\000e\000g\000r\000e\000s\000s\000i\000o\000n}{section.6}% 23
|
||||
\BOOKMARK [3][-]{subsubsection.6.2.1}{\376\377\000L\000i\000n\000e\000a\000r\000\040\000M\000o\000d\000e\000l}{subsection.6.2}% 24
|
||||
\BOOKMARK [3][-]{subsubsection.6.2.2}{\376\377\000N\000o\000n\000-\000L\000i\000n\000e\000a\000r\000\040\000M\000o\000d\000e\000l}{subsection.6.2}% 25
|
||||
\BOOKMARK [3][-]{subsubsection.6.2.3}{\376\377\000G\000R\000U\000\040\000M\000o\000d\000e\000l}{subsection.6.2}% 26
|
||||
\BOOKMARK [2][-]{subsection.6.3}{\376\377\000D\000i\000f\000f\000u\000s\000i\000o\000n}{section.6}% 27
|
||||
\BOOKMARK [2][-]{subsection.6.4}{\376\377\000C\000o\000m\000p\000a\000r\000i\000s\000o\000n}{section.6}% 28
|
||||
\BOOKMARK [1][-]{section.7}{\376\377\000P\000o\000l\000i\000c\000i\000e\000s\000\040\000f\000o\000r\000\040\000b\000a\000t\000t\000e\000r\000y\000\040\000o\000p\000t\000i\000m\000i\000z\000a\000t\000i\000o\000n}{}% 29
|
||||
\BOOKMARK [2][-]{subsection.7.1}{\376\377\000B\000a\000s\000e\000l\000i\000n\000e\000s}{section.7}% 30
|
||||
\BOOKMARK [2][-]{subsection.7.2}{\376\377\000P\000o\000l\000i\000c\000y\000\040\000u\000s\000i\000n\000g\000\040\000g\000e\000n\000e\000r\000a\000t\000e\000d\000\040\000N\000R\000V\000\040\000s\000a\000m\000p\000l\000e\000s}{section.7}% 31
|
||||
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,33 @@
|
||||
\acswitchoff
|
||||
\babel@toc {english}{}\relax
|
||||
\contentsline {section}{\numberline {1}Introduction}{2}{section.1}%
|
||||
\contentsline {section}{\numberline {2}Electricity market}{3}{section.2}%
|
||||
\contentsline {section}{\numberline {3}Generative modeling}{5}{section.3}%
|
||||
\contentsline {subsection}{\numberline {3.1}Quantile Regression}{6}{subsection.3.1}%
|
||||
\contentsline {subsection}{\numberline {3.2}Autoregressive vs Non-Autoregressive models}{8}{subsection.3.2}%
|
||||
\contentsline {subsection}{\numberline {3.3}Model Types}{9}{subsection.3.3}%
|
||||
\contentsline {subsubsection}{\numberline {3.3.1}Linear Model}{9}{subsubsection.3.3.1}%
|
||||
\contentsline {subsubsection}{\numberline {3.3.2}Non-Linear Model}{10}{subsubsection.3.3.2}%
|
||||
\contentsline {subsubsection}{\numberline {3.3.3}Recurrent Neural Network (RNN)}{10}{subsubsection.3.3.3}%
|
||||
\contentsline {subsection}{\numberline {3.4}Diffusion models}{11}{subsection.3.4}%
|
||||
\contentsline {subsubsection}{\numberline {3.4.1}Overview}{12}{subsubsection.3.4.1}%
|
||||
\contentsline {subsubsection}{\numberline {3.4.2}Applications}{12}{subsubsection.3.4.2}%
|
||||
\contentsline {subsubsection}{\numberline {3.4.3}Generation process}{12}{subsubsection.3.4.3}%
|
||||
\contentsline {subsection}{\numberline {3.5}Evaluation}{14}{subsection.3.5}%
|
||||
\contentsline {section}{\numberline {4}Policies}{17}{section.4}%
|
||||
\contentsline {subsection}{\numberline {4.1}Baselines}{17}{subsection.4.1}%
|
||||
\contentsline {subsection}{\numberline {4.2}Policies based on NRV generations}{18}{subsection.4.2}%
|
||||
\contentsline {section}{\numberline {5}Literature Study}{19}{section.5}%
|
||||
\contentsline {subsection}{\numberline {5.1}Electricity Price Forecasting}{19}{subsection.5.1}%
|
||||
\contentsline {subsection}{\numberline {5.2}Policies for Battery Optimization}{20}{subsection.5.2}%
|
||||
\contentsline {section}{\numberline {6}Results \& Discussion}{21}{section.6}%
|
||||
\contentsline {subsection}{\numberline {6.1}Data}{21}{subsection.6.1}%
|
||||
\contentsline {subsection}{\numberline {6.2}Quantile Regression}{22}{subsection.6.2}%
|
||||
\contentsline {subsubsection}{\numberline {6.2.1}Linear Model}{22}{subsubsection.6.2.1}%
|
||||
\contentsline {subsubsection}{\numberline {6.2.2}Non-Linear Model}{29}{subsubsection.6.2.2}%
|
||||
\contentsline {subsubsection}{\numberline {6.2.3}GRU Model}{32}{subsubsection.6.2.3}%
|
||||
\contentsline {subsection}{\numberline {6.3}Diffusion}{36}{subsection.6.3}%
|
||||
\contentsline {subsection}{\numberline {6.4}Comparison}{40}{subsection.6.4}%
|
||||
\contentsline {section}{\numberline {7}Policies for battery optimization}{43}{section.7}%
|
||||
\contentsline {subsection}{\numberline {7.1}Baselines}{43}{subsection.7.1}%
|
||||
\contentsline {subsection}{\numberline {7.2}Policy using generated NRV samples}{44}{subsection.7.2}%
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
class DiffusionModel(nn.Module):
|
||||
def __init__(self, time_dim: int = 64):
|
||||
super(DiffusionModel, self).__init__()
|
||||
@@ -16,7 +17,6 @@ class DiffusionModel(nn.Module):
|
||||
pos_enc = torch.cat((pos_enc_a, pos_enc_b), dim=-1)
|
||||
return pos_enc
|
||||
|
||||
|
||||
def forward(self, x, t, inputs):
|
||||
t = t.unsqueeze(-1).type(torch.float)
|
||||
t = self.pos_encoding(t, self.time_dim)
|
||||
@@ -31,33 +31,61 @@ class DiffusionModel(nn.Module):
|
||||
x = self.layers[-1](x)
|
||||
return x
|
||||
|
||||
|
||||
class SimpleDiffusionModel(DiffusionModel):
|
||||
def __init__(self, input_size: int, hidden_sizes: list, other_inputs_dim: int, time_dim: int = 64):
|
||||
def __init__(
|
||||
self,
|
||||
input_size: int,
|
||||
hidden_sizes: list,
|
||||
other_inputs_dim: int,
|
||||
time_dim: int = 64,
|
||||
dropout_rate: float = 0.2,
|
||||
):
|
||||
super(SimpleDiffusionModel, self).__init__(time_dim)
|
||||
|
||||
self.other_inputs_dim = other_inputs_dim
|
||||
|
||||
self.layers.append(nn.Linear(input_size + time_dim + other_inputs_dim, hidden_sizes[0]))
|
||||
self.layers.append(
|
||||
nn.Linear(input_size + time_dim + other_inputs_dim, hidden_sizes[0])
|
||||
)
|
||||
self.layers.append(nn.ReLU())
|
||||
self.layers.append(nn.Dropout(dropout_rate))
|
||||
|
||||
for i in range(1, len(hidden_sizes)):
|
||||
self.layers.append(nn.Linear(hidden_sizes[i - 1] + time_dim + other_inputs_dim, hidden_sizes[i]))
|
||||
self.layers.append(
|
||||
nn.Linear(
|
||||
hidden_sizes[i - 1] + time_dim + other_inputs_dim, hidden_sizes[i]
|
||||
)
|
||||
)
|
||||
self.layers.append(nn.ReLU())
|
||||
self.layers.append(nn.Dropout(dropout_rate))
|
||||
|
||||
self.layers.append(
|
||||
nn.Linear(hidden_sizes[-1] + time_dim + other_inputs_dim, input_size)
|
||||
)
|
||||
|
||||
self.layers.append(nn.Linear(hidden_sizes[-1] + time_dim + other_inputs_dim, input_size))
|
||||
|
||||
class GRUDiffusionModel(DiffusionModel):
|
||||
def __init__(self, input_size: int, hidden_sizes: list, other_inputs_dim: int, gru_hidden_size: int, time_dim: int = 64):
|
||||
def __init__(
|
||||
self,
|
||||
input_size: int,
|
||||
hidden_sizes: list,
|
||||
other_inputs_dim: int,
|
||||
gru_hidden_size: int,
|
||||
time_dim: int = 64,
|
||||
):
|
||||
super(GRUDiffusionModel, self).__init__(time_dim)
|
||||
|
||||
self.other_inputs_dim = other_inputs_dim
|
||||
self.gru_hidden_size = gru_hidden_size
|
||||
|
||||
# GRU layer
|
||||
self.gru = nn.GRU(input_size=input_size + time_dim + other_inputs_dim,
|
||||
hidden_size=gru_hidden_size,
|
||||
num_layers=3,
|
||||
batch_first=True)
|
||||
self.gru = nn.GRU(
|
||||
input_size=input_size + time_dim + other_inputs_dim,
|
||||
hidden_size=gru_hidden_size,
|
||||
num_layers=3,
|
||||
batch_first=True,
|
||||
)
|
||||
|
||||
# Fully connected layers after GRU
|
||||
self.fc_layers = nn.ModuleList()
|
||||
@@ -76,16 +104,20 @@ class GRUDiffusionModel(DiffusionModel):
|
||||
|
||||
# Positional encoding for each time step
|
||||
t = t.unsqueeze(-1).type(torch.float)
|
||||
t = self.pos_encoding(t, self.time_dim) # Shape: [batch_size, seq_len, time_dim]
|
||||
t = self.pos_encoding(
|
||||
t, self.time_dim
|
||||
) # Shape: [batch_size, seq_len, time_dim]
|
||||
|
||||
# repeat time encoding for each time step t is shape [batch_size, time_dim], i want [batch_size, seq_len, time_dim]
|
||||
t = t.unsqueeze(1).repeat(1, seq_len, 1)
|
||||
|
||||
# Concatenate x, t, and inputs along the feature dimension
|
||||
x = torch.cat((x, t, inputs), dim=-1) # Shape: [batch_size, seq_len, input_size + time_dim + other_inputs_dim]
|
||||
x = torch.cat(
|
||||
(x, t, inputs), dim=-1
|
||||
) # Shape: [batch_size, seq_len, input_size + time_dim + other_inputs_dim]
|
||||
|
||||
# Pass through GRU
|
||||
output, hidden = self.gru(x) # Hidden Shape: [batch_size, seq_len, 1]
|
||||
output, hidden = self.gru(x) # Hidden Shape: [batch_size, seq_len, 1]
|
||||
|
||||
# Get last hidden state
|
||||
x = hidden[-1]
|
||||
|
||||
@@ -44,8 +44,8 @@ class PolicyEvaluator:
|
||||
date,
|
||||
idx_samples,
|
||||
test_loader,
|
||||
charge_thresholds=np.arange(-1000, 1000, 5),
|
||||
discharge_thresholds=np.arange(-1000, 1000, 5),
|
||||
charge_thresholds=np.arange(-1000, 1000, 100),
|
||||
discharge_thresholds=np.arange(-1000, 1000, 100),
|
||||
penalty: int = 0,
|
||||
state_of_charge: float = 0.0,
|
||||
):
|
||||
|
||||
@@ -52,7 +52,7 @@ data_processor.set_full_day_skip(False)
|
||||
#### Hyperparameters ####
|
||||
data_processor.set_output_size(1)
|
||||
inputDim = data_processor.get_input_size()
|
||||
epochs = 16
|
||||
epochs = 300
|
||||
|
||||
# add parameters to clearml
|
||||
quantiles = task.get_parameter("general/quantiles", cast=True)
|
||||
@@ -142,9 +142,9 @@ optimal_penalty, profit, charge_cycles = (
|
||||
policy_evaluator.optimize_penalty_for_target_charge_cycles(
|
||||
idx_samples=idx_samples,
|
||||
test_loader=test_loader,
|
||||
initial_penalty=1000,
|
||||
initial_penalty=20000,
|
||||
target_charge_cycles=283,
|
||||
initial_learning_rate=3,
|
||||
initial_learning_rate=13,
|
||||
max_iterations=150,
|
||||
tolerance=1,
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user