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Updated thesis

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Victor Mylle
2024-05-09 13:59:18 +02:00
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6 changed files with 44 additions and 18 deletions
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26
Reports/Thesis/sections/results/models/diffusion.tex
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@@ -40,3 +40,29 @@ Other hyperparameters that need to be chosen are the number of denoising steps,
In Figure \ref{fig:diffusion_intermediates}, multiple intermediate steps of the denoising process are shown as an example from the test set. The model starts with noisy full-day NRV samples which can be seen in the first steps. These noisy samples are then denoised in multiple steps until realistic samples are generated. This can be seen in the last image in the figure. It can be observed that the confidence intervals get more narrow over time as the noise is removed from the samples.
\begin{table}[H]
\centering
\begin{adjustbox}{width=\textwidth,center}
\begin{tabular}{@{}cccccccc@{}}
\toprule
Features & Diffusion Steps & Layers & Hidden Size & MSE & MAE & CRPS \\
\midrule
NRV & & & & & & & \\
& 300 & 4 & 1024 & 42984.02 & 157.54 & 77.92 \\
\midrule
NRV + Load + Wind + PV + NP & & & & & & & \\
& 300 & 3 & 256 & & & \\
& 300 & 3 & 512 & 53159.99 & 178.46 & 81.95 \\
& 300 & 3 & 1024 & 47815.13 & 167.22 & 81.16 \\
& 300 & 3 & 2048 & 46448.90 & 164.50 & 81.06 \\
& 300 & 4 & 1024 & 47483.05 & 166.97 & 81.32 \\
& 300 & 4 & 2048 & 47076.77 & 166.06 & 81.05 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Non-linear quantile regression model results. All the models used a dropout of 0.2 .}
\label{tab:diffusion_results}
\end{table}

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Reports/Thesis/verslag.aux
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@@ -82,11 +82,13 @@
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Reports/Thesis/verslag.log
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src/training_scripts/autoregressive_quantiles.py
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@@ -117,7 +117,7 @@ trainer = AutoRegressiveQuantileTrainer(
data_processor,
quantiles,
"cuda",
policy_evaluator=policy_evaluator,
policy_evaluator=None,
debug=False,
)