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Adding intermediate table with non linear model results

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Victor Mylle
2024-04-20 18:49:26 +02:00
parent 3a40959a32
commit ac08707369
8 changed files with 63 additions and 23 deletions
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34
Reports/Thesis/sections/nrv_prediction.tex
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@@ -384,7 +384,39 @@ Linear (Linear) & [B, Number of quantiles] \\
\label{tab:non_linear_model_architecture}
\end{table}
This is still a quite simple model with not too many hyperparameters to experiment with. The hidden size of the linear layers and the number of layers can be experimented with. The same quantiles will be that were used for the linear quantile regression model.
This is still a quite simple model with not too many hyperparameters to experiment with. The hidden size of the linear layers and the number of layers can be experimented with. The same quantiles will be that were used for the linear quantile regression model. The model is trained using the Adam optimizer with a learning rate of 1e-4. Early stopping is used with a patience of 5 epochs. The results of the non-linear model are shown in Table \ref{tab:autoregressive_non_linear_model_results}.
\begin{table}[ht]
\centering
\caption{Comparison of autoregressive models with various configurations}
\label{tab:model_comparison}
\begin{adjustbox}{width=\textwidth,center}
\begin{tabular}{@{}cccccccccc@{}}
\toprule
Features & Layers & Hidden Size & \multicolumn{2}{c}{MSE} & \multicolumn{2}{c}{MAE} & \multicolumn{2}{c}{CRPS} \\
\cmidrule(lr){4-5} \cmidrule(lr){6-7} \cmidrule(lr){8-9}
& & & Train & Test & Train & Test & Train & Test \\
\midrule
NRV & & & & & & & & \\
& 2 & 256 & 32982.64 & 38117.43 & 138.92 & 147.55 & 82.10 & 86.42 \\
& 4 & 256 & 33317.10 & 37817.78 & 139.42 & 146.90 & 82.17 & 85.63 \\
& 8 & 256 & 32727.90 & 36346.57 & 139.21 & 144.80 & 81.86 & 84.51 \\
\midrule
NRV + Load + PV\\ + Wind & & & & & & & & \\
& 2 & 256 & 28860.10 & 42983.21 & 130.46 & 156.65 & 75.47 & 92.15 \\
\midrule
NRV + Load + PV\\ + Wind + Net Position\\ + QE (dim 5) & & & & & & & & \\
& 2 & 256 & 25064.82 & 37785.49 & 121.45 & 146.99 & 70.47 & 85.22 \\
& 4 & 256 & 24333.62 & 34232.57 & 119.16 & 139.78 & 68.60 & 80.14 \\
& 8 & 256 & 26399.20 & \textbf{32447.41} & 124.75 & \textbf{137.24} & 72.07 & \textbf{79.22} \\
& 2 & 512 & 28608.20 & 44281.20 & 12x9.41 & 158.63 & 75.54 & 91.82 \\
& 4 & 512 & 24564.89 & 34839.79 & 119.74 & 140.67 & 69.02 & 80.21 \\
& 8 & 512 & 24523.61 & 34925.46 & 119.90 & 141.11 & 69.26 & 81.11 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\subsubsection{GRU Model}

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2
Reports/Thesis/verslag.aux
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@@ -73,6 +73,8 @@
\newlabel{tab:non_linear_model_architecture}{{6}{22}{Non-linear Quantile Regression Model Architecture Details\relax }{table.caption.13}{}}
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\@writefile{lot}{\contentsline {table}{\numberline {7}{\ignorespaces Comparison of autoregressive models with various configurations\relax }}{23}{table.caption.14}\protected@file@percent }
\newlabel{tab:model_comparison}{{7}{23}{Comparison of autoregressive models with various configurations\relax }{table.caption.14}{}}
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\@writefile{toc}{\contentsline {section}{\numberline {6}Policies for battery optimization}{23}{section.6}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {6.1}Baselines}{23}{subsection.6.1}\protected@file@percent }

24
Reports/Thesis/verslag.log
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@@ -1,4 +1,4 @@
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@@ -1448,7 +1448,11 @@ Underfull \hbox (badness 10000) in paragraph at lines 364--386
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@@ -1464,18 +1468,18 @@ Package logreq Info: Writing requests to 'verslag.run.xml'.
)
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26
src/training_scripts/autoregressive_quantiles.py
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@@ -2,7 +2,9 @@ from src.utils.clearml import ClearMLHelper
#### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(task_name="AQR: Non-Linear (2 - 256 - 0.2)")
task = clearml_helper.get_task(
task_name="AQR: Non-Linear (8 - 512 - 0.2) + Load + PV + Wind + Net Position + QE (dim 5)"
)
task.execute_remotely(queue_name="default", exit_process=True)
from src.policies.PolicyEvaluator import PolicyEvaluator
@@ -28,19 +30,19 @@ data_config = DataConfig()
data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = False
data_config.LOAD_FORECAST = False
data_config.LOAD_HISTORY = True
data_config.LOAD_FORECAST = True
data_config.WIND_FORECAST = False
data_config.WIND_HISTORY = False
data_config.WIND_FORECAST = True
data_config.WIND_HISTORY = True
data_config.PV_FORECAST = False
data_config.PV_HISTORY = False
data_config.PV_FORECAST = True
data_config.PV_HISTORY = True
data_config.QUARTER = False
data_config.QUARTER = True
data_config.DAY_OF_WEEK = False
data_config.NOMINAL_NET_POSITION = False
data_config.NOMINAL_NET_POSITION = True
data_config = task.connect(data_config, name="data_features")
@@ -68,8 +70,8 @@ else:
model_parameters = {
"learning_rate": 0.0001,
"hidden_size": 256,
"num_layers": 2,
"hidden_size": 512,
"num_layers": 8,
"dropout": 0.2,
"time_feature_embedding": 5,
}
@@ -125,7 +127,7 @@ trainer = AutoRegressiveQuantileTrainer(
trainer.add_metrics_to_track(
[PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]
)
trainer.early_stopping(patience=5)
trainer.early_stopping(patience=10)
trainer.plot_every(15)
trainer.train(task=task, epochs=epochs, remotely=True)