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Increased patience for AQR

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Victor Mylle 2024-04-23 21:02:40 +02:00
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43
Reports/Thesis/sections/nrv_prediction.tex
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@ -510,5 +510,48 @@ Linear & [B, Number of quantiles] \\
\label{tab:gru_model_architecture}
\end{table}
Multiple experiments are conducted to find which hyperparameters and input features work best for the GRU model. The results of the GRU model are shown in Table \ref{tab:autoregressive_gru_model_results}.
\begin{table}[H]
\centering
\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 & 34942.89 & 39838.35 & 142.43 & 150.81 & 81.34 & 85.04 \\
& 4 & 256 & 34705.61 & 39506.55 & 141.74 & 149.81 & 81.89 & 85.46 \\
& 8 & 256 & 32885.71 & 37747.11 & 138.16 & 146.67 & 79.99 & 83.67 \\
& 2 & 512 & 35362.66 & 39955.79 & 143.19 & 150.77 & 84.37 & 87.88 \\
& 4 & 512 & 38253.89 & 43301.13 & 148.33 & 156.73 & 85.98 & 89.78 \\
& 8 & 512 & 33131.93 & 37681.71 & 138.93 & 146.62 & 79.64 & 83.08 \\
\midrule
NRV + Load & & & & & & & & & \\
& 2 & 256 & 33202.80 & 38427.91 & 138.02 & 147.27 & 79.62 & 84.17 \\
& 4 & 256 & 33600.73 & 38984.44 & 138.62 & 147.91 & 81.03 & 85.91 \\
& 8 & 256 & 32828.61 & 38343.98 & 136.82 & 146.44 & 79.42 & 84.22 \\
& 2 & 512 & 35979.57 & 41496.77 & 144.16 & 153.53 & 83.50 & 88.26 \\
& 4 & 512 & 32334.73 & 38000.40 & 135.92 & 146.10 & 78.82 & 83.99 \\
& 8 & 512 & 35177.39 & 41104.28 & 141.79 & 152.13 & 83.79 & 89.13 \\
\midrule
NRV + Load + PV + Wind & & & & & & & & & \\
& 4 & 256 & 31594.55 & 39872.46 & 134.11 & 149.34 & 77.52 & 85.91 \\
& 8 & 256 & 31481.22 & 39704.37 & 133.45 & 148.59 & 77.26 & 85.62 \\
& 4 & 512 & 31368.31 & 39024.27 & 134.02 & 147.91 & 76.58 & 84.18 \\
& 8 & 512 & 34566.66 & 42397.86 & 140.13 & 154.00 & 82.09 & 89.87 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Autoregressive GRU quantile regression model results. All the models used a dropout of 0.2 .}
\label{tab:autoregressive_gru_model_results}
\end{table}
\newpage
\subsection{Diffusion}

12
Reports/Thesis/verslag.aux
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@ -81,10 +81,12 @@
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20
Reports/Thesis/verslag.log
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@ -1,4 +1,4 @@
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@ -1521,7 +1521,7 @@ Underfull \hbox (badness 10000) in paragraph at lines 491--511
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[25 <./images/quantile_regression/rnn/RNN_diagram.png>] [26] [27]) [28] (./verslag.aux (./sections/introduction.aux) (./sections/background.aux) (./sections/literature_study.aux))
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Reports/Thesis/verslag.toc
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@ -17,7 +17,7 @@
\contentsline {subsubsection}{\numberline {5.2.3}Linear Model}{16}{subsubsection.5.2.3}%
\contentsline {subsubsection}{\numberline {5.2.4}Non-linear Model}{22}{subsubsection.5.2.4}%
\contentsline {subsubsection}{\numberline {5.2.5}GRU Model}{25}{subsubsection.5.2.5}%
\contentsline {subsection}{\numberline {5.3}Diffusion}{27}{subsection.5.3}%
\contentsline {section}{\numberline {6}Policies for battery optimization}{27}{section.6}%
\contentsline {subsection}{\numberline {6.1}Baselines}{27}{subsection.6.1}%
\contentsline {subsection}{\numberline {6.2}Policies using NRV predictions}{27}{subsection.6.2}%
\contentsline {subsection}{\numberline {5.3}Diffusion}{28}{subsection.5.3}%
\contentsline {section}{\numberline {6}Policies for battery optimization}{28}{section.6}%
\contentsline {subsection}{\numberline {6.1}Baselines}{28}{subsection.6.1}%
\contentsline {subsection}{\numberline {6.2}Policies using NRV predictions}{28}{subsection.6.2}%

28
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: GRU (2 - 256)")
task = clearml_helper.get_task(
task_name="AQR: GRU (8 - 512) + Load + PV + Wind + NP + QE (dim 5)"
)
task.execute_remotely(queue_name="default", exit_process=True)
from src.policies.PolicyEvaluator import PolicyEvaluator
@ -28,24 +30,24 @@ 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")
data_processor = DataProcessor(data_config, path="", lstm=False)
data_processor = DataProcessor(data_config, path="", lstm=True)
data_processor.set_batch_size(512)
data_processor.set_full_day_skip(False)
@ -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=10)
trainer.early_stopping(patience=25)
trainer.plot_every(15)
trainer.train(task=task, epochs=epochs, remotely=True)