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Added non-autoregresive non-linear results to thesis

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Victor Mylle
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Reports/Thesis/sections/results/linear.tex
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\subsubsection{Linear Model} \subsection{Linear Model}
% TODO: explainedi nsection reference ? % TODO: explainedi nsection reference ?
The simplest model to be trained for the NRV modeling is the linear model. The linear model is trained using the pinball loss function explained in the section above. The outputs of the model are values for the chosen quantiles. The linear model can be trained in an autoregressive and non-autoregressive way. Both methods will be compared to each other. The linear 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 linear model is evaluated using the mean squared error (MSE), mean absolute error (MAE), and continuous ranked probability score (CRPS). The influence of the input features is also evaluated by training the models with different input feature sets. The simplest model to be trained for the NRV modeling is the linear model. The linear model is trained using the pinball loss function explained in the section above. The outputs of the model are values for the chosen quantiles. The linear model can be trained in an autoregressive and non-autoregressive way. Both methods will be compared to each other. The linear 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 linear model is evaluated using the mean squared error (MSE), mean absolute error (MAE), and continuous ranked probability score (CRPS). The influence of the input features is also evaluated by training the models with different input feature sets.

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Reports/Thesis/sections/results/non-linear.tex
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\subsubsection{Non-Linear Model} \subsection{Non-Linear Model}
Adding nonlinearity to the model can be done by adding some non-linear activations between linear layers. This improves the model's ability to learn more complex patterns in the data. The model is trained the same way as the linear model for quantile regression using the pinball loss. Because a non-linear model is more complex, it is more prone to overfitting the training data. Because of this, dropout layers are added to the model to prevent overfitting. Adding nonlinearity to the model can be done by adding some non-linear activations between linear layers. This improves the model's ability to learn more complex patterns in the data. The model is trained the same way as the linear model for quantile regression using the pinball loss. Because a non-linear model is more complex, it is more prone to overfitting the training data. Because of this, dropout layers are added to the model to prevent overfitting.
The architecture of the non-linear model is illustrated in Table \ref{tab:non_linear_model_architecture}. The autoregressive model begins with an input layer that converts the quarter of the day into an embedding. This layer concatenates the other input features with the quarter embedding. These combined features are then processed through a sequence of layers: The architecture of the non-linear model is illustrated in Table \ref{tab:non_linear_model_architecture}. The autoregressive model begins with an input layer that converts the quarter of the day into an embedding. This layer concatenates the other input features with the quarter embedding. These combined features are then processed through a sequence of layers:
@@ -45,21 +45,21 @@ While this non-linear model is still quite simple, it offers the flexibility in
& & & Train & Test & Train & Test & Train & Test \\ & & & Train & Test & Train & Test & Train & Test \\
\midrule \midrule
NRV & & & & & & & & \\ NRV & & & & & & & & \\
& 2 & 256 & 32982.64 & 38117.43 & 138.92 & 147.55 & 82.10 & 86.42 \\ & 2 & 256 & 38117.43 & 41574.38 & 147.55 & 153.83 & 86.42 & 75.61 \\
& 4 & 256 & 33317.10 & 37817.78 & 139.42 & 146.90 & 82.17 & 85.63 \\ & 4 & 256 & 37817.78 & 40200.92 & 146.90 & 152.00 & 85.63 & 74.37 \\
& 8 & 256 & 32727.90 & 36346.57 & 139.21 & 144.80 & 81.86 & 84.51 \\ & 8 & 256 & 36346.57 & 38746.81 & 144.80 & 148.82 & 84.51 & 74.55 \\
& 16 & 256 & 35076.57 & 38624.83 & 143.28 & 148.61 & 84.70 & 87.05 \\ & 16 & 256 & 38624.83 & 39328.47 & 148.61 & 149.19 & 87.05 & 75.38 \\
\midrule \midrule
NRV + Load + PV\\ + Wind & & & & & & & & \\ NRV + Load + PV\\ + Wind & & & & & & & & \\
& 2 & 256 & 28860.10 & 42983.21 & 130.46 & 156.65 & 75.47 & 92.15 \\ & 2 & 256 & 42983.21 & 42950.17 & 156.65 & 156.88 & 92.15 & 76.21 \\
\midrule \midrule
NRV + Load + PV\\ + Wind + Net Position\\ + QE (dim 5) & & & & & & & & \\ NRV + Load + PV\\ + Wind + Net Position\\ + QE (dim 5) & & & & & & & & \\
& 2 & 256 & 25064.82 & 37785.49 & 121.45 & 146.99 & 70.47 & 85.22 \\ & 2 & 256 & 37785.49 & 42828.61 & 146.99 & 157.03 & 85.22 & 76.36 \\
& 4 & 256 & 24333.62 & 34232.57 & 119.16 & 139.78 & 68.60 & 80.14 \\ & 4 & 256 & 34232.57 & 42588.16 & 139.78 & 157.20 & 80.14 & 73.75 \\
& 8 & 256 & 26399.20 & \textbf{32447.41} & 124.75 & \textbf{137.24} & 72.07 & \textbf{79.22} \\ & 8 & 256 & \textbf{32447.41} & 40541.92 & \textbf{137.24} & 151.60 & \textbf{79.22} & 75.52 \\
& 2 & 512 & 28608.20 & 44281.20 & 12x9.41 & 158.63 & 75.54 & 91.82 \\ & 2 & 512 & 44281.20 & 44018.79 & 158.63 & 159.06 & 91.82 & 77.99 \\
& 4 & 512 & 24564.89 & 34839.79 & 119.74 & 140.67 & 69.02 & 80.21 \\ & 4 & 512 & 34839.79 & 41999.79 & 140.67 & 154.86 & 80.21 & 75.70 \\
& 8 & 512 & 24523.61 & 34925.46 & 119.90 & 141.11 & 69.26 & 81.11 \\ & 8 & 512 & 34925.46 & 39774.38 & 141.11 & 150.62 & 81.11 & 74.67 \\
\bottomrule \bottomrule
\end{tabular} \end{tabular}

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Reports/Thesis/verslag.aux
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\contentsline {subsection}{\numberline {5.2}Policies for Battery Optimization}{19}{subsection.5.2}% \contentsline {subsection}{\numberline {5.2}Policies for Battery Optimization}{19}{subsection.5.2}%
\contentsline {section}{\numberline {6}Results \& Discussion}{20}{section.6}% \contentsline {section}{\numberline {6}Results \& Discussion}{20}{section.6}%
\contentsline {subsection}{\numberline {6.1}Data}{20}{subsection.6.1}% \contentsline {subsection}{\numberline {6.1}Data}{20}{subsection.6.1}%
\contentsline {subsubsection}{\numberline {6.1.1}Linear Model}{21}{subsubsection.6.1.1}% \contentsline {subsection}{\numberline {6.2}Linear Model}{21}{subsection.6.2}%
\contentsline {subsubsection}{\numberline {6.1.2}Non-Linear Model}{27}{subsubsection.6.1.2}% \contentsline {subsection}{\numberline {6.3}Non-Linear Model}{27}{subsection.6.3}%
\contentsline {subsubsection}{\numberline {6.1.3}GRU Model}{30}{subsubsection.6.1.3}% \contentsline {subsubsection}{\numberline {6.3.1}GRU Model}{30}{subsubsection.6.3.1}%
\contentsline {subsection}{\numberline {6.2}Diffusion}{33}{subsection.6.2}% \contentsline {subsection}{\numberline {6.4}Diffusion}{33}{subsection.6.4}%
\contentsline {section}{\numberline {7}Policies for battery optimization}{33}{section.7}% \contentsline {section}{\numberline {7}Policies for battery optimization}{33}{section.7}%
\contentsline {subsection}{\numberline {7.1}Baselines}{33}{subsection.7.1}% \contentsline {subsection}{\numberline {7.1}Baselines}{33}{subsection.7.1}%
\contentsline {subsection}{\numberline {7.2}Policies using NRV predictions}{33}{subsection.7.2}% \contentsline {subsection}{\numberline {7.2}Policies using NRV predictions}{33}{subsection.7.2}%

40
src/training_scripts/autoregressive_quantiles.py
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@@ -2,9 +2,7 @@ from src.utils.clearml import ClearMLHelper
#### ClearML #### #### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast") clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task( task = clearml_helper.get_task(task_name="AQR: Linear + QE (dim 2)")
task_name="AQR: GRU (8 - 512) + Load + PV + Wind + NP + QE (dim 5)"
)
# task.execute_remotely(queue_name="default", exit_process=True) # task.execute_remotely(queue_name="default", exit_process=True)
from src.policies.PolicyEvaluator import PolicyEvaluator from src.policies.PolicyEvaluator import PolicyEvaluator
@@ -29,24 +27,24 @@ data_config = DataConfig()
data_config.NRV_HISTORY = True data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = True data_config.LOAD_HISTORY = False
data_config.LOAD_FORECAST = True data_config.LOAD_FORECAST = False
data_config.WIND_FORECAST = True data_config.WIND_FORECAST = False
data_config.WIND_HISTORY = True data_config.WIND_HISTORY = False
data_config.PV_FORECAST = True data_config.PV_FORECAST = False
data_config.PV_HISTORY = True data_config.PV_HISTORY = False
data_config.QUARTER = True data_config.QUARTER = True
data_config.DAY_OF_WEEK = False data_config.DAY_OF_WEEK = False
data_config.NOMINAL_NET_POSITION = True data_config.NOMINAL_NET_POSITION = False
data_config = task.connect(data_config, name="data_features") data_config = task.connect(data_config, name="data_features")
data_processor = DataProcessor(data_config, path="", lstm=True) data_processor = DataProcessor(data_config, path="", lstm=False)
data_processor.set_batch_size(512) data_processor.set_batch_size(512)
data_processor.set_full_day_skip(False) data_processor.set_full_day_skip(False)
@@ -72,7 +70,7 @@ model_parameters = {
"hidden_size": 512, "hidden_size": 512,
"num_layers": 8, "num_layers": 8,
"dropout": 0.2, "dropout": 0.2,
"time_feature_embedding": 5, "time_feature_embedding": 2,
} }
model_parameters = task.connect(model_parameters, name="model_parameters") model_parameters = task.connect(model_parameters, name="model_parameters")
@@ -83,13 +81,13 @@ time_embedding = TimeEmbedding(
# time_embedding = TrigonometricTimeEmbedding(data_processor.get_time_feature_size()) # time_embedding = TrigonometricTimeEmbedding(data_processor.get_time_feature_size())
lstm_model = GRUModel( # lstm_model = GRUModel(
time_embedding.output_dim(inputDim), # time_embedding.output_dim(inputDim),
len(quantiles), # len(quantiles),
hidden_size=model_parameters["hidden_size"], # hidden_size=model_parameters["hidden_size"],
num_layers=model_parameters["num_layers"], # num_layers=model_parameters["num_layers"],
dropout=model_parameters["dropout"], # dropout=model_parameters["dropout"],
) # )
# non_linear_model = NonLinearRegression( # non_linear_model = NonLinearRegression(
# time_embedding.output_dim(inputDim), # time_embedding.output_dim(inputDim),
@@ -99,9 +97,9 @@ lstm_model = GRUModel(
# dropout=model_parameters["dropout"], # dropout=model_parameters["dropout"],
# ) # )
# linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles)) linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))
model = nn.Sequential(time_embedding, lstm_model) model = nn.Sequential(time_embedding, linear_model)
model.output_size = 1 model.output_size = 1
optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"]) optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])

20
src/training_scripts/non_autoregressive_quantiles.py
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@@ -2,7 +2,9 @@ from src.utils.clearml import ClearMLHelper
#### ClearML #### #### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NAQR: Non-Linear") clearml_helper = ClearMLHelper(project_name="Thesis/NAQR: Non-Linear")
task = clearml_helper.get_task(task_name="NAQR: Non-Linear (2 - 256)") task = clearml_helper.get_task(
task_name="NAQR: Non-Linear (8 - 512) + NRV + LOAD + PV + WIND + NP"
)
task.execute_remotely(queue_name="default", exit_process=True) task.execute_remotely(queue_name="default", exit_process=True)
from src.policies.PolicyEvaluator import PolicyEvaluator from src.policies.PolicyEvaluator import PolicyEvaluator
@@ -27,16 +29,16 @@ from src.models.time_embedding_layer import TimeEmbedding
data_config = DataConfig() data_config = DataConfig()
data_config.NRV_HISTORY = True data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = False data_config.LOAD_HISTORY = True
data_config.LOAD_FORECAST = False data_config.LOAD_FORECAST = True
data_config.WIND_FORECAST = False data_config.WIND_FORECAST = True
data_config.WIND_HISTORY = True data_config.WIND_HISTORY = True
data_config.PV_FORECAST = False data_config.PV_FORECAST = True
data_config.PV_HISTORY = False data_config.PV_HISTORY = True
data_config.NOMINAL_NET_POSITION = False data_config.NOMINAL_NET_POSITION = True
data_config = task.connect(data_config, name="data_features") data_config = task.connect(data_config, name="data_features")
@@ -64,8 +66,8 @@ else:
model_parameters = { model_parameters = {
"learning_rate": 0.0001, "learning_rate": 0.0001,
"hidden_size": 256, "hidden_size": 512,
"num_layers": 2, "num_layers": 8,
"dropout": 0.2, "dropout": 0.2,
} }