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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. 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a/Reports/Thesis/verslag.synctex.gz b/Reports/Thesis/verslag.synctex.gz index 84eb81a..f678888 100644 Binary files a/Reports/Thesis/verslag.synctex.gz and b/Reports/Thesis/verslag.synctex.gz differ diff --git a/Reports/Thesis/verslag.toc b/Reports/Thesis/verslag.toc index e69de29..c109413 100644 --- a/Reports/Thesis/verslag.toc +++ b/Reports/Thesis/verslag.toc @@ -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}% diff --git a/src/models/diffusion_model.py b/src/models/diffusion_model.py index bfd076b..d1943ef 100644 --- a/src/models/diffusion_model.py +++ b/src/models/diffusion_model.py @@ -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] @@ -94,4 +126,4 @@ class GRUDiffusionModel(DiffusionModel): for layer in self.fc_layers: x = layer(x) - return x \ No newline at end of file + return x diff --git a/src/policies/PolicyEvaluator.py b/src/policies/PolicyEvaluator.py index e4879ff..a8ef357 100644 --- a/src/policies/PolicyEvaluator.py +++ b/src/policies/PolicyEvaluator.py @@ -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, ): diff --git a/src/training_scripts/autoregressive_quantiles.py b/src/training_scripts/autoregressive_quantiles.py index ccb48df..7f76d4d 100644 --- a/src/training_scripts/autoregressive_quantiles.py +++ b/src/training_scripts/autoregressive_quantiles.py @@ -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, )