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b/Reports/Thesis/images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP_QE-Sample_864.png differ diff --git a/Reports/Thesis/sections/nrv_prediction.tex b/Reports/Thesis/sections/nrv_prediction.tex index 16b9e77..093666f 100644 --- a/Reports/Thesis/sections/nrv_prediction.tex +++ b/Reports/Thesis/sections/nrv_prediction.tex @@ -41,6 +41,7 @@ TODO: ask Jonas: add urls to the correct data? via citation? A lot of data is available but only the most relevant data needs to be used. Experiments will be done to identify which data and features improve the NRV modeling. The data will be split into a training and test set. The training dataset starts depending on which data features are used but ends on 31-12-2022. The test set starts on 01-01-2023 and ends on (TODO: check the end date). This makes sure enough data is available to train the models and the test set is large enough to evaluate the models. The year 2023 is chosen as the test set because it is the most recent data available when the thesis experiments were conducted. Using data from 2022 in the test set also does not make a lot of sense because the trained models would be used to predict the future. Data from 2022 is not relevant anymore to evaluate the models. \subsection{Quantile Regression} +% TODO: Talk about the different number and which quantiles are used Forecasting the NRV is very difficult and most of the time not accurate. It is a very volatile time series and is hard to predict. Instead of just forecasting the NRV, a generative model can be trained and used to sample multiple full NRV samples for the next day. Sampling multiple times can give a better understanding of the uncertainty of the NRV. To be able to sample multiple times, a distribution of the NRV is needed. Only one value for the NRV for each quarter is available in the training and test data. There is no information on the distribution of this value. \\\\ Quantile regression can be used to model the distribution of the NRV. This is a technique that estimates the conditional quantiles of the target variable. A quantile is a statistical value of a random variable below which a certain proportion of observations fall. Figure \ref{fig:quantile_example} shows the cumulative distribution function of a normal distribution. The figure shows the 25th, 50th and 75th quantiles. The 25th quantile is the value below which 25\% of the observations fall. In the example, this value is -0.67. The other quantiles work in the same way. @@ -147,7 +148,7 @@ TODO: improve visualisation? \label{fig:crps_visualization} \end{figure} -\subsubsection{Models} +\subsubsection{Linear Model} A linear quantile regression model is used as a baseline. This model is very simple and can be used to compare the more complex models. Starting with a simple autoregressive model, the model is conditioned on the 96 previous NRV values. The model outputs the quantiles for the next quarter. The model is trained using the pinball loss function and the MAE, MSE, CRPS metrics are calculated on the test set. The linear model for the quantile regression is defined as: TODO: better equation for linear quantile regression model @@ -163,7 +164,7 @@ where: \end{itemize} % TODO: is it necessary to provide the parameter calculation? -The linear model outputs the values for the chosen quantiles. The total amount of parameters depends on the input features and the number of chosen quantiles. Assuming the input features are the 96 previous NRV values and 13 quantiles are chosen, the total amount of parameters is $96 * 13 + 13 = 1261$. 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. Different sets of input features are experimented with and are compared to each other based on the previously mentioned metrics. All results are shown in table \ref{tab:autoregressive_linear_model_baseline_results}. +The linear model outputs the values for the chosen quantiles. The total amount of parameters depends on the input features and the number of chosen quantiles. Assuming the input features are the 96 previous NRV values and 13 quantiles are chosen, the total amount of parameters is $96 * 13 + 13 = 1261$. 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. Different sets of input features are experimented with and are compared to each other based on the previously mentioned metrics. All results are shown in Table \ref{tab:autoregressive_linear_model_baseline_results}. \\\\ % TODO: ask Jonas: add number of parameters to this table? \begin{table}[ht] @@ -184,8 +185,8 @@ NRV + Load + Wind + Net Position & 31634.27 & 37890.66 & 137.87 & 149.37 & 81.17 NRV + Load + PV + Wind + Net Position & 29034.53 & \textbf{35725.42} & 131.87 & \textbf{145.64} & 76.23 & \textbf{83.30} \\ \bottomrule \end{tabular} -\label{tab:autoregressive_linear_model_baseline_results} \caption{Autoregressive linear model results} +\label{tab:autoregressive_linear_model_baseline_results} \end{table} The linear model outputs the quantiles for the next quarter based on the given input features. The input features consist of previous history values of a certain feature or forecasts of a certain feature. The model, however, does not know which quarter of the day it is modeling. @@ -196,7 +197,7 @@ Multiple methods exist to provide such information to the model. The quarter of \text{sin}(\frac{2\pi}{96} \times \text{quarter}) \quad \text{and} \quad \text{cos}(\frac{2\pi}{96} \times \text{quarter}) \end{equation} -The sine and cosine values are then concatenated with the input features. Another method that can be used is adding an embedding layer to the model. The discrete quarter of the day value can then be mapped to a vector. The embedding layer itself is learned during the training process which allows the model to learn patterns between quarters. The length of the embedding vector can be chosen and experimented with. The quarter-of-the-day information is then concatenated with the input features. Other information (eg. day of the week, month, year) can also easily be added to the model using this method by just increasing the size of the embedding layer. The results of the linear model with the quarter information are shown in table \ref{tab:autoregressive_linear_model_quarter_embedding_baseline_results}. The results show that adding the quarter embedding to the model improves the performance of the linear model. +The sine and cosine values are then concatenated with the input features. Another method that can be used is adding an embedding layer to the model. The discrete quarter of the day value can then be mapped to a vector. The embedding layer itself is learned during the training process which allows the model to learn patterns between quarters. The length of the embedding vector can be chosen and experimented with. The quarter-of-the-day information is then concatenated with the input features. Other information (eg. day of the week, month, year) can also easily be added to the model using this method by just increasing the size of the embedding layer. The results of the linear model with the quarter information are shown in Table \ref{tab:autoregressive_linear_model_quarter_embedding_baseline_results}. The results show that adding the quarter embedding to the model improves the performance of the linear model. \\\\ % TODO: Ask Jonas: Find cleaner way to present this table (remove repitition) % TODO: Add more time information like day of week, month @@ -220,8 +221,8 @@ NRV + Load + PV + Wind + Net Position \\ + Quarter Embedding \textbf{(2 dim)} & NRV + Load + PV + Wind + Net Position \\ + Quarter Embedding \textbf{(5 dim)} & 27407.41 & \textbf{34031.71} & 128.31 & \textbf{142.29} & 72.06 & \textbf{79.99} \\ \bottomrule \end{tabular} -\label{tab:autoregressive_linear_model_quarter_embedding_baseline_results} \caption{Autoregressive linear model results with time features} +\label{tab:autoregressive_linear_model_quarter_embedding_baseline_results} \end{table} Some examples of the sampled full NRV day samples are shown in figure \ref{fig:autoregressive_linear_model_samples}. The figure shows the real NRV values and the confidence intervals calculated based on 1000 full-day NRV samples. The mean of these samples is also plotted in the figure. The confidence intervals show the uncertainty of the NRV values. When the confidence interval is large, the model is not very certain about the NRV value and samples the NRV for that quarter with a high variance. The confidence intervals seen in the figure are quite narrow and do not always capture the real NRV value. @@ -261,7 +262,7 @@ The linear model is a simple model and can be used as a baseline to compare with % ----------- Non-autoregressive model ----------- Until now, only the autoregressive linear model has been discussed. The non-autoregressive linear model can also be used to model the NRV and generate full-day samples. The model will now output the quantiles for every quarter of the day. The number of output values can be calculated as the number of quarters in a day multiplied by the number of quantiles. From this output, the cumulative distribution functions for every quarter of the day can be reconstructed. These functions can then be used to sample the NRV values for each quarter. There is a problem with this approach. The sampled NRV values are independent of each other. The NRV sample for the next quarter does not depend on what value was sampled for the quarter before. \\\\ -Training the non-autoregressive quantile model can be done in the same way as the autoregressive model. Now the pinball loss is calculated for every quarter of the day and the mean is taken over all the quarters. The models are also trained using the Adam optimizer with a learning rate of 1e-4. Early stopping is used with a patience of 5 epochs. Results of the non-autoregressive linear model are shown in table \ref{tab:non_autoregressive_linear_model_baseline_results}. +Training the non-autoregressive quantile model can be done in the same way as the autoregressive model. Now the pinball loss is calculated for every quarter of the day and the mean is taken over all the quarters. The models are also trained using the Adam optimizer with a learning rate of 1e-4. Early stopping is used with a patience of 5 epochs. Results of the non-autoregressive linear model are shown in Table \ref{tab:non_autoregressive_linear_model_baseline_results}. \begin{table}[ht] \centering @@ -279,8 +280,8 @@ NRV + Load + PV + Wind & 39072.94 & 50312.85 & 151.07 & 169.06 & 68.40 & 79.85 \ NRV + Load + Wind + Net Position & 39505.20 & 49442.48 & 151.65 & 167.90 & 68.69 & 76.72 \\ \bottomrule \end{tabular} -\label{tab:non_autoregressive_linear_model_baseline_results} \caption{Non-Autoregressive linear model results} +\label{tab:non_autoregressive_linear_model_baseline_results} \end{table} In Figure \ref{fig:non_autoregressive_linear_model_samples}, some examples of the sampled full NRV day samples are shown. The confidence intervals are calculated based on 1000 full-day NRV samples. The mean of these samples is also plotted in the figure. The confidence intervals show the uncertainty of the NRV values. The same test set examples are used as the examples of the autoregressive linear model shown in Figure \ref{fig:autoregressive_linear_model_samples}. @@ -317,29 +318,77 @@ In Figure \ref{fig:non_autoregressive_linear_model_samples}, some examples of th Comparing the results from the autoregressive and non-autoregressive linear models, it is clear that the autoregressive model has lower MSE and MAE on the test set. The CRPS is, however, higher for the autoregressive model. The CRPS is calculated using the outputted quantiles while the MSE and MAE are calculated by sampling from the reconstructed distributions. % TODO: Is this reasoning correct? + Explain the results more we see in the table (Weird results :( ) -Because of error propagation in the autoregressive model, the outputted quantiles also contain more error which leads to a higher CRPS. The non-autoregressive model does not suffer from this problem. +Because of error propagation in the autoregressive model, the outputted quantiles also contain more error which leads to a higher CRPS. The non-autoregressive model does not suffer from this problem. When comparing the examples from the autoregressive model in Figure \ref{fig:autoregressive_linear_model_samples} and the examples from the non-autoregressive model in Figure \ref{fig:non_autoregressive_linear_model_samples}, it can be seen that the non-autoregressive model has a higher variance in the samples. The confidence intervals are larger and have a lot more peaks. The autoregressive model examples are way more contained in the range of -500 to 500. Some full-day NRV samples are also compared in Figure \ref{fig:linear_model_sample_comparison}. The peaky behavior also appears in the samples from the non-autoregressive model. This is quite logical because the sampled value of the next quarter doesn't depend on the value sampled of the previous quarter. This explains why a lot of peaks can be observed. The autoregressive full-day NRV samples on the other hand, do depend on the previous sampled value. This leads to a smoother curve and fewer peaks. \\\\ % figure with 2 models compared (left with 4 row examples and right with 4 row examples), autoregressive vs non-autoregressive \begin{figure}[ht] \centering \begin{subfigure}[b]{0.49\textwidth} - \includegraphics[width=\textwidth]{images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Example_864_samples.png} - \caption{Autoregressive model} - \label{fig:linear_model_sample_comparison_autoregressive} + \includegraphics[width=\textwidth]{images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP-QE-Example_864_samples.png} \end{subfigure} \hfill \begin{subfigure}[b]{0.49\textwidth} \includegraphics[width=\textwidth]{images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Example_864_samples.png} - \caption{Non-autoregressive model} - \label{fig:linear_model_sample_comparison_non_autoregressive} \end{subfigure} - \caption{Comparison of the autoregressive and non-autoregressive linear models. The plots show the confidence intervals calculated from 1000 generated full-day NRV samples. The samples were generated using input features NRV, Load, Wind, PV, Net Position and the quarter embedding.} + \begin{subfigure}[b]{0.49\textwidth} + \includegraphics[width=\textwidth]{images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP-QE-Example_4320_samples.png} + \end{subfigure} + \hfill + \begin{subfigure}[b]{0.49\textwidth} + \includegraphics[width=\textwidth]{images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Example_4320_samples.png} + \end{subfigure} + \begin{subfigure}[b]{0.49\textwidth} + \includegraphics[width=\textwidth]{images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP-QE-Example_6336_samples.png} + \caption{Autoregressive linear model} + \end{subfigure} + \hfill + \begin{subfigure}[b]{0.49\textwidth} + \includegraphics[width=\textwidth]{images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Example_6336_samples.png} + \caption{Non-autoregressive linear model} + \end{subfigure} + % \begin{subfigure}[b]{0.49\textwidth} + % \includegraphics[width=\textwidth]{images/quantile_regression/aqr_linear_model_samples/AQR_NRV_Load_Wind_PV_NP-QE-Example_7008_samples.png} + % + % \end{subfigure} + % \hfill + % \begin{subfigure}[b]{0.49\textwidth} + % \includegraphics[width=\textwidth]{images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Example_7008_samples.png} + % \end{subfigure} + + \caption{Comparison of the autoregressive and non-autoregressive linear models samples. The plots show ten samples of the full-day NRV for the autoregressive and non-autoregressive linear models. The samples were generated using input features NRV, Load, Wind, PV, and the Net Position. The autoregressive model also uses the quarter embedding with 5 dimensions.} \label{fig:linear_model_sample_comparison} \end{figure} +% TODO: Talk about the over/underestimation of the quantiles for the models. Plots have been made for this. +\subsubsection{Non-linear Model} +More complex models can also be used to model the NRV. The same training and evaluation process can be used as the linear model. The training loop is the same but the model architecture is different. Quantile regression is still used so the model outputs the quantiles for the NRV. The model is trained using the pinball loss function and the MAE, MSE, CRPS metrics are calculated on the test set. A simple feedforward neural network is used as a non-linear model. The model architecture used is shown in Table \ref{tab:non_linear_model_architecture}. First, if the inputs include the quarter of the day, the quarter embedding is calculated and concatenated with the other input features. These features are then passed to a linear layer with a chosen hidden size. The output of this layer is passed through a ReLu activation function and a dropout layer. This process is repeated N times to increase the depth of the model. The output of the last linear layer is the quantiles for the NRV prediction. For an autoregressive model, this is just the quantiles for one quarter. If the model is non-autoregressive, the quantiles for every quarter are outputted. The number of outputs is then the number of quarters in a day multiplied by the number of quantiles used. +\\\\ +\begin{table}[h] +\centering +\begin{tabularx}{\textwidth}{Xr} % Set the table width to the text width +\toprule +\textbf{Layer (Type)} & \textbf{Output Shape} \\ \midrule +Time Embedding (Embedding) & [B, Input Features Size + Time Embedding Size] \\ +\midrule +% Repeated Block +\multicolumn{2}{c}{\textit{Repeated Block (N times)}} \\ +Linear (Linear) & [B, Hidden Size] \\ +ReLU (Activation) & [B, Hidden Size] \\ +Dropout (Regularization) & [B, Hidden Size] \\ +% End of Repeated Block +\midrule +Linear (Linear) & [B, Number of quantiles] \\ +\bottomrule +\end{tabularx} +\caption{Non-linear Quantile Regression Model Architecture Details} +\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. +\subsubsection{GRU Model} +\subsubsection{Comparison} \newpage \subsection{Diffusion} diff --git a/Reports/Thesis/verslag.synctex.gz b/Reports/Thesis/verslag 14.synctex.gz similarity index 87% rename from Reports/Thesis/verslag.synctex.gz rename to Reports/Thesis/verslag 14.synctex.gz index 86f73ee..bba6566 100644 Binary files a/Reports/Thesis/verslag.synctex.gz and b/Reports/Thesis/verslag 14.synctex.gz differ diff --git a/Reports/Thesis/verslag 15.synctex.gz b/Reports/Thesis/verslag 15.synctex.gz new file mode 100644 index 0000000..bf1199e Binary files /dev/null and b/Reports/Thesis/verslag 15.synctex.gz differ diff --git a/Reports/Thesis/verslag 16.synctex.gz b/Reports/Thesis/verslag 16.synctex.gz new file mode 100644 index 0000000..b8b84c4 Binary files /dev/null and b/Reports/Thesis/verslag 16.synctex.gz differ diff --git a/Reports/Thesis/verslag 17.synctex.gz b/Reports/Thesis/verslag 17.synctex.gz new file mode 100644 index 0000000..a52baf8 Binary files /dev/null and b/Reports/Thesis/verslag 17.synctex.gz differ diff --git a/Reports/Thesis/verslag 18.synctex.gz b/Reports/Thesis/verslag 18.synctex.gz new file mode 100644 index 0000000..eb4ef80 Binary files /dev/null and b/Reports/Thesis/verslag 18.synctex.gz differ diff --git a/Reports/Thesis/verslag 19.synctex.gz b/Reports/Thesis/verslag 19.synctex.gz new file mode 100644 index 0000000..75db40d Binary files /dev/null and b/Reports/Thesis/verslag 19.synctex.gz differ diff --git a/Reports/Thesis/verslag 20.synctex.gz b/Reports/Thesis/verslag 20.synctex.gz new file mode 100644 index 0000000..75db40d Binary files /dev/null and b/Reports/Thesis/verslag 20.synctex.gz differ diff --git a/Reports/Thesis/verslag.aux b/Reports/Thesis/verslag.aux index 63c87c9..0696285 100644 --- a/Reports/Thesis/verslag.aux +++ b/Reports/Thesis/verslag.aux @@ -39,11 +39,11 @@ \@writefile{toc}{\contentsline {subsubsection}{\numberline {5.2.2}Evaluation}{15}{subsubsection.5.2.2}\protected@file@percent } \@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Visualization of the CRPS metric\relax }}{16}{figure.caption.6}\protected@file@percent } \newlabel{fig:crps_visualization}{{4}{16}{Visualization of the CRPS metric\relax }{figure.caption.6}{}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {5.2.3}Models}{16}{subsubsection.5.2.3}\protected@file@percent } -\newlabel{tab:autoregressive_linear_model_baseline_results}{{\caption@xref {tab:autoregressive_linear_model_baseline_results}{ on input line 187}}{17}{Models}{table.caption.7}{}} +\@writefile{toc}{\contentsline {subsubsection}{\numberline {5.2.3}Linear Model}{16}{subsubsection.5.2.3}\protected@file@percent } \@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Autoregressive linear model results\relax }}{17}{table.caption.7}\protected@file@percent } -\newlabel{tab:autoregressive_linear_model_quarter_embedding_baseline_results}{{\caption@xref {tab:autoregressive_linear_model_quarter_embedding_baseline_results}{ on input line 223}}{18}{Models}{table.caption.8}{}} +\newlabel{tab:autoregressive_linear_model_baseline_results}{{3}{17}{Autoregressive linear model results\relax }{table.caption.7}{}} \@writefile{lot}{\contentsline {table}{\numberline {4}{\ignorespaces Autoregressive linear model results with time features\relax }}{18}{table.caption.8}\protected@file@percent } +\newlabel{tab:autoregressive_linear_model_quarter_embedding_baseline_results}{{4}{18}{Autoregressive linear model results with time features\relax }{table.caption.8}{}} \newlabel{fig:autoregressive_linear_model_sample_1}{{5a}{19}{Sample 1\relax }{figure.caption.9}{}} \newlabel{sub@fig:autoregressive_linear_model_sample_1}{{a}{19}{Sample 1\relax }{figure.caption.9}{}} \newlabel{fig:autoregressive_linear_model_sample_2}{{5b}{19}{Sample 2\relax }{figure.caption.9}{}} @@ -54,8 +54,8 @@ \newlabel{sub@fig:autoregressive_linear_model_sample_4}{{d}{19}{Sample 4\relax }{figure.caption.9}{}} \@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Test examples of the autoregressive linear model. 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PDF statistics: - 346 PDF objects out of 1000 (max. 8388607) - 270 compressed objects within 3 object streams - 62 named destinations out of 1000 (max. 500000) - 236 words of extra memory for PDF output out of 10000 (max. 10000000) + 387 PDF objects out of 1000 (max. 8388607) + 298 compressed objects within 3 object streams + 67 named destinations out of 1000 (max. 500000) + 285 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/Reports/Thesis/verslag.out b/Reports/Thesis/verslag.out index f868d43..a5d8fe9 100644 --- a/Reports/Thesis/verslag.out +++ b/Reports/Thesis/verslag.out @@ -13,8 +13,11 @@ \BOOKMARK [2][-]{subsection.5.2}{\376\377\000Q\000u\000a\000n\000t\000i\000l\000e\000\040\000R\000e\000g\000r\000e\000s\000s\000i\000o\000n}{section.5}% 13 \BOOKMARK [3][-]{subsubsection.5.2.1}{\376\377\000T\000r\000a\000i\000n\000i\000n\000g}{subsection.5.2}% 14 \BOOKMARK 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[2][-]{subsection.6.2}{\376\377\000P\000o\000l\000i\000c\000i\000e\000s\000\040\000u\000s\000i\000n\000g\000\040\000N\000R\000V\000\040\000p\000r\000e\000d\000i\000c\000t\000i\000o\000n\000s}{section.6}% 23 diff --git a/Reports/Thesis/verslag.pdf b/Reports/Thesis/verslag.pdf index e95e9d6..c06d87e 100644 Binary files a/Reports/Thesis/verslag.pdf and b/Reports/Thesis/verslag.pdf differ diff --git a/Reports/Thesis/verslag.tex b/Reports/Thesis/verslag.tex index b126fdf..794684d 100644 --- a/Reports/Thesis/verslag.tex +++ b/Reports/Thesis/verslag.tex @@ -27,6 +27,7 @@ \usepackage{adjustbox} \usepackage{caption} \usepackage{subcaption} +\usepackage{booktabs} diff --git a/Reports/Thesis/verslag.toc b/Reports/Thesis/verslag.toc index 70034a3..0d428a2 100644 --- a/Reports/Thesis/verslag.toc +++ b/Reports/Thesis/verslag.toc @@ -14,8 +14,11 @@ \contentsline {subsection}{\numberline {5.2}Quantile Regression}{12}{subsection.5.2}% \contentsline {subsubsection}{\numberline {5.2.1}Training}{14}{subsubsection.5.2.1}% \contentsline {subsubsection}{\numberline {5.2.2}Evaluation}{15}{subsubsection.5.2.2}% -\contentsline {subsubsection}{\numberline {5.2.3}Models}{16}{subsubsection.5.2.3}% -\contentsline {subsection}{\numberline {5.3}Diffusion}{22}{subsection.5.3}% -\contentsline {section}{\numberline {6}Policies for battery optimization}{22}{section.6}% -\contentsline {subsection}{\numberline {6.1}Baselines}{22}{subsection.6.1}% -\contentsline {subsection}{\numberline {6.2}Policies using NRV predictions}{22}{subsection.6.2}% +\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}{22}{subsubsection.5.2.5}% +\contentsline {subsubsection}{\numberline {5.2.6}Comparison}{22}{subsubsection.5.2.6}% +\contentsline {subsection}{\numberline {5.3}Diffusion}{23}{subsection.5.3}% +\contentsline {section}{\numberline {6}Policies for battery optimization}{23}{section.6}% +\contentsline {subsection}{\numberline {6.1}Baselines}{23}{subsection.6.1}% +\contentsline {subsection}{\numberline {6.2}Policies using NRV predictions}{23}{subsection.6.2}% diff --git a/src/training_scripts/autoregressive_quantiles.py b/src/training_scripts/autoregressive_quantiles.py index 86f47cc..3dfbdef 100644 --- a/src/training_scripts/autoregressive_quantiles.py +++ b/src/training_scripts/autoregressive_quantiles.py @@ -2,9 +2,7 @@ from src.utils.clearml import ClearMLHelper #### ClearML #### clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast") -task = clearml_helper.get_task( - task_name="AQR: Linear Baseline + Load + PV + Wind + Net Position + Quarter (dim 5)" -) +task = clearml_helper.get_task(task_name="AQR: Non-Linear (2 - 256 - 0.2)") task.execute_remotely(queue_name="default", exit_process=True) from src.policies.PolicyEvaluator import PolicyEvaluator @@ -29,19 +27,20 @@ from src.models.time_embedding_layer import TimeEmbedding, TrigonometricTimeEmbe data_config = DataConfig() data_config.NRV_HISTORY = True -data_config.LOAD_HISTORY = True -data_config.LOAD_FORECAST = True -data_config.WIND_FORECAST = True -data_config.WIND_HISTORY = True +data_config.LOAD_HISTORY = False +data_config.LOAD_FORECAST = False -data_config.PV_FORECAST = True -data_config.PV_HISTORY = True +data_config.WIND_FORECAST = False +data_config.WIND_HISTORY = False -data_config.QUARTER = True +data_config.PV_FORECAST = False +data_config.PV_HISTORY = False + +data_config.QUARTER = 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") @@ -91,26 +90,25 @@ time_embedding = TimeEmbedding( # dropout=model_parameters["dropout"], # ) -# non_linear_model = NonLinearRegression( -# time_embedding.output_dim(inputDim), -# len(quantiles), -# hiddenSize=model_parameters["hidden_size"], -# numLayers=model_parameters["num_layers"], -# dropout=model_parameters["dropout"], -# ) +non_linear_model = NonLinearRegression( + time_embedding.output_dim(inputDim), + len(quantiles), + hiddenSize=model_parameters["hidden_size"], + numLayers=model_parameters["num_layers"], + dropout=model_parameters["dropout"], +) -linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles)) -# linear_model = LinearRegression(inputDim, len(quantiles)) +# linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles)) + +model = nn.Sequential(time_embedding, non_linear_model) -model = nn.Sequential(time_embedding, linear_model) -# model = linear_model model.output_size = 1 optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"]) ### Policy Evaluator ### -battery = Battery(2, 1) -baseline_policy = BaselinePolicy(battery, data_path="") -policy_evaluator = PolicyEvaluator(baseline_policy, task) +# battery = Battery(2, 1) +# baseline_policy = BaselinePolicy(battery, data_path="") +# policy_evaluator = PolicyEvaluator(baseline_policy, task) #### Trainer #### trainer = AutoRegressiveQuantileTrainer(