Added non autoregressive examples to thesis
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@@ -283,11 +283,45 @@ NRV + Load + Wind + Net Position & 39505.20 & 49442.48 & 151.65 & 167.90 & 68.69
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\caption{Non-Autoregressive linear model results}
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\end{table}
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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}.
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\begin{figure}[ht]
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\centering
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Sample_864.png}
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\caption{Sample 1}
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\label{fig:non_autoregressive_linear_model_sample_1}
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\end{subfigure}
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\hfill
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Sample_4320.png}
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\caption{Sample 2}
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\label{fig:non_autoregressive_linear_model_sample_2}
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\end{subfigure}
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\hfill
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Sample_6336.png}
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\caption{Sample 3}
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\label{fig:non_autoregressive_linear_model_sample_3}
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\end{subfigure}
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\hfill
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\begin{subfigure}[b]{0.49\textwidth}
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\includegraphics[width=\textwidth]{images/quantile_regression/naqr_linear_model_samples/NAQR_NRV_Load_Wind_PV_NP-Sample_7008.png}
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\caption{Sample 4}
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\label{fig:non_autoregressive_linear_model_sample_4}
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\end{subfigure}
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\caption{Test examples of the non-autoregressive linear model. 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 and the Net Position.}
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\label{fig:non_autoregressive_linear_model_samples}
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\end{figure}
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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.
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% TODO: Is this reasoning correct? + Explain the results more we see in the table (Weird results :( )
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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.
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\\\\
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\newpage
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\subsection{Diffusion}
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\subsection{Diffusion}
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@@ -55,10 +55,20 @@
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\newlabel{fig:autoregressive_linear_model_samples}{{5}{19}{Test examples of the autoregressive linear model. 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.\relax }{figure.caption.9}{}}
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\newlabel{tab:non_autoregressive_linear_model_baseline_results}{{\caption@xref {tab:non_autoregressive_linear_model_baseline_results}{ on input line 282}}{20}{Models}{table.caption.10}{}}
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\newlabel{fig:non_autoregressive_linear_model_sample_1}{{6a}{20}{Sample 1\relax }{figure.caption.11}{}}
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\newlabel{sub@fig:non_autoregressive_linear_model_sample_1}{{a}{20}{Sample 1\relax }{figure.caption.11}{}}
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\newlabel{fig:non_autoregressive_linear_model_sample_2}{{6b}{20}{Sample 2\relax }{figure.caption.11}{}}
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\newlabel{sub@fig:non_autoregressive_linear_model_sample_2}{{b}{20}{Sample 2\relax }{figure.caption.11}{}}
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\newlabel{fig:non_autoregressive_linear_model_sample_3}{{6c}{20}{Sample 3\relax }{figure.caption.11}{}}
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\newlabel{sub@fig:non_autoregressive_linear_model_sample_3}{{c}{20}{Sample 3\relax }{figure.caption.11}{}}
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\newlabel{fig:non_autoregressive_linear_model_sample_4}{{6d}{20}{Sample 4\relax }{figure.caption.11}{}}
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\newlabel{sub@fig:non_autoregressive_linear_model_sample_4}{{d}{20}{Sample 4\relax }{figure.caption.11}{}}
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\newlabel{fig:non_autoregressive_linear_model_samples}{{6}{20}{Test examples of the non-autoregressive linear model. 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 and the Net Position.\relax }{figure.caption.11}{}}
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@@ -15,7 +15,7 @@
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\contentsline {subsubsection}{\numberline {5.2.1}Training}{14}{subsubsection.5.2.1}%
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\contentsline {subsubsection}{\numberline {5.2.2}Evaluation}{15}{subsubsection.5.2.2}%
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\contentsline {subsubsection}{\numberline {5.2.3}Models}{16}{subsubsection.5.2.3}%
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\contentsline {subsection}{\numberline {5.3}Diffusion}{21}{subsection.5.3}%
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\contentsline {section}{\numberline {6}Policies for battery optimization}{21}{section.6}%
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\contentsline {subsection}{\numberline {6.1}Baselines}{21}{subsection.6.1}%
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\contentsline {subsection}{\numberline {6.2}Policies using NRV predictions}{21}{subsection.6.2}%
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\contentsline {subsection}{\numberline {5.3}Diffusion}{22}{subsection.5.3}%
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\contentsline {section}{\numberline {6}Policies for battery optimization}{22}{section.6}%
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\contentsline {subsection}{\numberline {6.1}Baselines}{22}{subsection.6.1}%
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\contentsline {subsection}{\numberline {6.2}Policies using NRV predictions}{22}{subsection.6.2}%
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@@ -2,9 +2,7 @@ from src.utils.clearml import ClearMLHelper
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#### ClearML ####
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clearml_helper = ClearMLHelper(project_name="Thesis/NAQR: Linear")
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task = clearml_helper.get_task(
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task_name="NAQR: Linear + Load + PV + Wind + Net Position"
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)
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task = clearml_helper.get_task(task_name="NAQR: Linear (extra test)")
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task.execute_remotely(queue_name="default", exit_process=True)
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from src.policies.PolicyEvaluator import PolicyEvaluator
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@@ -29,16 +27,16 @@ from src.models.time_embedding_layer import TimeEmbedding
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data_config = DataConfig()
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data_config.NRV_HISTORY = True
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data_config.LOAD_HISTORY = True
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data_config.LOAD_FORECAST = True
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data_config.LOAD_HISTORY = False
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data_config.LOAD_FORECAST = False
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data_config.WIND_FORECAST = True
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data_config.WIND_HISTORY = True
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data_config.WIND_FORECAST = False
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data_config.WIND_HISTORY = False
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data_config.PV_FORECAST = True
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data_config.PV_HISTORY = True
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data_config.PV_FORECAST = False
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data_config.PV_HISTORY = False
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data_config.NOMINAL_NET_POSITION = True
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data_config.NOMINAL_NET_POSITION = False
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data_config = task.connect(data_config, name="data_features")
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