Finished intermediate february report + next steps from meeting

Reports/February/verslag.aux

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Reports/February/verslag.log

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Reports/February/verslag.out

@@ -1,5 +1,5 @@
 \BOOKMARK [1][-]{section.1}{Intermediate Results}{}% 1
-\BOOKMARK [2][-]{subsection.1.1}{Net Regulation Volume Moddeling}{section.1}% 2
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 \BOOKMARK [3][-]{subsubsection.1.1.1}{Input Features}{subsection.1.1}% 3
 \BOOKMARK [3][-]{subsubsection.1.1.2}{Models}{subsection.1.1}% 4
 \BOOKMARK [3][-]{subsubsection.1.1.3}{Charging Policy}{subsection.1.1}% 5

Reports/February/verslag.tex

@@ -133,7 +133,7 @@
 
 \section{Intermediate Results}
 
-\subsection{Net Regulation Volume Moddeling}
+\subsection{Net Regulation Volume Modeling}
 Using a generative model, we try to predict the NRV for the next day. The model is trained on historical data and uses multiple input features to model the NRV. The data for the input features can all be downloaded from \href{https://www.elia.be/en/grid-data/open-data}{Elia Open Data}.
 
 \subsubsection{Input Features}
@@ -181,7 +181,7 @@ Using the predicted NRV, a policy can be implemented to charge and discharge a b
     \hline
     \textbf{Policy} & \textbf{Total Profit (€)} & \textbf{Charge Cycles} \\
     \hline
-    Baseline (charge: 150, discharge: 175) & 251,202.59 & 725 \\
+    Baseline (charge: €150, discharge: €175) & 251,202.59 & 725 \\
     Baseline (yesterday imbalance price) & 342,980.09 & 903 \\
     GRU Predicted NRV (mean thresholds) & 339,846.91 & 842 \\
     Diffusion Predicted NRV (mean thresholds) & 338,168.03 & 886 \\
@@ -191,10 +191,23 @@ Using the predicted NRV, a policy can be implemented to charge and discharge a b
     \label{table:energy_storage_policies}
 \end{table}
 
-The recommended charge cycles for a battery are <400 cycles per year. The policy also needs to make the charge cycles are below 400. A penalty parameter can be introduced and determined so that the policy is penalized for every charge cycle above 400. The policy can then be optimized using this penalty parameter. I am currenlty experimenting with this.
+The recommended charge cycles for a battery is <400 cycles per year. The policy also needs to take this into account. A penalty parameter can be introduced and determined so that the policy is penalized for every charge cycle above 400. The policy can then be optimized using this penalty parameter. I am currenlty experimenting with this.
+
 
 \newpage
 \section{Schedule next months}
+\begin{itemize}
+    \item Baselines with penalties for charge cycles above 400
+    \item Better visualizations of the policy profit results.
+    \item Case studies of days with extreme thresholds
+    
+    \item Finetuning of models and hyperparametres based on model errors and profits of the policy
+    \item Ablation study of input features
+    
+    \item Experiment further with diffusion models
+    
+    \item During the experimenting, I will write my thesis and update the results and conclusions chapters.
+\end{itemize}
 
 \end{document}
 

Result-Reports/Policies.md

@@ -154,4 +154,51 @@ Test data: 01-01-2023 until 08-10–2023
 
 - [ ] time steps reducing for diffusion model (UNet activation functions?)
 
-- [ ] (State space model? S4)
+- [ ] (State space model? S4)
+
+
+
+TODO:
+- [ ] diffusion model oefening generative models vragen
+- [ ] Non autoregressive models policy testen (Non Linear eerst) -> als dit al slect, niet verder kijken, wel vermelden
+- [ ] Policy in test set -> over charge cycles (stop trading electricity)
+
+- [ ] penalty bepalen op training data
+
+- [ ] cycles en profit herschalen naar per jaar
+
+
+baseline -> NRV van gisteren gebruiken om thresholds te bepalen voor vandaag
+andere policies -> NRV van vandaag voorspellen met model en thresholds bepalen voor vandaag
+
+
+Eerste baseline -> thresholds bepalen op training data maar ook stoppen als 400 cycles (herschalen) per jaar bereikt zijn -> thresholds zouden anders moeten zijn (Ook met penalty parameter)
+-> deze toepassen op test set (ook stoppen als 400/jaar bereikt zijn)
+
+
+Visualizatie van thresholds over test set voor baselines en complexere modellen -> zonder penalties tonen
+
+
+
+
+1 a 2 Case studies (extreme gevallen, thresholds 150, -5, normale mss)
+
+- Generatie van NRV (echte NRV)
+- Thresholds die eruit komen
+- Profit en charge cycles
+
+
+
+Policy volledig fixen en later training script met policy direct erachter (tijdens schrijven door laten runnen)
+
+1) Policy
+2) Finetuning van modellen (+ vergelijken met elkaar opbv profit en error)
+3) Ablation Study (input features weghalen en kijken wat er gebeurt)
+( 4) Diffusion tussendoor )  
+
+
+Inleiding +
+Literatuurstudie +
+Tabellen die we gaan bespreken -> updaten met nieuwe data dan
+
+Nog eens 3e meeting opbrengen voor 2e deel maart.