Updated Thesis and linear baseline

Result-Reports/Policies.md

@@ -216,4 +216,32 @@ Baseline -> thresholds bepalen training data, penalty aanpassen na evaluatie op
 
 # profit evaluation done day by day. Start with fresh battery. Maybe electritiy bought but not sold -> negative profits? What to do with this?
 2 solutions: - work further with the state of charge of the battery
-             - don't count the electricity bought but not sold in the profit calculation
+             - don't count the electricity bought but not sold in the profit calculation
+
+
+
+
+
+
+
+
+# Global baseline:
+uitleggen
+
+-> penalty bepalen aan de hand van de test set
+
+# Perfect baseline:
+uitleggen + niet absoluut maximum, normaal integer linear programming nodig
+
+Simple linear model 
+
+# forecast inputs 
+
+
+Plotten van thresholds over de test set (baseline, GRU, diffusion)
+Plotten van state of charge
+
+State of charge waarde van batterij wordt niet meegenomen in threshold bepaling, enkel de verkochte elektriciteit. -> simpele policy maar zelfde policy voor baselines en complexere modellen
+
+
+# Finer thresholds for the models

Result-Reports/Profit.md

@@ -1,3 +1,21 @@
-| Experiment | Model Type | Input Parameters | Profit | Charge Cycles |
-|------------|------------|------------------|--------|----------------|
-| [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/432396923e354d579494048656228c32/info-output/metrics/scalar) | Diffusion Model | 
+# Results
+## Global Baseline
+2 static thresholds determined to buy and sell electricity
+
+| Tuned on | Charge Threshold | Discharge Threshold | Profit | Charge Cycles (target 283) |
+|----------|------------------|---------------------|--------|---------------|
+| Training data | 100 | 200 | 266294.15 | 492.0 |
+| Test data | 200 | 250 | 143004.34 | 287.125 |
+
+## Yesterday NRV Baseline
+Thresholds are determiend on the reconstructed prices using the NRV of yesterday.
+
+| Penalty | Profit | Charge Cycles (target 283) |
+|---|---|---|
+| 807.25 | 200771.44 | 282.5 | 
+
+## Trained models
+| Model | Experiment | Profit | Charge Cycles (target 283) |
+|---|---|---|---|
+| Diffusion Model | [Link](https://clearml.victormylle.be/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/8487afb705bf47b78f3f013c19394da7/info-output/metrics/scalar?columns=selected&columns=type&columns=name&columns=tags&columns=status&columns=project.name&columns=users&columns=started&columns=last_update&columns=last_iteration&columns=m.290612199861c31d1036b185b4e69b75.0fa0d9b8ebcf2a3d9c95c22cd6e72912.value.Summary.OptimalProfit&columns=m.290612199861c31d1036b185b4e69b75.6d53291d99dd32fc27ecee29939046ec.value.Summary.OptimalChargeCycles&columns=m.0fa0d9b8ebcf2a3d9c95c22cd6e72912.098f6bcd4621d373cade4e832627b4f6.max_value.Optimal%20Profit.test&columns=m.1a899a19b54957e02a21c3a1d82577ad.0970ca62a85af2722008c5220e9d8a9e.value.Summary%2Ftest_CRPSLoss.lastreported&columns=m.293da6b015ca6a65992dcf7a53fa0237.098f6bcd4621d373cade4e832627b4f6.min_value.PinballLoss.test&order=-started&filter=) | 219848.9 | 283.06 |
+|