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Victor Mylle
2024-04-16 22:07:53 +02:00
parent 937b6abc0b
commit 0edcc91e65
12 changed files with 214 additions and 36 deletions
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2
Reports/Thesis/sections/nrv_prediction.tex
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@@ -162,7 +162,7 @@ where:
\item \(\beta_{0,\tau}, \beta_{1,\tau}, \beta_{2,\tau}, \ldots, \beta_{n,\tau} \) are the coefficients
\end{itemize}
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 = 1248.
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.
TODO: add results for this model

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Reports/Thesis/verslag.pdf
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Reports/Thesis/verslag.synctex.gz
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2
src/notebooks/thesis-visualizations.ipynb
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@@ -171,7 +171,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
"version": "3.undefined.undefined"
}
},
"nbformat": 4,

98
src/policies/PolicyEvaluator.py
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@@ -44,8 +44,8 @@ class PolicyEvaluator:
date,
idx_samples,
test_loader,
charge_thresholds=np.arange(-1500, 1500, 50),
discharge_thresholds=np.arange(-1500, 1500, 50),
charge_thresholds=np.arange(-1000, 1000, 5),
discharge_thresholds=np.arange(-1000, 1000, 5),
penalty: int = 0,
state_of_charge: float = 0.0,
):
@@ -96,6 +96,7 @@ class PolicyEvaluator:
max_iterations=10,
tolerance=10,
learning_rate_decay=0.9, # Factor to reduce the learning rate after each iteration
iteration=0,
):
self.cache = {}
penalty = initial_penalty
@@ -139,7 +140,7 @@ class PolicyEvaluator:
# Re-calculate profit and charge cycles for the final penalty to return accurate results
profit, charge_cycles = self.evaluate_test_set_for_penalty(
idx_samples, test_loader, penalty
idx_samples, test_loader, penalty, log_metrics=True, iteration=iteration
)
return penalty, profit, charge_cycles
@@ -232,25 +233,45 @@ class PolicyEvaluator:
],
)
def evaluate_test_set_for_penalty(self, idx_samples, test_loader, penalty):
def evaluate_test_set_for_penalty(
self, idx_samples, test_loader, penalty, log_metrics=False, iteration: int = 0
):
total_profit = 0
total_charge_cycles = 0
state_of_charge = 0.0
loggings = []
for date in tqdm(self.dates):
try:
profit, charge_cycles, _, _, new_state_of_charge = (
self.evaluate_for_date(
date,
idx_samples,
test_loader,
penalty=penalty,
state_of_charge=state_of_charge,
)
(
profit,
charge_cycles,
charge_thresholds,
discharge_thresholds,
new_state_of_charge,
) = self.evaluate_for_date(
date,
idx_samples,
test_loader,
penalty=penalty,
state_of_charge=state_of_charge,
)
state_of_charge = new_state_of_charge
total_profit += profit
total_charge_cycles += charge_cycles
new_info = {
"Date": date,
"Profit": profit,
"Charge Cycles": charge_cycles,
"State of Charge": state_of_charge,
"Charge Threshold": charge_thresholds,
"Discharge Threshold": discharge_thresholds,
}
loggings.append(new_info)
except KeyboardInterrupt:
print("Interrupted")
raise KeyboardInterrupt
@@ -259,6 +280,59 @@ class PolicyEvaluator:
print(e)
pass
if log_metrics:
log_df = pd.DataFrame(loggings)
fig = px.line(
log_df,
x="Date",
y="Profit",
title="Profit over time",
labels={"Profit": "Profit (€)", "Date": "Date"},
)
self.task.get_logger().report_plotly(
"Profit", "Profit", iteration=iteration, figure=fig
)
fig = px.line(
log_df,
x="Date",
y="Charge Cycles",
title="Charge Cycles over time",
labels={"Charge Cycles": "Charge Cycles", "Date": "Date"},
)
self.task.get_logger().report_plotly(
"Charge Cycles", "Charge Cycles", iteration=iteration, figure=fig
)
fig = px.line(
log_df,
x="Date",
y="State of Charge",
title="State of Charge over time",
labels={"State of Charge": "State of Charge", "Date": "Date"},
)
self.task.get_logger().report_plotly(
"State of Charge", "State of Charge", iteration=iteration, figure=fig
)
fig = px.line(
log_df,
x="Date",
y=["Charge Threshold", "Discharge Threshold"],
title="Charge and Discharge Thresholds per Day",
)
self.task.get_logger().report_plotly(
"Thresholds per Day",
"Thresholds per Day",
iteration=iteration,
figure=fig,
)
return total_profit, total_charge_cycles
def plot_profits_table(self):

14
src/policies/baselines/PerfectBaseline.py
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@@ -1,5 +1,5 @@
from clearml import Task
from policies.simple_baseline import BaselinePolicy
from src.policies.simple_baseline import BaselinePolicy
from src.policies.baselines.YesterdayBaselinePolicyExecutor import (
YesterdayBaselinePolicyEvaluator,
)
@@ -14,17 +14,21 @@ class PerfectBaseline(YesterdayBaselinePolicyEvaluator):
def evaluate_for_date(
self,
date,
charge_thresholds=np.arange(-100, 250, 25),
discharge_thresholds=np.arange(-100, 250, 25),
charge_thresholds=np.arange(-300, 300, 5),
discharge_thresholds=np.arange(-300, 300, 5),
penalty: int = 0,
current_state_of_charge=0.0,
):
real_imbalance_prices = self.get_imbanlance_prices_for_date(date.date())
real_imbalance_prices_tensor = torch.tensor(
np.array([real_imbalance_prices]), device="cpu"
)
best_charge_thresholds, best_discharge_thresholds = (
self.baseline_policy.get_optimal_thresholds(
real_imbalance_prices,
real_imbalance_prices_tensor,
charge_thresholds,
discharge_thresholds,
penalty,
@@ -45,4 +49,6 @@ class PerfectBaseline(YesterdayBaselinePolicyEvaluator):
best_profit[0][0].item(),
best_charge_cycles[0][0].item(),
new_state_of_charge.squeeze(0).item(),
best_charge_thresholds.mean(axis=0),
best_discharge_thresholds.mean(axis=0),
)

105
src/policies/baselines/YesterdayBaselinePolicyExecutor.py
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@@ -6,6 +6,7 @@ import numpy as np
import pandas as pd
from tqdm import tqdm
import torch
import plotly.express as px
class YesterdayBaselinePolicyEvaluator(PolicyEvaluator):
@@ -15,8 +16,8 @@ class YesterdayBaselinePolicyEvaluator(PolicyEvaluator):
def evaluate_for_date(
self,
date,
charge_thresholds=np.arange(-100, 250, 25),
discharge_thresholds=np.arange(-100, 250, 25),
charge_thresholds=np.arange(-500, 500, 5),
discharge_thresholds=np.arange(-500, 500, 5),
penalty: int = 0,
current_state_of_charge=0.0,
):
@@ -52,9 +53,13 @@ class YesterdayBaselinePolicyEvaluator(PolicyEvaluator):
yesterday_profit[0][0].item(),
yesterday_charge_cycles[0][0].item(),
new_state_of_charge.squeeze(0).item(),
yesterday_charge_thresholds.mean(axis=0),
yesterday_discharge_thresholds.mean(axis=0),
)
def evaluate_test_set_for_penalty(self, data_processor, penalty: int = 0):
def evaluate_test_set_for_penalty(
self, data_processor, penalty: int = 0, log_metrics=False
):
if data_processor:
filtered_dates = []
@@ -71,20 +76,89 @@ class YesterdayBaselinePolicyEvaluator(PolicyEvaluator):
charge_cycles = 0
state_of_charge = 0.0
loggings = []
for date in tqdm(self.dates):
try:
new_profit, new_charge_cycles, new_state_of_charge = (
self.evaluate_for_date(
date, penalty=penalty, current_state_of_charge=state_of_charge
)
(
new_profit,
new_charge_cycles,
new_state_of_charge,
charge_threshold,
discharge_threshold,
) = self.evaluate_for_date(
date, penalty=penalty, current_state_of_charge=state_of_charge
)
profit += new_profit
charge_cycles += new_charge_cycles
state_of_charge = new_state_of_charge
new_info = {
"Date": date,
"Profit": profit,
"Charge Cycles": charge_cycles,
"State of Charge": state_of_charge,
"Charge Threshold": charge_threshold.item(),
"Discharge Threshold": discharge_threshold.item(),
}
loggings.append(new_info)
except Exception as e:
print(e)
pass
if log_metrics:
log_df = pd.DataFrame(loggings)
fig = px.line(
log_df,
x="Date",
y="Profit",
title="Profit over time",
labels={"Profit": "Profit (€)", "Date": "Date"},
)
self.task.get_logger().report_plotly(
"Profit", "Profit", iteration=0, figure=fig
)
fig = px.line(
log_df,
x="Date",
y="Charge Cycles",
title="Charge Cycles over time",
labels={"Charge Cycles": "Charge Cycles", "Date": "Date"},
)
self.task.get_logger().report_plotly(
"Charge Cycles", "Charge Cycles", iteration=0, figure=fig
)
fig = px.line(
log_df,
x="Date",
y="State of Charge",
title="State of Charge over time",
labels={"State of Charge": "State of Charge", "Date": "Date"},
)
self.task.get_logger().report_plotly(
"State of Charge", "State of Charge", iteration=0, figure=fig
)
fig = px.line(
log_df,
x="Date",
y=["Charge Threshold", "Discharge Threshold"],
title="Charge and Discharge Thresholds per Day",
)
self.task.get_logger().report_plotly(
"Thresholds per Day", "Thresholds per Day", iteration=0, figure=fig
)
return profit, charge_cycles
def optimize_penalty_for_target_charge_cycles(
@@ -108,6 +182,21 @@ class YesterdayBaselinePolicyEvaluator(PolicyEvaluator):
f"Penalty: {penalty}, Charge Cycles: {simulated_charge_cycles}, Profit: {simulated_profit}"
)
self.task.get_logger().report_scalar(
"Penalty", "Penalty", penalty, iteration=iteration
)
self.task.get_logger().report_scalar(
"Charge Cycles",
"Charge Cycles",
simulated_charge_cycles,
iteration=iteration,
)
self.task.get_logger().report_scalar(
"Profit", "Profit", simulated_profit, iteration=iteration
)
# Calculate the gradient (difference) between the simulated and target charge cycles
gradient = simulated_charge_cycles - target_charge_cycles
@@ -125,7 +214,7 @@ class YesterdayBaselinePolicyEvaluator(PolicyEvaluator):
# Re-calculate profit and charge cycles for the final penalty to return accurate results
profit, charge_cycles = self.evaluate_test_set_for_penalty(
data_processor, penalty
data_processor, penalty, log_metrics=True
)
return penalty, profit, charge_cycles

11
src/policies/baselines/global_threshold_baseline.py
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@@ -55,8 +55,15 @@ charge_discharge_threshold, total_profit, total_charge_cycles = (
policy_evaluator.determine_best_thresholds_test_set(data_processor)
)
task.get_logger().report_single_value(name="Optimal Profit", value=profit)
task.get_logger().report_single_value(name="Optimal Charge Cycles", value=charge_cycles)
print("Thresholds determined on test set")
print(f"Best Charge Discharge Threshold: {charge_discharge_threshold}")
print(f"Total Profit: {total_profit}")
print(f"Total Charge Cycles: {total_charge_cycles}")
task.get_logger().report_single_value(name="Optimal Profit", value=total_profit)
task.get_logger().report_single_value(
name="Optimal Charge Cycles", value=total_charge_cycles
)
task.get_logger().report_single_value(
name="Optimal Charge Threshold", value=charge_discharge_threshold[0]
)

2
src/policies/baselines/perfect_baseline.py
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@@ -7,7 +7,7 @@ task.execute_remotely(queue_name="default", exit_process=True)
from src.policies.simple_baseline import BaselinePolicy, Battery
from src.data import DataProcessor, DataConfig
from policies.baselines.PerfectBaseline import PerfectBaseline
from src.policies.baselines.PerfectBaseline import PerfectBaseline
### Data Processor ###
data_config = DataConfig()

9
src/trainers/diffusion_trainer.py
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@@ -208,7 +208,7 @@ class DiffusionTrainer:
running_loss /= len(train_loader.dataset)
if epoch % 150 == 0 and epoch != 0:
if epoch % 75 == 0 and epoch != 0:
crps, _ = self.test(test_loader, epoch, task)
if best_crps is None or crps < best_crps:
@@ -217,7 +217,7 @@ class DiffusionTrainer:
else:
early_stopping += 1
if early_stopping > 15:
if early_stopping > 5:
break
if task:
@@ -249,7 +249,7 @@ class DiffusionTrainer:
test_loader=test_loader,
initial_penalty=900,
target_charge_cycles=283,
learning_rate=1,
initial_learning_rate=1,
max_iterations=50,
tolerance=1,
)
@@ -438,9 +438,10 @@ class DiffusionTrainer:
test_loader=test_loader,
initial_penalty=self.prev_optimal_penalty,
target_charge_cycles=283,
learning_rate=1,
initial_learning_rate=1,
max_iterations=50,
tolerance=1,
iteration=epoch,
)
)

5
src/trainers/quantile_trainer.py
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@@ -192,9 +192,10 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
test_loader=dataloader,
initial_penalty=900,
target_charge_cycles=283,
learning_rate=2,
initial_learning_rate=5,
max_iterations=100,
tolerance=1,
iteration=epoch,
)
)
@@ -823,7 +824,7 @@ class NonAutoRegressiveQuantileRegression(Trainer):
test_loader=dataloader,
initial_penalty=500,
target_charge_cycles=283,
learning_rate=2,
initial_learning_rate=2,
max_iterations=100,
tolerance=1,
)

2
src/training_scripts/autoregressive_quantiles.py
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@@ -113,7 +113,7 @@ trainer = AutoRegressiveQuantileTrainer(
data_processor,
quantiles,
"cuda",
policy_evaluator=policy_evaluator,
policy_evaluator=None,
debug=False,
)