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Updated thesis

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Victor Mylle
2024-04-25 14:08:43 +02:00
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2
Reports/Thesis/sections/nrv_prediction.tex
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@@ -140,7 +140,7 @@ The MAE and MSE metrics do not compare the distribution of the NRV to the real N
The mean CRPS can be calculated over the different days to get a single value. The lower this value, the better the NRV is modeled. The CRPS metric can be visualized as shown in figure \ref{fig:crps_visualization}. The CRPS is the area between the predicted cumulative distribution function and the Heavyside function. The lower the area between the curves, the better the NRV is modeled.
TODO: improve visualisation?
TODO: improve visualisation? -> echte NRV + y as cummulative prob
\begin{figure}[H]
\centering
\includegraphics[width=0.8\textwidth]{images/quantile_regression/crps_visualization.png}

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Reports/Thesis/verslag.log
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Reports/Thesis/verslag.tex
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@@ -29,7 +29,26 @@
\usepackage{subcaption}
\usepackage{booktabs}
% Electricity market
% Generative Modeling
% -> Quantile Regression
% -> Autoregressive vs non autoregressive
% -> Modellen (linear, non linear, gru)
% -> Diffusion (1 grote)
% Policies (globaal, hoe winst maken) Wij heel simpele, tonen dat NRV generaties nut hebben. Reinforcement learning voor complexere
% -> Baseline Policies
% -> Policies based on generations (NRV is nu full day samples)
% Waarom nuttig om toekomst te modellen
% Results & discussion
% -> Per model resultaten
% -> Comparison between models
% Conclusion
% plot mean and std for averaged NRV over all days
% Autoregressive models krijgen enkels voorspelde waardes voor dat kwartier, waarom niet van kwartieren erna ook? Uitleg: voor laatste kwartier van de dag, voorspelling van de dag erna nodig. Anders extra padding.
% Non autoregressive vs autoregressive. Autoregressive weet niet dat hij T+1 ... T+96 moet voorspellen. Denkt dat hij enkel T+1 voorspelt. Te overconfident in voorspellingen voor input met error.
\newcolumntype{C}{>{\centering\arraybackslash}X}
@@ -158,6 +177,7 @@
\include{sections/literature_study}
% In introduction
\section{TODO: Better title for this section}
This thesis can be divided into two main parts. The first part focuses on modeling the Net Regulation Volume (NRV) of the Belgian electricity market for the next day. This modeling is conditioned on multiple inputs that can be obtained from Elia (TODO: add citation to the open data of Elia). The second part of the thesis focuses on optimizing a simple policy using the NRV generations for the next day. The policy tries to maximize profit by charging and discharging a battery and thereby buying and selling electricity on the market. Multiple models are trained and tested to model the NRV and compared to each other based on their profit optimization.

49
src/trainers/diffusion_trainer.py
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@@ -242,30 +242,31 @@ class DiffusionTrainer:
_, generated_sampels = self.test(test_loader, -1, task)
# self.policy_evaluator.plot_profits_table()
if self.policy_evaluator:
optimal_penalty, profit, charge_cycles = (
self.policy_evaluator.optimize_penalty_for_target_charge_cycles(
idx_samples=generated_sampels,
test_loader=test_loader,
initial_penalty=900,
target_charge_cycles=283,
initial_learning_rate=1,
max_iterations=50,
tolerance=1,
optimal_penalty, profit, charge_cycles = (
self.policy_evaluator.optimize_penalty_for_target_charge_cycles(
idx_samples=generated_sampels,
test_loader=test_loader,
initial_penalty=900,
target_charge_cycles=283,
initial_learning_rate=1,
max_iterations=50,
tolerance=1,
)
)
)
print(
f"Optimal Penalty: {optimal_penalty}, Profit: {profit}, Charge Cycles: {charge_cycles}"
)
print(
f"Optimal Penalty: {optimal_penalty}, Profit: {profit}, Charge Cycles: {charge_cycles}"
)
task.get_logger().report_single_value(
name="Optimal Penalty", value=optimal_penalty
)
task.get_logger().report_single_value(name="Optimal Profit", value=profit)
task.get_logger().report_single_value(
name="Optimal Charge Cycles", value=charge_cycles
)
task.get_logger().report_single_value(
name="Optimal Penalty", value=optimal_penalty
)
task.get_logger().report_single_value(name="Optimal Profit", value=profit)
task.get_logger().report_single_value(
name="Optimal Charge Cycles", value=charge_cycles
)
if task:
task.close()
@@ -436,13 +437,13 @@ class DiffusionTrainer:
inversed_samples_batched - inversed_expanded_targets
)
inversed_mae_mean = inversed_mae.mean()
all_inversed_mae.extend(inversed_mae_mean.tolist())
all_inversed_mae.append(inversed_mae_mean)
inversed_mse = np.square(
inversed_samples_batched - inversed_expanded_targets
)
inversed_mse_mean = inversed_mse.mean()
all_inversed_mse.extend(inversed_mse_mean.tolist())
all_inversed_mse.append(inversed_mse_mean)
# add all values from crps_mean to all_crps
all_crps.extend(crps_mean.tolist())
@@ -460,12 +461,12 @@ class DiffusionTrainer:
mean_inversed_mae = np.array(all_inversed_mae).mean()
task.get_logger().report_single_value(
name="test_MSELoss", value=mean_inversed_mae
name="test_L1Loss", value=mean_inversed_mae
)
mean_inversed_mse = np.array(all_inversed_mse).mean()
task.get_logger().report_single_value(
name="test_L1Loss", value=mean_inversed_mse
name="test_MSELoss", value=mean_inversed_mse
)
if self.best_score is None or mean_crps < self.best_score:

25
src/training_scripts/diffusion_training.py
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@@ -2,7 +2,7 @@ from src.utils.clearml import ClearMLHelper
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(
task_name="Diffusion Training: hidden_sizes=[256, 256], lr=0.0001, time_dim=8"
task_name="Diffusion Training: hidden_sizes=[256, 256], lr=0.0001, time_dim=8 + Load + PV + Wind + NP"
)
task.execute_remotely(queue_name="default", exit_process=True)
@@ -19,16 +19,19 @@ from src.policies.PolicyEvaluator import PolicyEvaluator
data_config = DataConfig()
data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = False
data_config.LOAD_FORECAST = False
data_config.LOAD_HISTORY = True
data_config.LOAD_FORECAST = True
data_config.WIND_FORECAST = False
data_config.WIND_HISTORY = False
data_config.PV_FORECAST = True
data_config.PV_HISTORY = True
data_config.QUARTER = False
data_config.DAY_OF_WEEK = False
data_config.WIND_FORECAST = True
data_config.WIND_HISTORY = True
data_config.NOMINAL_NET_POSITION = False
data_config.QUARTER = True
data_config.DAY_OF_WEEK = True
data_config.NOMINAL_NET_POSITION = True
data_config = task.connect(data_config, name="data_features")
@@ -42,7 +45,7 @@ print("Input dim: ", inputDim)
model_parameters = {
"epochs": 15000,
"learning_rate": 0.0001,
"hidden_sizes": [256, 256],
"hidden_sizes": [256, 256, 256],
"time_dim": 8,
}
@@ -70,7 +73,5 @@ baseline_policy = BaselinePolicy(battery, data_path="")
policy_evaluator = PolicyEvaluator(baseline_policy, task)
#### Trainer ####
trainer = DiffusionTrainer(
model, data_processor, "cuda", policy_evaluator=policy_evaluator
)
trainer = DiffusionTrainer(model, data_processor, "cuda", policy_evaluator=None)
trainer.train(model_parameters["epochs"], model_parameters["learning_rate"], task)