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Added notebook to sample from predictions and load model

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Victor Mylle
2023-12-18 21:12:14 +00:00
parent c437c23566
commit fee948cc09
11 changed files with 1226 additions and 236 deletions
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36
Result-Reports/December.md
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@@ -11,7 +11,9 @@ Eerst literatuur bekijken ofdat probleem al voorkomt
- Training data -> 2 tresholds (1 voor charging, 1 voor discharging) (Volledig chargen / dischargen) - Training data -> 2 tresholds (1 voor charging, 1 voor discharging) (Volledig chargen / dischargen)
- 2 Mwh -> 8 kwartieren - 2 Mwh -> 8 kwartieren
- 1000 generaties voor 1 dag -> 1000 prijzen reconstrueren -> tresholds bepalen (logische tresholds) -> 2 tresholds voor 1 dag en toepassen op volledige test set 2 MWh -> 0.25 MW per kwartier -> charge cycle: 4 MWh -> 16 kwartieren iets doen -> dan charge cycle (max 400 per jaar voor later)
- 1000 generaties voor 1 dag uit test set -> 1000 prijzen reconstrueren -> tresholds bepalen (logische tresholds) -> 2 tresholds voor 1 dag en toepassen op de echte prijzen van de dag
- [x] Learning rate non autoregressive - [x] Learning rate non autoregressive
@@ -28,6 +30,38 @@ Eerst literatuur bekijken ofdat probleem al voorkomt
- [ ] Diffusion vs Variable Selection Network - [ ] Diffusion vs Variable Selection Network
- [x] Bid ladder van gisteren
- [ ] Plot van reconstucted prices with real bid ladder + bid ladder from yersterday against real reconstructed prices
Presentatie:
- Korte motivatie -> op dit moment: veel batterijen op simpele manier zoals onze baseline
- Policy per dag afhankelijk van generation model (policy word day ahead vastgelegd)
~~- NRV -> resultaten van verschillende resultaten (verder werken met autoregresive om te kunnen samplen)
-> ook overconfidence tonen van de quantiles (met die histogram)~~
~~- Quantile regreession + figuur van CDF
Resultaten van de autoregression modellen~~
- Plot van CRPS (kijk teams)
~~- Prijs reconstructie + momenteel bid ladders geplubliceerd door Elia momenteel. (paar plotjes)~~
~~- Baseline Policy voor batterij~~
- (Resultaten van nieuwe methode met samplen)
~~- Volgende stappen:~~
~~3 mogelijkheden:~~
~~- bid ladders (niet echt doen)~~
~~- Generaties beter van NRV -> quantile regression + overconfidence fixen (+ betere modellen)
Diffusion model is er een van en niet probleem van autoregressive~~
~~- Betere RL modellen (policies)~~
(mag 20 minuten zijn)
# Different Model Architectures (For Quantile Regression) # Different Model Architectures (For Quantile Regression)
## Baseline ## Baseline
The baseline just calculates the values for the given quantiles using the training data. The baseline just calculates the values for the given quantiles using the training data.

117
Result-Reports/Policies.md
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@@ -10,72 +10,57 @@ Determining 2 thresholds for battery charging and discharging. Charging when the
Training data: 01-01-2020 - 31-12-2022 Training data: 01-01-2020 - 31-12-2022
Test data: 01-01-2023 - 12-12-2023 Test data: 01-01-2023 - 12-12-2023
| Charging threshold (€/MWh) | Discharge threshold (€/MWh) | Profit (€) | Charge Cycles | | | Charge treshold | Discharge treshold | Charging Cost | Discharging Profit | Total Profit | Charge cycles | Mean charging price | Mean discharging price |
------------------:|---------------------:|---------------:|----------------:| |---:|------------------:|---------------------:|----------------:|---------------------:|---------------:|----------------:|----------------------:|-------------------------:|
| 150 | 100 | 366406 | 2658 | | 0 | 150 | 100 | 184072 | 1.03303e+06 | 848962 | 1884.62 | 32.4889 | 332.002 |
| 150 | 50 | 366406 | 2658 | | 0 | 150 | 0 | 184072 | 1.03303e+06 | 848962 | 1884.62 | 32.4889 | 332.002 |
| 150 | 0 | 366406 | 2658 | | 0 | 150 | -100 | 184072 | 1.03303e+06 | 848962 | 1884.62 | 32.4889 | 332.002 |
| 150 | 150 | 365999 | 2639 | | 0 | 150 | -50 | 184072 | 1.03303e+06 | 848962 | 1884.62 | 32.4889 | 332.002 |
| 100 | 150 | 364905 | 2306 | | 0 | 150 | 50 | 184072 | 1.03303e+06 | 848962 | 1884.62 | 32.4889 | 332.002 |
| 100 | 100 | 364483 | 2886 | | 0 | 150 | 150 | 182152 | 1.02598e+06 | 843824 | 1860.12 | 32.4889 | 333.447 |
| 100 | 0 | 364376 | 2896 | | 0 | 200 | 150 | 248326 | 1.0773e+06 | 828976 | 1695.5 | 42.9038 | 367.058 |
| 100 | 50 | 364376 | 2896 | | 0 | 200 | 100 | 248326 | 1.0773e+06 | 828976 | 1695.5 | 42.9038 | 367.058 |
| 200 | 150 | 352953 | 2416 | | 0 | 200 | 50 | 248326 | 1.0773e+06 | 828976 | 1695.5 | 42.9038 | 367.058 |
| 200 | 100 | 352953 | 2416 | | 0 | 200 | 0 | 248326 | 1.0773e+06 | 828976 | 1695.5 | 42.9038 | 367.058 |
| 200 | 50 | 352953 | 2416 | | 0 | 200 | -50 | 248326 | 1.0773e+06 | 828976 | 1695.5 | 42.9038 | 367.058 |
| 200 | 0 | 352953 | 2416 | | 0 | 200 | -100 | 248326 | 1.0773e+06 | 828976 | 1695.5 | 42.9038 | 367.058 |
| 200 | 200 | 352811 | 2413 | | 0 | 200 | 200 | 247560 | 1.07529e+06 | 827727 | 1690 | 42.9038 | 367.44 |
| 150 | 200 | 350975 | 2243 | | 0 | 150 | 200 | 151145 | 937599 | 786454 | 1514.88 | 32.4889 | 367.44 |
| 100 | 200 | 345855 | 1929 | | 0 | 100 | -100 | 85591.8 | 865530 | 779938 | 1978.12 | 19.9739 | 283.851 |
| 50 | 50 | 341569 | 3864 | | 0 | 100 | 50 | 85591.8 | 865530 | 779938 | 1978.12 | 19.9739 | 283.851 |
| 50 | 100 | 341421 | 2357 | | 0 | 100 | 0 | 85591.8 | 865530 | 779938 | 1978.12 | 19.9739 | 283.851 |
| 50 | 0 | 341202 | 3865 | | 0 | 100 | -50 | 85591.8 | 865530 | 779938 | 1978.12 | 19.9739 | 283.851 |
| 200 | 250 | 337567 | 1996 | | 0 | 100 | 100 | 84872.7 | 864043 | 779171 | 1966.62 | 19.9739 | 284.61 |
| 50 | 150 | 330189 | 1815 | | 0 | 100 | 150 | 63807.1 | 802437 | 738630 | 1502.5 | 19.9739 | 333.447 |
| 150 | 250 | 329684 | 1835 | | 0 | 50 | 50 | -35789.2 | 689455 | 725244 | 2831 | -9.65396 | 204.943 |
| 200 | 300 | 329343 | 1610 | | 0 | 50 | -100 | -35776.3 | 688933 | 724710 | 2833.88 | -9.65396 | 204.79 |
| 250 | 250 | 323456 | 2089 | | 0 | 50 | 0 | -35776.3 | 688933 | 724710 | 2833.88 | -9.65396 | 204.79 |
| 250 | 0 | 322692 | 2103 | | 0 | 50 | -50 | -35776.3 | 688933 | 724710 | 2833.88 | -9.65396 | 204.79 |
| 250 | 150 | 322692 | 2103 | | 0 | 100 | 200 | 49469.3 | 721380 | 671911 | 1196.12 | 19.9739 | 367.44 |
| 250 | 50 | 322692 | 2103 | | 0 | 50 | 100 | -46110.5 | 592694 | 638805 | 1417.5 | -9.65396 | 284.61 |
| 250 | 100 | 322692 | 2103 | | 0 | 50 | 150 | -45858.6 | 531472 | 577330 | 1007.5 | -9.65396 | 333.447 |
| 250 | 200 | 322692 | 2103 | | 0 | 50 | 200 | -43738 | 473478 | 517216 | 779.25 | -9.65396 | 367.44 |
| 250 | 300 | 321378 | 1685 | | 0 | 0 | 100 | -97804.3 | 341072 | 438876 | 761.5 | -73.2011 | 284.61 |
| 100 | 250 | 319651 | 1554 | | 0 | 0 | 50 | -121468 | 314671 | 436139 | 993.625 | -73.2011 | 204.943 |
| 150 | 300 | 319089 | 1462 | | 0 | 0 | 150 | -85919 | 341046 | 426964 | 635.75 | -73.2011 | 333.447 |
| 300 | 250 | 314776 | 1752 | | 0 | 0 | 200 | -75822.2 | 328188 | 404010 | 541.25 | -73.2011 | 367.44 |
| 300 | 50 | 314776 | 1752 | | 0 | 0 | 0 | -135954 | 259915 | 395869 | 1110.12 | -73.2011 | 146.436 |
| 300 | 100 | 314776 | 1752 | | 0 | 0 | -100 | -136936 | 251758 | 388694 | 1117.5 | -73.2011 | 145.226 |
| 300 | 150 | 314776 | 1752 | | 0 | 0 | -50 | -136936 | 251758 | 388694 | 1117.5 | -73.2011 | 145.226 |
| 300 | 200 | 314776 | 1752 | | 0 | -50 | 150 | -97889.1 | 219525 | 317414 | 409.312 | -127.845 | 333.447 |
| 300 | 0 | 314776 | 1752 | | 0 | -50 | 100 | -108274 | 208273 | 316548 | 458.312 | -127.845 | 284.61 |
| 50 | 200 | 313685 | 1476 | | 0 | -50 | 200 | -87698.7 | 223235 | 310933 | 363.625 | -127.845 | 367.44 |
| 300 | 300 | 313469 | 1749 | | 0 | -50 | 50 | -124664 | 184027 | 308691 | 525.688 | -127.845 | 204.943 |
| 100 | 300 | 309773 | 1236 | | 0 | -50 | 0 | -131679 | 158118 | 289797 | 551.188 | -127.845 | 146.436 |
| 200 | 350 | 306832 | 1341 | | 0 | -50 | -50 | -138246 | 126218 | 264464 | 576.5 | -127.845 | 136.356 |
| 250 | 350 | 302349 | 1411 | | 0 | -50 | -100 | -138429 | 125059 | 263488 | 577.625 | -127.845 | 136.262 |
| 150 | 350 | 295917 | 1202 | | 0 | -100 | 200 | -76499.7 | 137174 | 213674 | 219.625 | -179.829 | 367.44 |
| 300 | 350 | 290570 | 1466 | | 0 | -100 | 150 | -82202.7 | 128215 | 210418 | 237.125 | -179.829 | 333.447 |
| 50 | 250 | 290097 | 1195 | | 0 | -100 | 100 | -86319.5 | 117325 | 203645 | 250.25 | -179.829 | 284.61 |
| 350 | 300 | 284856 | 1507 | | 0 | -100 | 50 | -94055.9 | 99443.5 | 193499 | 269.875 | -179.829 | 204.943 |
| 350 | 250 | 284856 | 1507 | | 0 | -100 | 0 | -95713.7 | 87279.5 | 182993 | 274.625 | -179.829 | 146.436 |
| 350 | 150 | 284856 | 1507 | | 0 | -100 | -50 | -97722.9 | 71560.6 | 169283 | 280.375 | -179.829 | 136.356 |
| 350 | 100 | 284856 | 1507 | | 0 | -100 | -100 | -101133 | 45851.2 | 146985 | 289.375 | -179.829 | 130.542 |
| 350 | 50 | 284856 | 1507 |
| 350 | 0 | 284856 | 1507 |
| 350 | 200 | 284856 | 1507 |
| 100 | 350 | 284323 | 996 |
| 50 | 300 | 280922 | 954 |
| 350 | 350 | 279834 | 1496 |
| 0 | 150 | 277863 | 1243 |
| 0 | 100 | 276435 | 1477 |
| 0 | 200 | 270056 | 1080 |
| 0 | 50 | 269265 | 1914 |
| 50 | 350 | 259569 | 787 |
| 0 | 250 | 258282 | 908 |
| 0 | 300 | 250679 | 741 |
| 0 | 0 | 248357 | 2065 |
| 0 | 350 | 233775 | 624 |
## Optimal policy ## Optimal policy
Charge threshold: 150 €/MWh \ Charge threshold: 150 €/MWh \

7
src/data/dataset.py
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@@ -1,3 +1,4 @@
import datetime
import torch import torch
from torch.utils.data import Dataset, DataLoader from torch.utils.data import Dataset, DataLoader
import pandas as pd import pandas as pd
@@ -29,7 +30,6 @@ class NrvDataset(Dataset):
range(len(dataframe) - self.sequence_length - self.predict_sequence_length) range(len(dataframe) - self.sequence_length - self.predict_sequence_length)
) )
self.valid_indices = sorted(list(total_indices - set(self.samples_to_skip))) self.valid_indices = sorted(list(total_indices - set(self.samples_to_skip)))
print(len(self.valid_indices))
self.history_features = [] self.history_features = []
if self.data_config.LOAD_HISTORY: if self.data_config.LOAD_HISTORY:
@@ -69,7 +69,9 @@ class NrvDataset(Dataset):
self.time_feature = None self.time_feature = None
self.nrv = torch.tensor(dataframe["nrv"].values).float().reshape(-1) self.nrv = torch.tensor(dataframe["nrv"].values).float().reshape(-1)
self.datetime = dataframe["datetime"]
print(dataframe.columns)
self.history_features, self.forecast_features = self.preprocess_data(dataframe) self.history_features, self.forecast_features = self.preprocess_data(dataframe)
def skip_samples(self, dataframe): def skip_samples(self, dataframe):
@@ -203,3 +205,6 @@ class NrvDataset(Dataset):
return None, torch.stack(targets) return None, torch.stack(targets)
return torch.stack(features), torch.stack(targets) return torch.stack(features), torch.stack(targets)
def get_idx_for_date(self, date: datetime.date):
return self.datetime[self.datetime.dt.date == date].index[0]

113
src/notebooks/loss_test.ipynb
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50
src/notebooks/training.ipynb
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@@ -187,22 +187,56 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"ClearML Task: created new task id=6b50442e1cec4bf9b3bd5a34077b4217\n", "ClearML Task: created new task id=d19c767120a24f97b3231f0e8ac9f2b5\n",
"ClearML results page: http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/6b50442e1cec4bf9b3bd5a34077b4217/output/log\n", "ClearML results page: http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/d19c767120a24f97b3231f0e8ac9f2b5/output/log\n",
"151780\n", "151780\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).\n",
"\u0000Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f37044039a0>\n",
"Traceback (most recent call last):\n",
" File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1478, in __del__\n",
" self._shutdown_workers()\n",
" File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1442, in _shutdown_workers\n",
" w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)\n",
" File \"/opt/conda/lib/python3.10/multiprocessing/process.py\", line 149, in join\n",
" res = self._popen.wait(timeout)\n",
" File \"/opt/conda/lib/python3.10/multiprocessing/popen_fork.py\", line 40, in wait\n",
" if not wait([self.sentinel], timeout):\n",
" File \"/opt/conda/lib/python3.10/multiprocessing/connection.py\", line 931, in wait\n",
" ready = selector.select(timeout)\n",
" File \"/opt/conda/lib/python3.10/selectors.py\", line 416, in select\n",
" fd_event_list = self._selector.poll(timeout)\n",
" File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py\", line 66, in handler\n",
" _error_if_any_worker_fails()\n",
"RuntimeError: DataLoader worker (pid 326715) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"151780\n", "151780\n",
"24979\n", "24979\n",
"151780\n", "151780\n",
"24979\n", "24979\n",
"151780\n", "151780\n",
"24979\n", "24979\n",
"Using get_plot_error\n",
"Using get_plot_error\n",
"Using get_plot_error\n",
"Using get_plot_error\n",
"Early stopping triggered\n", "Early stopping triggered\n",
"151780\n", "151780\n",
"24979\n" "24979\n"
@@ -210,7 +244,7 @@
} }
], ],
"source": [ "source": [
"task = clearml_helper.get_task(task_name=\"Autoregressive Non Linear Quantile Regression + Quarter + DoW\")\n", "task = clearml_helper.get_task(task_name=\"Autoregressive Non Linear Quantile Regression + Quarter + DoW + Net\")\n",
"data_config = task.connect(data_config, name=\"data_features\")\n", "data_config = task.connect(data_config, name=\"data_features\")\n",
"\n", "\n",
"#### Hyperparameters ####\n", "#### Hyperparameters ####\n",
@@ -227,8 +261,8 @@
" quantiles = eval(quantiles)\n", " quantiles = eval(quantiles)\n",
"\n", "\n",
"model_parameters = {\n", "model_parameters = {\n",
" \"learning_rate\": 0.0001,\n", " \"learning_rate\": 0.0002,\n",
" \"hidden_size\": 512,\n", " \"hidden_size\": 1024,\n",
" \"num_layers\": 3,\n", " \"num_layers\": 3,\n",
" \"dropout\": 0.2,\n", " \"dropout\": 0.2,\n",
" \"time_feature_embedding\": 2,\n", " \"time_feature_embedding\": 2,\n",
@@ -256,7 +290,7 @@
" [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]\n", " [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]\n",
")\n", ")\n",
"trainer.early_stopping(patience=10)\n", "trainer.early_stopping(patience=10)\n",
"trainer.plot_every(15)\n", "trainer.plot_every(25)\n",
"trainer.train(task=task, epochs=epochs, remotely=False)" "trainer.train(task=task, epochs=epochs, remotely=False)"
] ]
}, },

454
src/policies/policy_tester.ipynb Normal file
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75
src/policies/simple_baseline.py
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@@ -17,32 +17,18 @@ class Battery:
self.power = power self.power = power
self.charge_cycles = 0 self.charge_cycles = 0
self.charging = False
self.discharging = False
def simulate(self):
"""
Simulate the battery for one time step (one quarter of an hour)
"""
if self.charging:
return self.charge()
elif self.discharging:
return self.discharge()
return 0
def discharge(self): def discharge(self):
""" """
Discharge the battery by one time step (one quarter of an hour) Discharge the battery by one time step (one quarter of an hour)
""" """
if self.current_charge == 0: if self.current_charge == 0:
return 0 return 0
self.discharging = True
self.current_charge -= self.power / 4 self.current_charge -= self.power / 4
self.charge_cycles += 1/16
if self.current_charge <= 0: if self.current_charge <= 0:
self.current_charge = 0 self.current_charge = 0
self.discharging = False
self.charge_cycles += 1
return self.power / 4 return self.power / 4
@@ -52,12 +38,12 @@ class Battery:
""" """
if self.current_charge == self.capacity: if self.current_charge == self.capacity:
return 0 return 0
self.charging = True
self.current_charge += self.power / 4 self.current_charge += self.power / 4
self.charge_cycles += 1/16
if self.current_charge >= self.capacity: if self.current_charge >= self.capacity:
self.current_charge = self.capacity self.current_charge = self.capacity
self.charging = False
return self.power / 4 return self.power / 4
@@ -89,7 +75,7 @@ class BaselinePolicy():
imbalance_prices = imbalance_prices.loc[imbalance_prices['DateTime'].dt.year >= 2020] imbalance_prices = imbalance_prices.loc[imbalance_prices['DateTime'].dt.year >= 2020]
else: else:
imbalance_prices = imbalance_prices.loc[imbalance_prices['DateTime'].dt.year == 2023] imbalance_prices = imbalance_prices.loc[imbalance_prices['DateTime'].dt.year == 2023]
imbalance_prices = imbalance_prices.sort_values(by=['DateTime']) imbalance_prices = imbalance_prices.sort_values(by=['DateTime'], ascending=True)
return imbalance_prices return imbalance_prices
def get_train_score(self, charge_treshold, discharge_treshold): def get_train_score(self, charge_treshold, discharge_treshold):
@@ -98,42 +84,53 @@ class BaselinePolicy():
def get_test_score(self, charge_treshold, discharge_treshold): def get_test_score(self, charge_treshold, discharge_treshold):
return self.get_score(self.test_data, charge_treshold, discharge_treshold) return self.get_score(self.test_data, charge_treshold, discharge_treshold)
# if price is below treshold -> charge battery: total_profit -= charge * price # if price is below charging treshold (cheap charging) -> charge battery: total_profit -= charge * price
# if price is above treshold -> discharge battery: total_profit += discharge * price # if price is above treshold -> discharge battery: total_profit += discharge * price
def get_score(self, df, charge_treshold, discharge_treshold): def get_score(self, df, charge_treshold, discharge_treshold):
self.battery.reset() self.battery.reset()
total_profit = 0 total_charging_cost = 0
total_discharging_profit = 0
mean_charging_price = 0
mean_discharging_price = 0
number_of_charges = 0
number_of_discharges = 0
for index, row in df.iterrows(): for index, row in df.iterrows():
if self.battery.charging:
total_profit -= self.battery.simulate() * row['Positive imbalance price']
elif self.battery.discharging:
total_profit += self.battery.simulate() * row['Positive imbalance price']
else:
if row['Positive imbalance price'] < charge_treshold: if row['Positive imbalance price'] < charge_treshold:
total_profit -= self.battery.charge() * row['Positive imbalance price'] total_charging_cost += self.battery.charge() * row['Positive imbalance price']
mean_charging_price += row['Positive imbalance price']
number_of_charges += 1
elif row['Positive imbalance price'] > discharge_treshold: elif row['Positive imbalance price'] > discharge_treshold:
total_profit += self.battery.discharge() * row['Positive imbalance price'] total_discharging_profit += self.battery.discharge() * row['Positive imbalance price']
mean_discharging_price += row['Positive imbalance price']
number_of_discharges += 1
return total_profit, self.battery.charge_cycles return total_charging_cost, total_discharging_profit, self.battery.charge_cycles, mean_charging_price / number_of_charges, mean_discharging_price / number_of_discharges
def treshold_scores(self, charge_tresholds, discharge_tresholds): def treshold_scores(self, charge_tresholds, discharge_tresholds):
df = pd.DataFrame(columns=["Charge treshold", "Discharge treshold", "Total Profit", "Charge cycles"]) df = pd.DataFrame(columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])
df_test = pd.DataFrame(columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])
for charge_treshold, discharge_treshold in tqdm(itertools.product(charge_tresholds, discharge_tresholds)): for charge_treshold, discharge_treshold in tqdm(itertools.product(charge_tresholds, discharge_tresholds)):
total_profit, charge_cycles = self.get_train_score(charge_treshold, discharge_treshold) total_charging_cost, total_discharge_profit, charge_cycles, mean_charging_price, mean_discharging_price = self.get_train_score(charge_treshold, discharge_treshold)
df = pd.concat([df, pd.DataFrame([[charge_treshold, discharge_treshold, total_profit, charge_cycles]], columns=["Charge treshold", "Discharge treshold", "Total Profit", "Charge cycles"])]) df = pd.concat([df, pd.DataFrame([[charge_treshold, discharge_treshold, total_charging_cost, total_discharge_profit, total_discharge_profit - total_charging_cost, charge_cycles, mean_charging_price, mean_discharging_price]], columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])])
total_charging_cost, total_discharge_profit, charge_cycles, mean_charging_price, mean_discharging_price = self.get_test_score(charge_treshold, discharge_treshold)
df_test = pd.concat([df_test, pd.DataFrame([[charge_treshold, discharge_treshold, total_charging_cost, total_discharge_profit, total_discharge_profit - total_charging_cost, charge_cycles, mean_charging_price, mean_discharging_price]], columns=["Charge treshold", "Discharge treshold", "Charging Cost", "Discharging Profit", "Total Profit", "Charge cycles", "Mean charging price", "Mean discharging price"])])
df = df.sort_values(by=['Total Profit'], ascending=False) df = df.sort_values(by=['Total Profit'], ascending=False)
return df
return df, df_test
battery = Battery(2, 1) battery = Battery(2, 1)
policy = BaselinePolicy(battery) policy = BaselinePolicy(battery)
# charge_tresholds = [0, 50, 100, 150, 200, 250, 300, 350] charge_tresholds = np.arange(-100, 250, 50)
# discharge_tresholds = [0, 50, 100, 150, 200, 250, 300, 350] discharge_tresholds = np.arange(-100, 250, 50)
# df = policy.treshold_scores(charge_tresholds, discharge_tresholds)
# print(df.to_markdown())
print(policy.get_test_score(150, 100)) df, df_test = policy.treshold_scores(charge_tresholds, discharge_tresholds)
print(df.to_markdown())
print(df_test.to_markdown())
# print(policy.get_test_score(150, 100))

8
src/training_scripts/autoregressive_quantiles.py
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@@ -1,16 +1,12 @@
from src.models.lstm_model import LSTMModel, GRUModel from src.models.lstm_model import GRUModel
from src.data import DataProcessor, DataConfig from src.data import DataProcessor, DataConfig
from src.trainers.quantile_trainer import AutoRegressiveQuantileTrainer, NonAutoRegressiveQuantileRegression from src.trainers.quantile_trainer import AutoRegressiveQuantileTrainer
from src.trainers.probabilistic_baseline import ProbabilisticBaselineTrainer
from src.trainers.autoregressive_trainer import AutoRegressiveTrainer
from src.trainers.trainer import Trainer from src.trainers.trainer import Trainer
from src.utils.clearml import ClearMLHelper from src.utils.clearml import ClearMLHelper
from src.models import * from src.models import *
from src.losses import * from src.losses import *
import torch import torch
import numpy as np
from torch.nn import MSELoss, L1Loss from torch.nn import MSELoss, L1Loss
from datetime import datetime
import torch.nn as nn import torch.nn as nn
from src.models.time_embedding_layer import TimeEmbedding from src.models.time_embedding_layer import TimeEmbedding

584
src/utils/bid_ladder.ipynb
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File diff suppressed because one or more lines are too long

8
src/utils/cdf_pdf.py
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@@ -1,8 +0,0 @@
import numpy as np
import matplotlib.pyplot as plt
# Given lists of quantiles and their corresponding probabilities
quantiles = [-0.23013, -0.19831, -0.15217, -0.13654, -0.05726,
0.011687, 0.015129, 0.043187, 0.047704]
probs = [0.025, 0.05, 0.1, 0.15, 0.5, 0.85, 0.9, 0.95, 0.975]

4
src/utils/imbalance_price_calculator.py
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@@ -83,8 +83,8 @@ class ImbalancePriceCalculator:
fig.add_trace(go.Scatter(x=x_dec_interpolated, y=y_dec_interpolated, mode='lines+markers', name="dec")) fig.add_trace(go.Scatter(x=x_dec_interpolated, y=y_dec_interpolated, mode='lines+markers', name="dec"))
fig.update_layout( fig.update_layout(
title='Bid ladder', title='Bid ladder',
xaxis_title='Volume', xaxis_title='Volume [MWh]',
yaxis_title='Price', yaxis_title='Price [EUR/MWh]',
hovermode='x unified' hovermode='x unified'
) )