Files
EOS/tests/single_test_prediction.py
Bobby Noelte 6498c7dc32 Add database support for measurements and historic prediction data. (#848)
The database supports backend selection, compression, incremental data load,
automatic data saving to storage, automatic vaccum and compaction.

Make SQLite3 and LMDB database backends available.

Update tests for new interface conventions regarding data sequences,
data containers, data providers. This includes the measurements provider and
the prediction providers.

Add database documentation.

The fix includes several bug fixes that are not directly related to the database
implementation but are necessary to keep EOS running properly and to test and
document the changes.

* fix: config eos test setup

  Make the config_eos fixture generate a new instance of the config_eos singleton.
  Use correct env names to setup data folder path.

* fix: startup with no config

  Make cache and measurements complain about missing data path configuration but
  do not bail out.

* fix: soc data preparation and usage for genetic optimization.

  Search for soc measurments 48 hours around the optimization start time.
  Only clamp soc to maximum in battery device simulation.

* fix: dashboard bailout on zero value solution display

  Do not use zero values to calculate the chart values adjustment for display.

* fix: openapi generation script

  Make the script also replace data_folder_path and data_output_path to hide
  real (test) environment pathes.

* feat: add make repeated task function

  make_repeated_task allows to wrap a function to be repeated cyclically.

* chore: removed index based data sequence access

  Index based data sequence access does not make sense as the sequence can be backed
  by the database. The sequence is now purely time series data.

* chore: refactor eos startup to avoid module import startup

  Avoid module import initialisation expecially of the EOS configuration.
  Config mutation, singleton initialization, logging setup, argparse parsing,
  background task definitions depending on config and environment-dependent behavior
  is now done at function startup.

* chore: introduce retention manager

  A single long-running background task that owns the scheduling of all periodic
  server-maintenance jobs (cache cleanup, DB autosave, …)

* chore: canonicalize timezone name for UTC

  Timezone names that are semantically identical to UTC are canonicalized to UTC.

* chore: extend config file migration for default value handling

  Extend the config file migration handling values None or nonexisting values
  that will invoke a default value generation in the new config file. Also
  adapt test to handle this situation.

* chore: extend datetime util test cases

* chore: make version test check for untracked files

  Check for files that are not tracked by git. Version calculation will be
  wrong if these files will not be commited.

* chore: bump pandas to 3.0.0

  Pandas 3.0 now performs inference on the appropriate resolution (a.k.a. unit)
  for the output dtype which may become datetime64[us] (before it was ns). Also
  numeric dtype detection is now more strict which needs a different detection for
  numerics.

* chore: bump pydantic-settings to 2.12.0

  pydantic-settings 2.12.0 under pytest creates a different behaviour. The tests
  were adapted and a workaround was introduced. Also ConfigEOS was adapted
  to allow for fine grain initialization control to be able to switch
  off certain settings such as file settings during test.

* chore: remove sci learn kit from dependencies

  The sci learn kit is not strictly necessary as long as we have scipy.

* chore: add documentation mode guarding for sphinx autosummary

  Sphinx autosummary excecutes functions. Prevent exceptions in case of pure doc
  mode.

* chore: adapt docker-build CI workflow to stricter GitHub handling

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2026-02-22 14:12:42 +01:00

229 lines
6.8 KiB
Python

#!/usr/bin/env python3
import argparse
import cProfile
import pstats
import sys
import time
from akkudoktoreos.core.coreabc import get_config, get_prediction
config_eos = get_config()
prediction_eos = get_prediction()
def config_pvforecast() -> dict:
"""Configure settings for PV forecast."""
settings = {
"general": {
"latitude": 52.52,
"longitude": 13.405,
},
"prediction": {
"hours": 48,
"historic_hours": 24,
},
"pvforecast": {
"provider": "PVForecastAkkudoktor",
"planes": [
{
"peakpower": 5.0,
"surface_azimuth": -10,
"surface_tilt": 7,
"userhorizon": [20, 27, 22, 20],
"inverter_paco": 10000,
},
{
"peakpower": 4.8,
"surface_azimuth": -90,
"surface_tilt": 7,
"userhorizon": [30, 30, 30, 50],
"inverter_paco": 10000,
},
{
"peakpower": 1.4,
"surface_azimuth": -40,
"surface_tilt": 60,
"userhorizon": [60, 30, 0, 30],
"inverter_paco": 2000,
},
{
"peakpower": 1.6,
"surface_azimuth": 5,
"surface_tilt": 45,
"userhorizon": [45, 25, 30, 60],
"inverter_paco": 1400,
},
],
},
}
return settings
def config_weather() -> dict:
"""Configure settings for weather forecast."""
settings = {
"general": {
"latitude": 52.52,
"longitude": 13.405,
},
"prediction": {
"hours": 48,
"historic_hours": 24,
},
"weather": dict(),
}
return settings
def config_elecprice() -> dict:
"""Configure settings for electricity price forecast."""
settings = {
"general": {
"latitude": 52.52,
"longitude": 13.405,
},
"prediction": {
"hours": 48,
"historic_hours": 24,
},
"elecprice": dict(),
}
return settings
def config_feedintarifffixed() -> dict:
"""Configure settings for feed in tariff forecast."""
settings = {
"general": {
"latitude": 52.52,
"longitude": 13.405,
},
"prediction": {
"hours": 48,
"historic_hours": 24,
},
"feedintariff": dict(),
}
return settings
def config_load() -> dict:
"""Configure settings for load forecast."""
settings = {
"general": {
"latitude": 52.52,
"longitude": 13.405,
},
"prediction": {
"hours": 48,
"historic_hours": 24,
},
}
return settings
def run_prediction(provider_id: str, verbose: bool = False) -> str:
"""Run the prediction.
Args:
provider_id (str): ID of prediction provider.
verbose (bool, optional): Whether to print verbose output. Defaults to False.
Returns:
dict: Prediction result as a dictionary
"""
# Initialize the oprediction
config_eos = get_config()
prediction_eos = get_prediction()
if provider_id in ("PVForecastAkkudoktor",):
settings = config_pvforecast()
forecast = "pvforecast"
elif provider_id in ("BrightSky", "ClearOutside"):
settings = config_weather()
forecast = "weather"
elif provider_id in ("ElecPriceAkkudoktor",):
settings = config_elecprice()
forecast = "elecprice"
elif provider_id in ("FeedInTariffFixed",):
settings = config_feedintarifffixed()
forecast = "feedintariff"
elif provider_id in ("LoadAkkudoktor",):
settings = config_load()
forecast = "loadforecast"
settings["load"]["LoadAkkudoktor"]["loadakkudoktor_year_energy_wh"] = 1000
else:
raise ValueError(f"Unknown provider '{provider_id}'.")
settings[forecast]["provider"] = provider_id
config_eos.merge_settings_from_dict(settings)
provider = prediction_eos.provider_by_id(provider_id)
prediction_eos.update_data()
# Return result of prediction
if verbose:
print(f"\nProvider ID: {provider.provider_id()}")
print("----------")
print("\nSettings\n----------")
print(settings)
print("\nProvider\n----------")
print(f"elecprice.provider: {config_eos.elecprice.provider}")
print(f"feedintariff.provider: {config_eos.feedintariff.provider}")
print(f"load.provider: {config_eos.load.provider}")
print(f"pvforecast.provider: {config_eos.pvforecast.provider}")
print(f"weather.provider: {config_eos.weather.provider}")
print(f"enabled: {provider.enabled()}")
for key in provider.record_keys:
print(f"\n{key}\n----------")
print(f"Array: {provider.key_to_array(key)}")
return provider.model_dump_json(indent=4)
def main():
"""Main function to run the optimization script with optional profiling."""
parser = argparse.ArgumentParser(description="Run Prediction")
parser.add_argument("--profile", action="store_true", help="Enable performance profiling")
parser.add_argument(
"--verbose", action="store_true", help="Enable verbose output during prediction"
)
parser.add_argument("--provider-id", type=str, default=0, help="Provider ID of prediction")
args = parser.parse_args()
if args.profile:
# Run with profiling
profiler = cProfile.Profile()
try:
result = profiler.runcall(
run_prediction, provider_id=args.provider_id, verbose=args.verbose
)
# Print profiling statistics
stats = pstats.Stats(profiler)
stats.strip_dirs().sort_stats("cumulative").print_stats(200)
# Print result
print("\nPrediction Result:")
print(result)
except Exception as e:
print(f"Error during prediction: {e}", file=sys.stderr)
sys.exit(1)
else:
# Run without profiling
try:
start_time = time.time()
result = run_prediction(provider_id=args.provider_id, verbose=args.verbose)
end_time = time.time()
elapsed_time = end_time - start_time
print(f"\nElapsed time: {elapsed_time:.4f} seconds.")
print("\nPrediction Result:")
print(result)
except Exception as e:
print(f"Error during prediction: {e}", file=sys.stderr)
sys.exit(1)
if __name__ == "__main__":
main()