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fix: load data for automatic optimization (#731)

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Automatic optimization used to take the adjusted load data even if there were no
measurements leading to 0 load values.

Split LoadAkkudoktor into LoadAkkudoktor and LoadAkkudoktorAdjusted. This allows
to select load data either purely from the load data database or load data additionally
adjusted by load measurements. Some value names have been adapted to denote
also the unit of a value.

For better load bug squashing the optimization solution data availability was
improved. For better data visbility prediction data can now be distinguished from
solution data in the generic optimization solution.

Some predictions that may be of interest to understand the solution were added.

Documentation was updated to resemble the addition load prediction provider and
the value name changes.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
2025-11-01 00:49:11 +01:00
committed by GitHub
parent e3c5b758dd
commit b01bb1c61c
26 changed files with 515 additions and 227 deletions
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10
src/akkudoktoreos/prediction/loadvrm.py
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@@ -78,7 +78,7 @@ class LoadVrm(LoadProvider):
return to_datetime(timestamp / 1000, in_timezone=self.config.general.timezone)
def _update_data(self, force_update: Optional[bool] = False) -> None:
"""Fetch and store VRM load forecast as load_mean and related values."""
"""Fetch and store VRM load forecast as loadforecast_power_w and related values."""
start_date = self.ems_start_datetime.start_of("day")
end_date = self.ems_start_datetime.add(hours=self.config.prediction.hours)
start_ts = int(start_date.timestamp())
@@ -87,19 +87,19 @@ class LoadVrm(LoadProvider):
logger.info(f"Updating Load forecast from VRM: {start_date} to {end_date}")
vrm_forecast_data = self._request_forecast(start_ts, end_ts)
load_mean_data = []
loadforecast_power_w_data = []
for timestamp, value in vrm_forecast_data.records.vrm_consumption_fc:
date = self._ts_to_datetime(timestamp)
rounded_value = round(value, 2)
self.update_value(
date,
{"load_mean": rounded_value, "load_std": 0.0, "load_mean_adjusted": rounded_value},
{"loadforecast_power_w": rounded_value},
)
load_mean_data.append((date, rounded_value))
loadforecast_power_w_data.append((date, rounded_value))
logger.debug(f"Updated load_mean with {len(load_mean_data)} entries.")
logger.debug(f"Updated loadforecast_power_w with {len(loadforecast_power_w_data)} entries.")
self.update_datetime = to_datetime(in_timezone=self.config.general.timezone)