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https://github.com/Akkudoktor-EOS/EOS.git
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aa334d0b61
* Update utilities in utils submodule. * Add base configuration modules. * Add server base configuration modules. * Add devices base configuration modules. * Add optimization base configuration modules. * Add utils base configuration modules. * Add prediction abstract and base classes plus tests. * Add PV forecast to prediction submodule. The PV forecast modules are adapted from the class_pvforecast module and replace it. * Add weather forecast to prediction submodule. The modules provide classes and methods to retrieve, manage, and process weather forecast data from various sources. Includes are structured representations of weather data and utilities for fetching forecasts for specific locations and time ranges. BrightSky and ClearOutside are currently supported. * Add electricity price forecast to prediction submodule. * Adapt fastapi server to base config and add fasthtml server. * Add ems to core submodule. * Adapt genetic to config. * Adapt visualize to config. * Adapt common test fixtures to config. * Add load forecast to prediction submodule. * Add core abstract and base classes. * Adapt single test optimization to config. * Adapt devices to config. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
70 lines
2.8 KiB
Python
70 lines
2.8 KiB
Python
"""Retrieves load forecast data from Akkudoktor load profiles."""
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from pathlib import Path
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from typing import Optional
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import numpy as np
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from pydantic import Field
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from akkudoktoreos.config.configabc import SettingsBaseModel
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from akkudoktoreos.prediction.loadabc import LoadProvider
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from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration
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from akkudoktoreos.utils.logutil import get_logger
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logger = get_logger(__name__)
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class LoadAkkudoktorCommonSettings(SettingsBaseModel):
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"""Common settings for load data import from file."""
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loadakkudoktor_year_energy: Optional[float] = Field(
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default=None, description="Yearly energy consumption (kWh)."
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)
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class LoadAkkudoktor(LoadProvider):
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"""Fetch Load forecast data from Akkudoktor load profiles."""
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@classmethod
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def provider_id(cls) -> str:
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"""Return the unique identifier for the LoadAkkudoktor provider."""
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return "LoadAkkudoktor"
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def load_data(self) -> np.ndarray:
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"""Loads data from the Akkudoktor load file."""
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load_file = Path(__file__).parent.parent.joinpath("data/load_profiles.npz")
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data_year_energy = None
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try:
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file_data = np.load(load_file)
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profile_data = np.array(
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list(zip(file_data["yearly_profiles"], file_data["yearly_profiles_std"]))
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)
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data_year_energy = profile_data * self.config.loadakkudoktor_year_energy
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# pprint(self.data_year_energy)
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except FileNotFoundError:
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error_msg = f"Error: File {load_file} not found."
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logger.error(error_msg)
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raise FileNotFoundError(error_msg)
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except Exception as e:
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error_msg = f"An error occurred while loading data: {e}"
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logger.error(error_msg)
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raise ValueError(error_msg)
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return data_year_energy
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def _update_data(self, force_update: Optional[bool] = False) -> None:
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"""Adds the load means and standard deviations."""
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data_year_energy = self.load_data()
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for load in self.loads():
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attr_load_mean = f"{load}_mean"
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attr_load_std = f"{load}_std"
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date = self.start_datetime
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for i in range(self.config.prediction_hours):
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# Extract mean and standard deviation for the given day and hour
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# Day indexing starts at 0, -1 because of that
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hourly_stats = data_year_energy[date.day_of_year - 1, :, date.hour]
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self.update_value(date, attr_load_mean, hourly_stats[0])
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self.update_value(date, attr_load_std, hourly_stats[1])
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date += to_duration("1 hour")
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# We are working on fresh data (no cache), report update time
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self.update_datetime = to_datetime(in_timezone=self.config.timezone)
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