fix: automatic optimization (#596)

This fix implements the long term goal to have the EOS server run optimization (or energy management) on regular intervals automatically. Thus clients can request the current energy management plan at any time and it is updated on regular intervals without interaction by the client. This fix started out to "only" make automatic optimization (or energy management) runs working. It turned out there are several endpoints that in some way update predictions or run the optimization. To lock against such concurrent attempts the code had to be refactored to allow control of execution. During refactoring it became clear that some classes and files are named without a proper reference to their usage. Thus not only refactoring but also renaming became necessary. The names are still not the best, but I hope they are more intuitive. The fix includes several bug fixes that are not directly related to the automatic optimization but are necessary to keep EOS running properly to do the automatic optimization and to test and document the changes. This is a breaking change as the configuration structure changed once again and the server API was also enhanced and streamlined. The server API that is used by Andreas and Jörg in their videos has not changed. * fix: automatic optimization Allow optimization to automatically run on configured intervals gathering all optimization parameters from configuration and predictions. The automatic run can be configured to only run prediction updates skipping the optimization. Extend documentaion to also cover automatic optimization. Lock automatic runs against runs initiated by the /optimize or other endpoints. Provide new endpoints to retrieve the energy management plan and the genetic solution of the latest automatic optimization run. Offload energy management to thread pool executor to keep the app more responsive during the CPU heavy optimization run. * fix: EOS servers recognize environment variables on startup Force initialisation of EOS configuration on server startup to assure all sources of EOS configuration are properly set up and read. Adapt server tests and configuration tests to also test for environment variable configuration. * fix: Remove 0.0.0.0 to localhost translation under Windows EOS imposed a 0.0.0.0 to localhost translation under Windows for convenience. This caused some trouble in user configurations. Now, as the default IP address configuration is 127.0.0.1, the user is responsible for to set up the correct Windows compliant IP address. * fix: allow names for hosts additional to IP addresses * fix: access pydantic model fields by class Access by instance is deprecated. * fix: down sampling key_to_array * fix: make cache clear endpoint clear all cache files Make /v1/admin/cache/clear clear all cache files. Before it only cleared expired cache files by default. Add new endpoint /v1/admin/clear-expired to only clear expired cache files. * fix: timezonefinder returns Europe/Paris instead of Europe/Berlin timezonefinder 8.10 got more inaccurate for timezones in europe as there is a common timezone. Use new package tzfpy instead which is still returning Europe/Berlin if you are in Germany. tzfpy also claims to be faster than timezonefinder. * fix: provider settings configuration Provider configuration used to be a union holding the settings for several providers. Pydantic union handling does not always find the correct type for a provider setting. This led to exceptions in specific configurations. Now provider settings are explicit comfiguration items for each possible provider. This is a breaking change as the configuration structure was changed. * fix: ClearOutside weather prediction irradiance calculation Pvlib needs a pandas time index. Convert time index. * fix: test config file priority Do not use config_eos fixture as this fixture already creates a config file. * fix: optimization sample request documentation Provide all data in documentation of optimization sample request. * fix: gitlint blocking pip dependency resolution Replace gitlint by commitizen. Gitlint is not actively maintained anymore. Gitlint dependencies blocked pip from dependency resolution. * fix: sync pre-commit config to actual dependency requirements .pre-commit-config.yaml was out of sync, also requirements-dev.txt. * fix: missing babel in requirements.txt Add babel to requirements.txt * feat: setup default device configuration for automatic optimization In case the parameters for automatic optimization are not fully defined a default configuration is setup to allow the automatic energy management run. The default configuration may help the user to correctly define the device configuration. * feat: allow configuration of genetic algorithm parameters The genetic algorithm parameters for number of individuals, number of generations, the seed and penalty function parameters are now avaliable as configuration options. * feat: allow configuration of home appliance time windows The time windows a home appliance is allowed to run are now configurable by the configuration (for /v1 API) and also by the home appliance parameters (for the classic /optimize API). If there is no such configuration the time window defaults to optimization hours, which was the standard before the change. Documentation on how to configure time windows is added. * feat: standardize mesaurement keys for battery/ ev SoC measurements The standardized measurement keys to report battery SoC to the device simulations can now be retrieved from the device configuration as a read-only config option. * feat: feed in tariff prediction Add feed in tarif predictions needed for automatic optimization. The feed in tariff can be retrieved as fixed feed in tarif or can be imported. Also add tests for the different feed in tariff providers. Extend documentation to cover the feed in tariff providers. * feat: add energy management plan based on S2 standard instructions EOS can generate an energy management plan as a list of simple instructions. May be retrieved by the /v1/energy-management/plan endpoint. The instructions loosely follow the S2 energy management standard. * feat: make measurement keys configurable by EOS configuration. The fixed measurement keys are replaced by configurable measurement keys. * feat: make pendulum DateTime, Date, Duration types usable for pydantic models Use pydantic_extra_types.pendulum_dt to get pydantic pendulum types. Types are added to the datetimeutil utility. Remove custom made pendulum adaptations from EOS pydantic module. Make EOS modules use the pydantic pendulum types managed by the datetimeutil module instead of the core pendulum types. * feat: Add Time, TimeWindow, TimeWindowSequence and to_time to datetimeutil. The time windows are are added to support home appliance time window configuration. All time classes are also pydantic models. Time is the base class for time definition derived from pendulum.Time. * feat: Extend DataRecord by configurable field like data. Configurable field like data was added to support the configuration of measurement records. * feat: Add additional information to health information Version information is added to the health endpoints of eos and eosDash. The start time of the last optimization and the latest run time of the energy management is added to the EOS health information. * feat: add pydantic merge model tests * feat: add plan tab to EOSdash The plan tab displays the current energy management instructions. * feat: add predictions tab to EOSdash The predictions tab displays the current predictions. * feat: add cache management to EOSdash admin tab The admin tab is extended by a section for cache management. It allows to clear the cache. * feat: add about tab to EOSdash The about tab resembles the former hello tab and provides extra information. * feat: Adapt changelog and prepare for release management Release management using commitizen is added. The changelog file is adapted and teh changelog and a description for release management is added in the documentation. * feat(doc): Improve install and devlopment documentation Provide a more concise installation description in Readme.md and add extra installation page and development page to documentation. * chore: Use memory cache for interpolation instead of dict in inverter Decorate calculate_self_consumption() with @cachemethod_until_update to cache results in memory during an energy management/ optimization run. Replacement of dict type caching in inverter is now possible because all optimization runs are properly locked and the memory cache CacheUntilUpdateStore is properly cleared at the start of any energy management/ optimization operation. * chore: refactor genetic Refactor the genetic algorithm modules for enhanced module structure and better readability. Removed unnecessary and overcomplex devices singleton. Also split devices configuration from genetic algorithm parameters to allow further development independently from genetic algorithm parameter format. Move charge rates configuration for electric vehicles from optimization to devices configuration to allow to have different charge rates for different cars in the future. * chore: Rename memory cache to CacheEnergyManagementStore The name better resembles the task of the cache to chache function and method results for an energy management run. Also the decorator functions are renamed accordingly: cachemethod_energy_management, cache_energy_management * chore: use class properties for config/ems/prediction mixin classes * chore: skip debug logs from mathplotlib Mathplotlib is very noisy in debug mode. * chore: automatically sync bokeh js to bokeh python package bokeh was updated to 3.8.0, make JS CDN automatically follow the package version. * chore: rename hello.py to about.py Make hello.py the adapted EOSdash about page. * chore: remove demo page from EOSdash As no the plan and prediction pages are working without configuration, the demo page is no longer necessary * chore: split test_server.py for system test Split test_server.py to create explicit test_system.py for system tests. * chore: move doc utils to generate_config_md.py The doc utils are only used in scripts/generate_config_md.py. Move it there to attribute for strong cohesion. * chore: improve pydantic merge model documentation * chore: remove pendulum warning from readme * chore: remove GitHub discussions from contributing documentation Github discussions is to be replaced by Akkudoktor.net. * chore(release): bump version to 0.1.0+dev for development * build(deps): bump fastapi[standard] from 0.115.14 to 0.117.1 bump fastapi and make coverage version (for pytest-cov) explicit to avoid pip break. * build(deps): bump uvicorn from 0.36.0 to 0.37.0 BREAKING CHANGE: EOS configuration changed. V1 API changed. - The available_charge_rates_percent configuration is removed from optimization. Use the new charge_rate configuration for the electric vehicle - Optimization configuration parameter hours renamed to horizon_hours - Device configuration now has to provide the number of devices and device properties per device. - Specific prediction provider configuration to be provided by explicit configuration item (no union for all providers). - Measurement keys to be provided as a list. - New feed in tariff providers have to be configured. - /v1/measurement/loadxxx endpoints are removed. Use generic mesaurement endpoints. - /v1/admin/cache/clear now clears all cache files. Use /v1/admin/cache/clear-expired to only clear all expired cache files. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-11-25 06:46:25 +00:00 · 2025-10-28 02:50:31 +01:00
parent 20a9eb78d8
commit b397b5d43e
146 changed files with 22024 additions and 5339 deletions
--- a/src/akkudoktoreos/prediction/elecprice.py
+++ b/src/akkudoktoreos/prediction/elecprice.py
@@ -17,6 +17,14 @@ elecprice_providers = [
 ]


+class ElecPriceCommonProviderSettings(SettingsBaseModel):
+    """Electricity Price Prediction Provider Configuration."""
+
+    ElecPriceImport: Optional[ElecPriceImportCommonSettings] = Field(
+        default=None, description="ElecPriceImport settings", examples=[None]
+    )
+
+
 class ElecPriceCommonSettings(SettingsBaseModel):
    """Electricity Price Prediction Configuration."""

@@ -26,7 +34,10 @@ class ElecPriceCommonSettings(SettingsBaseModel):
        examples=["ElecPriceAkkudoktor"],
    )
    charges_kwh: Optional[float] = Field(
-        default=None, ge=0, description="Electricity price charges (€/kWh).", examples=[0.21]
+        default=None,
+        ge=0,
+        description="Electricity price charges [€/kWh]. Will be added to variable market price.",
+        examples=[0.21],
    )
    vat_rate: Optional[float] = Field(
        default=1.19,
@@ -35,8 +46,15 @@ class ElecPriceCommonSettings(SettingsBaseModel):
        examples=[1.19],
    )

-    provider_settings: Optional[ElecPriceImportCommonSettings] = Field(
-        default=None, description="Provider settings", examples=[None]
+    provider_settings: ElecPriceCommonProviderSettings = Field(
+        default_factory=ElecPriceCommonProviderSettings,
+        description="Provider settings",
+        examples=[
+            # Example 1: Empty/default settings (all providers None)
+            {
+                "ElecPriceImport": None,
+            },
+        ],
    )

    # Validators
--- a/src/akkudoktoreos/prediction/elecpriceakkudoktor.py
+++ b/src/akkudoktoreos/prediction/elecpriceakkudoktor.py
@@ -102,10 +102,10 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
            - add the file cache again.
        """
        source = "https://api.akkudoktor.net"
-        if not self.start_datetime:
-            raise ValueError(f"Start DateTime not set: {self.start_datetime}")
+        if not self.ems_start_datetime:
+            raise ValueError(f"Start DateTime not set: {self.ems_start_datetime}")
        # Try to take data from 5 weeks back for prediction
-        date = to_datetime(self.start_datetime - to_duration("35 days"), as_string="YYYY-MM-DD")
+        date = to_datetime(self.ems_start_datetime - to_duration("35 days"), as_string="YYYY-MM-DD")
        last_date = to_datetime(self.end_datetime, as_string="YYYY-MM-DD")
        url = f"{source}/prices?start={date}&end={last_date}&tz={self.config.general.timezone}"
        response = requests.get(url, timeout=10)
@@ -147,8 +147,8 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
        """
        # Get Akkudoktor electricity price data
        akkudoktor_data = self._request_forecast(force_update=force_update)  # type: ignore
-        if not self.start_datetime:
-            raise ValueError(f"Start DateTime not set: {self.start_datetime}")
+        if not self.ems_start_datetime:
+            raise ValueError(f"Start DateTime not set: {self.ems_start_datetime}")

        # Assumption that all lists are the same length and are ordered chronologically
        # in ascending order and have the same timestamps.
@@ -186,13 +186,13 @@ class ElecPriceAkkudoktor(ElecPriceProvider):
        # some of our data is already in the future, so we need to predict less. If we got less data we increase the prediction hours
        needed_hours = int(
            self.config.prediction.hours
-            - ((highest_orig_datetime - self.start_datetime).total_seconds() // 3600)
+            - ((highest_orig_datetime - self.ems_start_datetime).total_seconds() // 3600)
        )

        if needed_hours <= 0:
            logger.warning(
-                f"No prediction needed. needed_hours={needed_hours}, hours={self.config.prediction.hours},highest_orig_datetime {highest_orig_datetime}, start_datetime {self.start_datetime}"
-            )  # this might keep data longer than self.start_datetime + self.config.prediction.hours in the records
+                f"No prediction needed. needed_hours={needed_hours}, hours={self.config.prediction.hours},highest_orig_datetime {highest_orig_datetime}, start_datetime {self.ems_start_datetime}"
+            )  # this might keep data longer than self.ems_start_datetime + self.config.prediction.hours in the records
            return

        if amount_datasets > 800:  # we do the full ets with seasons of 1 week
--- a/src/akkudoktoreos/prediction/elecpriceenergycharts.py
+++ b/src/akkudoktoreos/prediction/elecpriceenergycharts.py
@@ -91,7 +91,7 @@ class ElecPriceEnergyCharts(ElecPriceProvider):
        if start_date is None:
            # Try to take data from 5 weeks back for prediction
            start_date = to_datetime(
-                self.start_datetime - to_duration("35 days"), as_string="YYYY-MM-DD"
+                self.ems_start_datetime - to_duration("35 days"), as_string="YYYY-MM-DD"
            )

        last_date = to_datetime(self.end_datetime, as_string="YYYY-MM-DD")
@@ -172,17 +172,17 @@ class ElecPriceEnergyCharts(ElecPriceProvider):
        hours_ahead = 23 if now.time() < pd.Timestamp("14:00").time() else 47
        end = midnight + pd.Timedelta(hours=hours_ahead)

-        if not self.start_datetime:
-            raise ValueError(f"Start DateTime not set: {self.start_datetime}")
+        if not self.ems_start_datetime:
+            raise ValueError(f"Start DateTime not set: {self.ems_start_datetime}")

        # Determine if update is needed and how many days
        past_days = 35
        if self.highest_orig_datetime:
            history_series = self.key_to_series(
-                key="elecprice_marketprice_wh", start_datetime=self.start_datetime
+                key="elecprice_marketprice_wh", start_datetime=self.ems_start_datetime
            )
            # If history lower, then start_datetime
-            if history_series.index.min() <= self.start_datetime:
+            if history_series.index.min() <= self.ems_start_datetime:
                past_days = 0

            needs_update = end > self.highest_orig_datetime
@@ -195,7 +195,7 @@ class ElecPriceEnergyCharts(ElecPriceProvider):
            )
            # Set start_date try to take data from 5 weeks back for prediction
            start_date = to_datetime(
-                self.start_datetime - to_duration(f"{past_days} days"), as_string="YYYY-MM-DD"
+                self.ems_start_datetime - to_duration(f"{past_days} days"), as_string="YYYY-MM-DD"
            )
            # Get Energy-Charts electricity price data
            energy_charts_data = self._request_forecast(
@@ -227,13 +227,13 @@ class ElecPriceEnergyCharts(ElecPriceProvider):
        # some of our data is already in the future, so we need to predict less. If we got less data we increase the prediction hours
        needed_hours = int(
            self.config.prediction.hours
-            - ((self.highest_orig_datetime - self.start_datetime).total_seconds() // 3600)
+            - ((self.highest_orig_datetime - self.ems_start_datetime).total_seconds() // 3600)
        )

        if needed_hours <= 0:
            logger.warning(
-                f"No prediction needed. needed_hours={needed_hours}, hours={self.config.prediction.hours},highest_orig_datetime {self.highest_orig_datetime}, start_datetime {self.start_datetime}"
-            )  # this might keep data longer than self.start_datetime + self.config.prediction.hours in the records
+                f"No prediction needed. needed_hours={needed_hours}, hours={self.config.prediction.hours},highest_orig_datetime {self.highest_orig_datetime}, start_datetime {self.ems_start_datetime}"
+            )  # this might keep data longer than self.ems_start_datetime + self.config.prediction.hours in the records
            return

        if amount_datasets > 800:  # we do the full ets with seasons of 1 week
--- a/src/akkudoktoreos/prediction/elecpriceimport.py
+++ b/src/akkudoktoreos/prediction/elecpriceimport.py
@@ -61,15 +61,16 @@ class ElecPriceImport(ElecPriceProvider, PredictionImportProvider):
        return "ElecPriceImport"

    def _update_data(self, force_update: Optional[bool] = False) -> None:
-        if self.config.elecprice.provider_settings is None:
+        if self.config.elecprice.provider_settings.ElecPriceImport is None:
            logger.debug(f"{self.provider_id()} data update without provider settings.")
            return
-        if self.config.elecprice.provider_settings.import_file_path:
+        if self.config.elecprice.provider_settings.ElecPriceImport.import_file_path:
            self.import_from_file(
-                self.config.elecprice.provider_settings.import_file_path,
+                self.config.elecprice.provider_settings.ElecPriceImport.import_file_path,
                key_prefix="elecprice",
            )
-        if self.config.elecprice.provider_settings.import_json:
+        if self.config.elecprice.provider_settings.ElecPriceImport.import_json:
            self.import_from_json(
-                self.config.elecprice.provider_settings.import_json, key_prefix="elecprice"
+                self.config.elecprice.provider_settings.ElecPriceImport.import_json,
+                key_prefix="elecprice",
            )
--- a/src/akkudoktoreos/prediction/feedintariff.py
+++ b/src/akkudoktoreos/prediction/feedintariff.py
@@ -0,0 +1,61 @@
+from typing import Optional
+
+from pydantic import Field, field_validator
+
+from akkudoktoreos.config.configabc import SettingsBaseModel
+from akkudoktoreos.prediction.feedintariffabc import FeedInTariffProvider
+from akkudoktoreos.prediction.feedintarifffixed import FeedInTariffFixedCommonSettings
+from akkudoktoreos.prediction.feedintariffimport import FeedInTariffImportCommonSettings
+from akkudoktoreos.prediction.prediction import get_prediction
+
+prediction_eos = get_prediction()
+
+# Valid feedintariff providers
+feedintariff_providers = [
+    provider.provider_id()
+    for provider in prediction_eos.providers
+    if isinstance(provider, FeedInTariffProvider)
+]
+
+
+class FeedInTariffCommonProviderSettings(SettingsBaseModel):
+    """Feed In Tariff Prediction Provider Configuration."""
+
+    FeedInTariffFixed: Optional[FeedInTariffFixedCommonSettings] = Field(
+        default=None, description="FeedInTariffFixed settings", examples=[None]
+    )
+    FeedInTariffImport: Optional[FeedInTariffImportCommonSettings] = Field(
+        default=None, description="FeedInTariffImport settings", examples=[None]
+    )
+
+
+class FeedInTariffCommonSettings(SettingsBaseModel):
+    """Feed In Tariff Prediction Configuration."""
+
+    provider: Optional[str] = Field(
+        default=None,
+        description="Feed in tariff provider id of provider to be used.",
+        examples=["FeedInTariffFixed", "FeedInTarifImport"],
+    )
+
+    provider_settings: FeedInTariffCommonProviderSettings = Field(
+        default_factory=FeedInTariffCommonProviderSettings,
+        description="Provider settings",
+        examples=[
+            # Example 1: Empty/default settings (all providers None)
+            {
+                "FeedInTariffFixed": None,
+                "FeedInTariffImport": None,
+            },
+        ],
+    )
+
+    # Validators
+    @field_validator("provider", mode="after")
+    @classmethod
+    def validate_provider(cls, value: Optional[str]) -> Optional[str]:
+        if value is None or value in feedintariff_providers:
+            return value
+        raise ValueError(
+            f"Provider '{value}' is not a valid feed in tariff provider: {feedintariff_providers}."
+        )
--- a/src/akkudoktoreos/prediction/feedintariffabc.py
+++ b/src/akkudoktoreos/prediction/feedintariffabc.py
@@ -0,0 +1,58 @@
+"""Abstract and base classes for feed in tariff predictions.
+
+Notes:
+    - Ensure appropriate API keys or configurations are set up if required by external data sources.
+"""
+
+from abc import abstractmethod
+from typing import List, Optional
+
+from pydantic import Field, computed_field
+
+from akkudoktoreos.prediction.predictionabc import PredictionProvider, PredictionRecord
+
+
+class FeedInTariffDataRecord(PredictionRecord):
+    """Represents a feed in tariff data record containing various price attributes at a specific datetime.
+
+    Attributes:
+        date_time (Optional[AwareDatetime]): The datetime of the record.
+
+    """
+
+    feed_in_tariff_wh: Optional[float] = Field(None, description="Feed in tariff per Wh (€/Wh)")
+
+    # Computed fields
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def feed_in_tariff_kwh(self) -> Optional[float]:
+        """Feed in tariff per kWh (€/kWh).
+
+        Convenience attribute calculated from `feed_in_tariff_wh`.
+        """
+        if self.feed_in_tariff_wh is None:
+            return None
+        return self.feed_in_tariff_wh * 1000.0
+
+
+class FeedInTariffProvider(PredictionProvider):
+    """Abstract base class for feed in tariff providers.
+
+    FeedInTariffProvider is a thread-safe singleton, ensuring only one instance of this class is created.
+
+    Configuration variables:
+        feed in tariff_provider (str): Prediction provider for feed in tarif.
+    """
+
+    # overload
+    records: List[FeedInTariffDataRecord] = Field(
+        default_factory=list, description="List of FeedInTariffDataRecord records"
+    )
+
+    @classmethod
+    @abstractmethod
+    def provider_id(cls) -> str:
+        return "FeedInTariffProvider"
+
+    def enabled(self) -> bool:
+        return self.provider_id() == self.config.feedintariff.provider
--- a/src/akkudoktoreos/prediction/feedintarifffixed.py
+++ b/src/akkudoktoreos/prediction/feedintarifffixed.py
@@ -0,0 +1,48 @@
+"""Provides feed in tariff data."""
+
+from typing import Optional
+
+from loguru import logger
+from pydantic import Field
+
+from akkudoktoreos.config.configabc import SettingsBaseModel
+from akkudoktoreos.prediction.feedintariffabc import FeedInTariffProvider
+from akkudoktoreos.utils.datetimeutil import to_datetime
+
+
+class FeedInTariffFixedCommonSettings(SettingsBaseModel):
+    """Common settings for elecprice fixed price."""
+
+    feed_in_tariff_kwh: Optional[float] = Field(
+        default=None,
+        ge=0,
+        description="Electricity price feed in tariff [€/kWH].",
+        examples=[0.078],
+    )
+
+
+class FeedInTariffFixed(FeedInTariffProvider):
+    """Fixed price feed in tariff data.
+
+    FeedInTariffFixed is a singleton-based class that retrieves elecprice data.
+    """
+
+    @classmethod
+    def provider_id(cls) -> str:
+        """Return the unique identifier for the FeedInTariffFixed provider."""
+        return "FeedInTariffFixed"
+
+    def _update_data(self, force_update: Optional[bool] = False) -> None:
+        error_msg = "Feed in tariff not provided"
+        try:
+            feed_in_tariff = (
+                self.config.feedintariff.provider_settings.FeedInTariffFixed.feed_in_tariff_kwh
+            )
+        except:
+            logger.exception(error_msg)
+            raise ValueError(error_msg)
+        if feed_in_tariff is None:
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+        feed_in_tariff_wh = feed_in_tariff / 1000
+        self.update_value(to_datetime(), "feed_in_tariff_wh", feed_in_tariff_wh)
--- a/src/akkudoktoreos/prediction/feedintariffimport.py
+++ b/src/akkudoktoreos/prediction/feedintariffimport.py
@@ -0,0 +1,76 @@
+"""Retrieves feed in tariff forecast data from an import file.
+
+This module provides classes and mappings to manage feed in tariff data obtained from
+an import file. The data is mapped to the `FeedInTariffDataRecord` format, enabling consistent
+access to forecasted and historical feed in tariff attributes.
+"""
+
+from pathlib import Path
+from typing import Optional, Union
+
+from loguru import logger
+from pydantic import Field, field_validator
+
+from akkudoktoreos.config.configabc import SettingsBaseModel
+from akkudoktoreos.prediction.feedintariffabc import FeedInTariffProvider
+from akkudoktoreos.prediction.predictionabc import PredictionImportProvider
+
+
+class FeedInTariffImportCommonSettings(SettingsBaseModel):
+    """Common settings for feed in tariff data import from file or JSON string."""
+
+    import_file_path: Optional[Union[str, Path]] = Field(
+        default=None,
+        description="Path to the file to import feed in tariff data from.",
+        examples=[None, "/path/to/feedintariff.json"],
+    )
+    import_json: Optional[str] = Field(
+        default=None,
+        description="JSON string, dictionary of feed in tariff forecast value lists.",
+        examples=['{"fead_in_tariff_wh": [0.000078, 0.000078, 0.000023]}'],
+    )
+
+    # Validators
+    @field_validator("import_file_path", mode="after")
+    @classmethod
+    def validate_feedintariffimport_file_path(
+        cls, value: Optional[Union[str, Path]]
+    ) -> Optional[Path]:
+        if value is None:
+            return None
+        if isinstance(value, str):
+            value = Path(value)
+        """Ensure file is available."""
+        value.resolve()
+        if not value.is_file():
+            raise ValueError(f"Import file path '{value}' is not a file.")
+        return value
+
+
+class FeedInTariffImport(FeedInTariffProvider, PredictionImportProvider):
+    """Fetch Feed In Tariff data from import file or JSON string.
+
+    FeedInTariffImport is a singleton-based class that retrieves fedd in tariff forecast data
+    from a file or JSON string and maps it to `FeedInTariffDataRecord` fields. It manages the forecast
+    over a range of hours into the future and retains historical data.
+    """
+
+    @classmethod
+    def provider_id(cls) -> str:
+        """Return the unique identifier for the FeedInTariffImport provider."""
+        return "FeedInTariffImport"
+
+    def _update_data(self, force_update: Optional[bool] = False) -> None:
+        if self.config.feedintariff.provider_settings.FeedInTariffImport is None:
+            logger.debug(f"{self.provider_id()} data update without provider settings.")
+            return
+        if self.config.feedintariff.provider_settings.FeedInTariffImport.import_file_path:
+            self.import_from_file(
+                self.config.provider_settings.FeedInTariffImport.import_file_path,
+                key_prefix="feedintariff",
+            )
+        if self.config.feedintariff.provider_settings.FeedInTariffImport.import_json:
+            self.import_from_json(
+                self.config.feedintariff.provider_settings.FeedInTariffImport.import_json,
+                key_prefix="feedintariff",
+            )
--- a/src/akkudoktoreos/prediction/interpolator.py
+++ b/src/akkudoktoreos/prediction/interpolator.py
@@ -1,11 +1,11 @@
 #!/usr/bin/env python
 import pickle
-from functools import lru_cache
 from pathlib import Path

 import numpy as np
 from scipy.interpolate import RegularGridInterpolator

+from akkudoktoreos.core.cache import cachemethod_energy_management
 from akkudoktoreos.core.coreabc import SingletonMixin


@@ -16,8 +16,7 @@ class SelfConsumptionProbabilityInterpolator:
        with open(self.filepath, "rb") as file:
            self.interpolator: RegularGridInterpolator = pickle.load(file)  # noqa: S301

-    @lru_cache(maxsize=128)
-    def generate_points(
+    def _generate_points(
        self, load_1h_power: float, pv_power: float
    ) -> tuple[np.ndarray, np.ndarray]:
        """Generate the grid points for interpolation."""
@@ -25,8 +24,20 @@ class SelfConsumptionProbabilityInterpolator:
        points = np.array([np.full_like(partial_loads, load_1h_power), partial_loads]).T
        return points, partial_loads

+    @cachemethod_energy_management
    def calculate_self_consumption(self, load_1h_power: float, pv_power: float) -> float:
-        points, partial_loads = self.generate_points(load_1h_power, pv_power)
+        """Calculate the PV self-consumption rate using RegularGridInterpolator.
+
+        The results are cached until the start of the next energy management run/ optimization.
+
+        Args:
+         - last_1h_power: 1h power levels (W).
+         - pv_power: Current PV power output (W).
+
+        Returns:
+         - Self-consumption rate as a float.
+        """
+        points, partial_loads = self._generate_points(load_1h_power, pv_power)
        probabilities = self.interpolator(points)
        return probabilities.sum()

--- a/src/akkudoktoreos/prediction/load.py
+++ b/src/akkudoktoreos/prediction/load.py
@@ -1,6 +1,6 @@
 """Load forecast module for load predictions."""

-from typing import Optional, Union
+from typing import Optional

 from pydantic import Field, field_validator

@@ -21,6 +21,20 @@ load_providers = [
 ]


+class LoadCommonProviderSettings(SettingsBaseModel):
+    """Load Prediction Provider Configuration."""
+
+    LoadAkkudoktor: Optional[LoadAkkudoktorCommonSettings] = Field(
+        default=None, description="LoadAkkudoktor settings", examples=[None]
+    )
+    LoadVrm: Optional[LoadVrmCommonSettings] = Field(
+        default=None, description="LoadVrm settings", examples=[None]
+    )
+    LoadImport: Optional[LoadImportCommonSettings] = Field(
+        default=None, description="LoadImport settings", examples=[None]
+    )
+
+
 class LoadCommonSettings(SettingsBaseModel):
    """Load Prediction Configuration."""

@@ -30,9 +44,18 @@ class LoadCommonSettings(SettingsBaseModel):
        examples=["LoadAkkudoktor"],
    )

-    provider_settings: Optional[
-        Union[LoadAkkudoktorCommonSettings, LoadVrmCommonSettings, LoadImportCommonSettings]
-    ] = Field(default=None, description="Provider settings", examples=[None])
+    provider_settings: LoadCommonProviderSettings = Field(
+        default_factory=LoadCommonProviderSettings,
+        description="Provider settings",
+        examples=[
+            # Example 1: Empty/default settings (all providers None)
+            {
+                "LoadAkkudoktor": None,
+                "LoadVrm": None,
+                "LoadImport": None,
+            },
+        ],
+    )

    # Validators
    @field_validator("provider", mode="after")
--- a/src/akkudoktoreos/prediction/loadakkudoktor.py
+++ b/src/akkudoktoreos/prediction/loadakkudoktor.py
@@ -90,7 +90,9 @@ class LoadAkkudoktor(LoadProvider):
            )
            # Calculate values in W by relative profile data and yearly consumption given in kWh
            data_year_energy = (
-                profile_data * self.config.load.provider_settings.loadakkudoktor_year_energy * 1000
+                profile_data
+                * self.config.load.provider_settings.LoadAkkudoktor.loadakkudoktor_year_energy
+                * 1000
            )
        except FileNotFoundError:
            error_msg = f"Error: File {load_file} not found."
@@ -108,8 +110,8 @@ class LoadAkkudoktor(LoadProvider):
        weekday_adjust, weekend_adjust = self._calculate_adjustment(data_year_energy)
        # We provide prediction starting at start of day, to be compatible to old system.
        # End date for prediction is prediction hours from now.
-        date = self.start_datetime.start_of("day")
-        end_date = self.start_datetime.add(hours=self.config.prediction.hours)
+        date = self.ems_start_datetime.start_of("day")
+        end_date = self.ems_start_datetime.add(hours=self.config.prediction.hours)
        while compare_datetimes(date, end_date).lt:
            # Extract mean (index 0) and standard deviation (index 1) for the given day and hour
            # Day indexing starts at 0, -1 because of that
--- a/src/akkudoktoreos/prediction/loadimport.py
+++ b/src/akkudoktoreos/prediction/loadimport.py
@@ -60,10 +60,14 @@ class LoadImport(LoadProvider, PredictionImportProvider):
        return "LoadImport"

    def _update_data(self, force_update: Optional[bool] = False) -> None:
-        if self.config.load.provider_settings is None:
+        if self.config.load.provider_settings.LoadImport is None:
            logger.debug(f"{self.provider_id()} data update without provider settings.")
            return
-        if self.config.load.provider_settings.import_file_path:
-            self.import_from_file(self.config.provider_settings.import_file_path, key_prefix="load")
-        if self.config.load.provider_settings.import_json:
-            self.import_from_json(self.config.load.provider_settings.import_json, key_prefix="load")
+        if self.config.load.provider_settings.LoadImport.import_file_path:
+            self.import_from_file(
+                self.config.provider_settings.LoadImport.import_file_path, key_prefix="load"
+            )
+        if self.config.load.provider_settings.LoadImport.import_json:
+            self.import_from_json(
+                self.config.load.provider_settings.LoadImport.import_json, key_prefix="load"
+            )
--- a/src/akkudoktoreos/prediction/loadvrm.py
+++ b/src/akkudoktoreos/prediction/loadvrm.py
@@ -4,13 +4,12 @@ from typing import Any, Optional, Union

 import requests
 from loguru import logger
-from pendulum import DateTime
 from pydantic import Field, ValidationError

 from akkudoktoreos.config.configabc import SettingsBaseModel
 from akkudoktoreos.core.pydantic import PydanticBaseModel
 from akkudoktoreos.prediction.loadabc import LoadProvider
-from akkudoktoreos.utils.datetimeutil import to_datetime
+from akkudoktoreos.utils.datetimeutil import DateTime, to_datetime


 class VrmForecastRecords(PydanticBaseModel):
@@ -57,8 +56,8 @@ class LoadVrm(LoadProvider):
    def _request_forecast(self, start_ts: int, end_ts: int) -> VrmForecastResponse:
        """Fetch forecast data from Victron VRM API."""
        base_url = "https://vrmapi.victronenergy.com/v2/installations"
-        installation_id = self.config.load.provider_settings.load_vrm_idsite
-        api_token = self.config.load.provider_settings.load_vrm_token
+        installation_id = self.config.load.provider_settings.LoadVrm.load_vrm_idsite
+        api_token = self.config.load.provider_settings.LoadVrm.load_vrm_token

        url = f"{base_url}/{installation_id}/stats?type=forecast&start={start_ts}&end={end_ts}&interval=hours"
        headers = {"X-Authorization": f"Token {api_token}", "Content-Type": "application/json"}
@@ -80,8 +79,8 @@ class LoadVrm(LoadProvider):

    def _update_data(self, force_update: Optional[bool] = False) -> None:
        """Fetch and store VRM load forecast as load_mean and related values."""
-        start_date = self.start_datetime.start_of("day")
-        end_date = self.start_datetime.add(hours=self.config.prediction.hours)
+        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())
        end_ts = int(end_date.timestamp())

--- a/src/akkudoktoreos/prediction/prediction.py
+++ b/src/akkudoktoreos/prediction/prediction.py
@@ -34,6 +34,8 @@ from akkudoktoreos.config.configabc import SettingsBaseModel
 from akkudoktoreos.prediction.elecpriceakkudoktor import ElecPriceAkkudoktor
 from akkudoktoreos.prediction.elecpriceenergycharts import ElecPriceEnergyCharts
 from akkudoktoreos.prediction.elecpriceimport import ElecPriceImport
+from akkudoktoreos.prediction.feedintarifffixed import FeedInTariffFixed
+from akkudoktoreos.prediction.feedintariffimport import FeedInTariffImport
 from akkudoktoreos.prediction.loadakkudoktor import LoadAkkudoktor
 from akkudoktoreos.prediction.loadimport import LoadImport
 from akkudoktoreos.prediction.loadvrm import LoadVrm
@@ -67,6 +69,7 @@ class PredictionCommonSettings(SettingsBaseModel):
    hours: Optional[int] = Field(
        default=48, ge=0, description="Number of hours into the future for predictions"
    )
+
    historic_hours: Optional[int] = Field(
        default=48,
        ge=0,
@@ -88,6 +91,8 @@ class Prediction(PredictionContainer):
            ElecPriceAkkudoktor,
            ElecPriceEnergyCharts,
            ElecPriceImport,
+            FeedInTariffFixed,
+            FeedInTariffImport,
            LoadAkkudoktor,
            LoadVrm,
            LoadImport,
@@ -105,6 +110,8 @@ class Prediction(PredictionContainer):
 elecprice_akkudoktor = ElecPriceAkkudoktor()
 elecprice_energy_charts = ElecPriceEnergyCharts()
 elecprice_import = ElecPriceImport()
+feedintariff_fixed = FeedInTariffFixed()
+feedintariff_import = FeedInTariffImport()
 load_akkudoktor = LoadAkkudoktor()
 load_vrm = LoadVrm()
 load_import = LoadImport()
@@ -125,6 +132,8 @@ def get_prediction() -> Prediction:
            elecprice_akkudoktor,
            elecprice_energy_charts,
            elecprice_import,
+            feedintariff_fixed,
+            feedintariff_import,
            load_akkudoktor,
            load_vrm,
            load_import,
--- a/src/akkudoktoreos/prediction/predictionabc.py
+++ b/src/akkudoktoreos/prediction/predictionabc.py
@@ -11,7 +11,6 @@ and manipulation of configuration and prediction data in a clear, scalable, and
 from typing import List, Optional

 from loguru import logger
-from pendulum import DateTime
 from pydantic import Field, computed_field

 from akkudoktoreos.core.coreabc import MeasurementMixin
@@ -23,7 +22,7 @@ from akkudoktoreos.core.dataabc import (
    DataRecord,
    DataSequence,
 )
-from akkudoktoreos.utils.datetimeutil import to_duration
+from akkudoktoreos.utils.datetimeutil import DateTime, to_duration


 class PredictionBase(DataBase, MeasurementMixin):
@@ -119,17 +118,21 @@ class PredictionStartEndKeepMixin(PredictionBase):
        Returns:
            Optional[DateTime]: The calculated end datetime, or `None` if inputs are missing.
        """
-        if self.start_datetime and self.config.prediction.hours:
-            end_datetime = self.start_datetime + to_duration(
+        if self.ems_start_datetime and self.config.prediction.hours:
+            end_datetime = self.ems_start_datetime + to_duration(
                f"{self.config.prediction.hours} hours"
            )
-            dst_change = end_datetime.offset_hours - self.start_datetime.offset_hours
-            logger.debug(f"Pre: {self.start_datetime}..{end_datetime}: DST change: {dst_change}")
+            dst_change = end_datetime.offset_hours - self.ems_start_datetime.offset_hours
+            logger.debug(
+                f"Pre: {self.ems_start_datetime}..{end_datetime}: DST change: {dst_change}"
+            )
            if dst_change < 0:
                end_datetime = end_datetime + to_duration(f"{abs(int(dst_change))} hours")
            elif dst_change > 0:
                end_datetime = end_datetime - to_duration(f"{abs(int(dst_change))} hours")
-            logger.debug(f"Pst: {self.start_datetime}..{end_datetime}: DST change: {dst_change}")
+            logger.debug(
+                f"Pst: {self.ems_start_datetime}..{end_datetime}: DST change: {dst_change}"
+            )
            return end_datetime
        return None

@@ -141,7 +144,7 @@ class PredictionStartEndKeepMixin(PredictionBase):
        Returns:
            Optional[DateTime]: The calculated retention cutoff datetime, or `None` if inputs are missing.
        """
-        if self.start_datetime is None:
+        if self.ems_start_datetime is None:
            return None
        historic_hours = self.historic_hours_min()
        if (
@@ -149,7 +152,7 @@ class PredictionStartEndKeepMixin(PredictionBase):
            and self.config.prediction.historic_hours > historic_hours
        ):
            historic_hours = int(self.config.prediction.historic_hours)
-        return self.start_datetime - to_duration(f"{historic_hours} hours")
+        return self.ems_start_datetime - to_duration(f"{historic_hours} hours")

    @computed_field  # type: ignore[prop-decorator]
    @property
@@ -162,7 +165,7 @@ class PredictionStartEndKeepMixin(PredictionBase):
        end_dt = self.end_datetime
        if end_dt is None:
            return None
-        duration = end_dt - self.start_datetime
+        duration = end_dt - self.ems_start_datetime
        return int(duration.total_hours())

    @computed_field  # type: ignore[prop-decorator]
@@ -176,7 +179,7 @@ class PredictionStartEndKeepMixin(PredictionBase):
        keep_dt = self.keep_datetime
        if keep_dt is None:
            return None
-        duration = self.start_datetime - keep_dt
+        duration = self.ems_start_datetime - keep_dt
        return int(duration.total_hours())


--- a/src/akkudoktoreos/prediction/pvforecast.py
+++ b/src/akkudoktoreos/prediction/pvforecast.py
@@ -1,6 +1,6 @@
 """PV forecast module for PV power predictions."""

-from typing import Any, List, Optional, Self, Union
+from typing import Any, List, Optional, Self

 from pydantic import Field, computed_field, field_validator, model_validator

@@ -8,8 +8,7 @@ from akkudoktoreos.config.configabc import SettingsBaseModel
 from akkudoktoreos.prediction.prediction import get_prediction
 from akkudoktoreos.prediction.pvforecastabc import PVForecastProvider
 from akkudoktoreos.prediction.pvforecastimport import PVForecastImportCommonSettings
-from akkudoktoreos.prediction.pvforecastvrm import PVforecastVrmCommonSettings
-from akkudoktoreos.utils.docs import get_model_structure_from_examples
+from akkudoktoreos.prediction.pvforecastvrm import PVForecastVrmCommonSettings

 prediction_eos = get_prediction()

@@ -121,6 +120,17 @@ class PVForecastPlaneSetting(SettingsBaseModel):
        return pvtechchoice


+class PVForecastCommonProviderSettings(SettingsBaseModel):
+    """PV Forecast Provider Configuration."""
+
+    PVForecastImport: Optional[PVForecastImportCommonSettings] = Field(
+        default=None, description="PVForecastImport settings", examples=[None]
+    )
+    PVForecastVrm: Optional[PVForecastVrmCommonSettings] = Field(
+        default=None, description="PVForecastVrm settings", examples=[None]
+    )
+
+
 class PVForecastCommonSettings(SettingsBaseModel):
    """PV Forecast Configuration."""

@@ -135,20 +145,68 @@ class PVForecastCommonSettings(SettingsBaseModel):
        examples=["PVForecastAkkudoktor"],
    )

-    provider_settings: Optional[
-        Union[PVForecastImportCommonSettings, PVforecastVrmCommonSettings]
-    ] = Field(default=None, description="Provider settings", examples=[None])
+    provider_settings: PVForecastCommonProviderSettings = Field(
+        default_factory=PVForecastCommonProviderSettings,
+        description="Provider settings",
+        examples=[
+            # Example 1: Empty/default settings (all providers None)
+            {
+                "PVForecastImport": None,
+                "PVForecastVrm": None,
+            },
+        ],
+    )

    planes: Optional[list[PVForecastPlaneSetting]] = Field(
        default=None,
        description="Plane configuration.",
-        examples=[get_model_structure_from_examples(PVForecastPlaneSetting, True)],
+        examples=[
+            [
+                {
+                    "surface_tilt": 10.0,
+                    "surface_azimuth": 180.0,
+                    "userhorizon": [10.0, 20.0, 30.0],
+                    "peakpower": 5.0,
+                    "pvtechchoice": "crystSi",
+                    "mountingplace": "free",
+                    "loss": 14.0,
+                    "trackingtype": 0,
+                    "optimal_surface_tilt": False,
+                    "optimalangles": False,
+                    "albedo": None,
+                    "module_model": None,
+                    "inverter_model": None,
+                    "inverter_paco": 6000,
+                    "modules_per_string": 20,
+                    "strings_per_inverter": 2,
+                },
+                {
+                    "surface_tilt": 20.0,
+                    "surface_azimuth": 90.0,
+                    "userhorizon": [5.0, 15.0, 25.0],
+                    "peakpower": 3.5,
+                    "pvtechchoice": "crystSi",
+                    "mountingplace": "free",
+                    "loss": 14.0,
+                    "trackingtype": 1,
+                    "optimal_surface_tilt": False,
+                    "optimalangles": False,
+                    "albedo": None,
+                    "module_model": None,
+                    "inverter_model": None,
+                    "inverter_paco": 4000,
+                    "modules_per_string": 20,
+                    "strings_per_inverter": 2,
+                },
+            ]
+        ],
    )

    max_planes: Optional[int] = Field(
        default=0,
        ge=0,
        description="Maximum number of planes that can be set",
+        examples=[1, 2],
    )

    # Validators
--- a/src/akkudoktoreos/prediction/pvforecastakkudoktor.py
+++ b/src/akkudoktoreos/prediction/pvforecastakkudoktor.py
@@ -330,8 +330,8 @@ class PVForecastAkkudoktor(PVForecastProvider):
            logger.error(f"Akkudoktor schema change: {error_msg}")
            raise ValueError(error_msg)

-        if not self.start_datetime:
-            raise ValueError(f"Start DateTime not set: {self.start_datetime}")
+        if not self.ems_start_datetime:
+            raise ValueError(f"Start DateTime not set: {self.ems_start_datetime}")

        # Iterate over forecast data points
        for forecast_values in zip(*akkudoktor_data.values):
@@ -339,7 +339,7 @@ class PVForecastAkkudoktor(PVForecastProvider):
            dt = to_datetime(original_datetime, in_timezone=self.config.general.timezone)

            # Skip outdated forecast data
-            if compare_datetimes(dt, self.start_datetime.start_of("day")).lt:
+            if compare_datetimes(dt, self.ems_start_datetime.start_of("day")).lt:
                continue

            sum_dc_power = sum(values.dcPower for values in forecast_values)
@@ -357,7 +357,7 @@ class PVForecastAkkudoktor(PVForecastProvider):
        if len(self) < self.config.prediction.hours:
            raise ValueError(
                f"The forecast must cover at least {self.config.prediction.hours} hours, "
-                f"but only {len(self)} hours starting from {self.start_datetime} "
+                f"but only {len(self)} hours starting from {self.ems_start_datetime} "
                f"were predicted."
            )

--- a/src/akkudoktoreos/prediction/pvforecastimport.py
+++ b/src/akkudoktoreos/prediction/pvforecastimport.py
@@ -61,16 +61,16 @@ class PVForecastImport(PVForecastProvider, PredictionImportProvider):
        return "PVForecastImport"

    def _update_data(self, force_update: Optional[bool] = False) -> None:
-        if self.config.pvforecast.provider_settings is None:
+        if self.config.pvforecast.provider_settings.PVForecastImport is None:
            logger.debug(f"{self.provider_id()} data update without provider settings.")
            return
-        if self.config.pvforecast.provider_settings.import_file_path is not None:
+        if self.config.pvforecast.provider_settings.PVForecastImport.import_file_path is not None:
            self.import_from_file(
-                self.config.pvforecast.provider_settings.import_file_path,
+                self.config.pvforecast.provider_settings.PVForecastImport.import_file_path,
                key_prefix="pvforecast",
            )
-        if self.config.pvforecast.provider_settings.import_json is not None:
+        if self.config.pvforecast.provider_settings.PVForecastImport.import_json is not None:
            self.import_from_json(
-                self.config.pvforecast.provider_settings.import_json,
+                self.config.pvforecast.provider_settings.PVForecastImport.import_json,
                key_prefix="pvforecast",
            )
--- a/src/akkudoktoreos/prediction/pvforecastvrm.py
+++ b/src/akkudoktoreos/prediction/pvforecastvrm.py
@@ -4,13 +4,12 @@ from typing import Any, Optional, Union

 import requests
 from loguru import logger
-from pendulum import DateTime
 from pydantic import Field, ValidationError

 from akkudoktoreos.config.configabc import SettingsBaseModel
 from akkudoktoreos.core.pydantic import PydanticBaseModel
 from akkudoktoreos.prediction.pvforecastabc import PVForecastProvider
-from akkudoktoreos.utils.datetimeutil import to_datetime
+from akkudoktoreos.utils.datetimeutil import DateTime, to_datetime


 class VrmForecastRecords(PydanticBaseModel):
@@ -24,7 +23,7 @@ class VrmForecastResponse(PydanticBaseModel):
    totals: dict


-class PVforecastVrmCommonSettings(SettingsBaseModel):
+class PVForecastVrmCommonSettings(SettingsBaseModel):
    """Common settings for VRM API."""

    pvforecast_vrm_token: str = Field(
@@ -60,8 +59,8 @@ class PVForecastVrm(PVForecastProvider):
    def _request_forecast(self, start_ts: int, end_ts: int) -> VrmForecastResponse:
        """Fetch forecast data from Victron VRM API."""
        source = "https://vrmapi.victronenergy.com/v2/installations"
-        id_site = self.config.pvforecast.provider_settings.pvforecast_vrm_idsite
-        api_token = self.config.pvforecast.provider_settings.pvforecast_vrm_token
+        id_site = self.config.pvforecast.provider_settings.PVForecastVrm.pvforecast_vrm_idsite
+        api_token = self.config.pvforecast.provider_settings.PVForecastVrm.pvforecast_vrm_token
        headers = {"X-Authorization": f"Token {api_token}", "Content-Type": "application/json"}
        url = f"{source}/{id_site}/stats?type=forecast&start={start_ts}&end={end_ts}&interval=hours"
        logger.debug(f"Requesting VRM forecast: {url}")
@@ -82,8 +81,8 @@ class PVForecastVrm(PVForecastProvider):

    def _update_data(self, force_update: Optional[bool] = False) -> None:
        """Update forecast data in the PVForecastDataRecord format."""
-        start_date = self.start_datetime.start_of("day")
-        end_date = self.start_datetime.add(hours=self.config.prediction.hours)
+        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())
        end_ts = int(end_date.timestamp())

--- a/src/akkudoktoreos/prediction/weather.py
+++ b/src/akkudoktoreos/prediction/weather.py
@@ -19,6 +19,14 @@ weather_providers = [
 ]


+class WeatherCommonProviderSettings(SettingsBaseModel):
+    """Weather Forecast Provider Configuration."""
+
+    WeatherImport: Optional[WeatherImportCommonSettings] = Field(
+        default=None, description="WeatherImport settings", examples=[None]
+    )
+
+
 class WeatherCommonSettings(SettingsBaseModel):
    """Weather Forecast Configuration."""

@@ -28,8 +36,15 @@ class WeatherCommonSettings(SettingsBaseModel):
        examples=["WeatherImport"],
    )

-    provider_settings: Optional[WeatherImportCommonSettings] = Field(
-        default=None, description="Provider settings", examples=[None]
+    provider_settings: WeatherCommonProviderSettings = Field(
+        default_factory=WeatherCommonProviderSettings,
+        description="Provider settings",
+        examples=[
+            # Example 1: Empty/default settings (all providers None)
+            {
+                "WeatherImport": None,
+            },
+        ],
    )

    # Validators
--- a/src/akkudoktoreos/prediction/weatherbrightsky.py
+++ b/src/akkudoktoreos/prediction/weatherbrightsky.py
@@ -94,7 +94,7 @@ class WeatherBrightSky(WeatherProvider):
            ValueError: If the API response does not include expected `weather` data.
        """
        source = "https://api.brightsky.dev"
-        date = to_datetime(self.start_datetime, as_string=True)
+        date = to_datetime(self.ems_start_datetime, as_string=True)
        last_date = to_datetime(self.end_datetime, as_string=True)
        response = requests.get(
            f"{source}/weather?lat={self.config.general.latitude}&lon={self.config.general.longitude}&date={date}&last_date={last_date}&tz={self.config.general.timezone}",
@@ -223,7 +223,7 @@ class WeatherBrightSky(WeatherProvider):
        assert key  # noqa: S101
        temperature = self.key_to_array(
            key=key,
-            start_datetime=self.start_datetime,
+            start_datetime=self.ems_start_datetime,
            end_datetime=self.end_datetime,
            interval=to_duration("1 hour"),
        )
@@ -236,7 +236,7 @@ class WeatherBrightSky(WeatherProvider):
        assert key  # noqa: S101
        humidity = self.key_to_array(
            key=key,
-            start_datetime=self.start_datetime,
+            start_datetime=self.ems_start_datetime,
            end_datetime=self.end_datetime,
            interval=to_duration("1 hour"),
        )
@@ -250,7 +250,10 @@ class WeatherBrightSky(WeatherProvider):
            data=data,
            index=pd.DatetimeIndex(
                pd.date_range(
-                    start=self.start_datetime, end=self.end_datetime, freq="1h", inclusive="left"
+                    start=self.ems_start_datetime,
+                    end=self.end_datetime,
+                    freq="1h",
+                    inclusive="left",
                )
            ),
        )
--- a/src/akkudoktoreos/prediction/weatherclearoutside.py
+++ b/src/akkudoktoreos/prediction/weatherclearoutside.py
@@ -301,7 +301,8 @@ class WeatherClearOutside(WeatherProvider):

            # Converting the cloud cover into Irradiance (GHI, DNI, DHI)
            cloud_cover = pd.Series(
-                data=clearout_data["Total Clouds (% Sky Obscured)"], index=clearout_data["DateTime"]
+                data=clearout_data["Total Clouds (% Sky Obscured)"],
+                index=pd.to_datetime(clearout_data["DateTime"]),
            )
            ghi, dni, dhi = self.estimate_irradiance_from_cloud_cover(
                self.config.general.latitude, self.config.general.longitude, cloud_cover
--- a/src/akkudoktoreos/prediction/weatherimport.py
+++ b/src/akkudoktoreos/prediction/weatherimport.py
@@ -61,14 +61,16 @@ class WeatherImport(WeatherProvider, PredictionImportProvider):
        return "WeatherImport"

    def _update_data(self, force_update: Optional[bool] = False) -> None:
-        if self.config.weather.provider_settings is None:
+        if self.config.weather.provider_settings.WeatherImport is None:
            logger.debug(f"{self.provider_id()} data update without provider settings.")
            return
-        if self.config.weather.provider_settings.import_file_path:
+        if self.config.weather.provider_settings.WeatherImport.import_file_path:
            self.import_from_file(
-                self.config.weather.provider_settings.import_file_path, key_prefix="weather"
+                self.config.weather.provider_settings.WeatherImport.import_file_path,
+                key_prefix="weather",
            )
-        if self.config.weather.provider_settings.import_json:
+        if self.config.weather.provider_settings.WeatherImport.import_json:
            self.import_from_json(
-                self.config.weather.provider_settings.import_json, key_prefix="weather"
+                self.config.weather.provider_settings.WeatherImport.import_json,
+                key_prefix="weather",
            )