Wind

class enflow.assets.wind.WindTurbine(capacity: float | pandas.core.frame.DataFrame, hub_height: float | None = None, rotor_diameter: float | None = None, turbine_model: str | None = None, power_curve: pandas.core.frame.DataFrame | dict | NoneType = None, power_coefficient_curve: pandas.core.frame.DataFrame | dict | NoneType = None, *, name: str | None = None, location: energydatamodel.geospatial.GeoLocation | None = None, latitude: float | None = None, longitude: float | None = None, altitude: float | None = None, tz: timezone | None = None, timeseries: energydatamodel.base.TimeSeries | None = None)

Bases: EnergyAsset

capacity: float | DataFrame

hub_height: float | None = None

rotor_diameter: float | None = None

turbine_model: str | None = None

power_curve: DataFrame | dict | None = None

power_coefficient_curve: DataFrame | dict | None = None

create_table_representation()

altitude: t.Optional[float] = None

geometry_to_geojson(geometry)

get_location()

latitude: t.Optional[float] = None

location: t.Optional[Location] = None

longitude: t.Optional[float] = None

name: t.Optional[str] = None

plot_timeseries(start_date: str | DatetimeIndex | None = None, end_date: str | DatetimeIndex | None = None) → Axes

Plots a pandas Series using its built-in plot method.

Args:

start_date: The start date for the plot. end_date: The end date for the plot.

Returns:

The Matplotlib Axes object of the plot.

timeseries: t.Optional[TimeSeries] = None

to_dataframe()

Convert data class to a pandas DataFrame.

to_geojson(exclude_none=True)

to_json(include_none: bool = False) → str

tz: t.Optional[pytz.timezone] = None

class enflow.assets.wind.WindFarm(wind_turbines: list[energydatamodel.wind.WindTurbine] = None, capacity: float | pandas.core.frame.DataFrame = None, farm_efficiency: pandas.core.frame.DataFrame | None = None, *, name: str | None = None, location: energydatamodel.geospatial.GeoLocation | None = None, latitude: float | None = None, longitude: float | None = None, altitude: float | None = None, tz: timezone | None = None, timeseries: energydatamodel.base.TimeSeries | None = None)

Bases: EnergyAsset

wind_turbines: list[WindTurbine] = None

capacity: float | DataFrame = None

farm_efficiency: DataFrame | None = None

altitude: t.Optional[float] = None

geometry_to_geojson(geometry)

get_location()

latitude: t.Optional[float] = None

location: t.Optional[Location] = None

longitude: t.Optional[float] = None

name: t.Optional[str] = None

plot_timeseries(start_date: str | DatetimeIndex | None = None, end_date: str | DatetimeIndex | None = None) → Axes

Plots a pandas Series using its built-in plot method.

Args:

start_date: The start date for the plot. end_date: The end date for the plot.

Returns:

The Matplotlib Axes object of the plot.

timeseries: t.Optional[TimeSeries] = None

to_dataframe()

Convert data class to a pandas DataFrame.

to_geojson(exclude_none=True)

to_json(include_none: bool = False) → str

tz: t.Optional[pytz.timezone] = None

class enflow.assets.wind.WindPowerArea(geopolygon: energydatamodel.geospatial.GeoPolygon, capacity: float | pandas.core.frame.DataFrame, area: float, wind_turbines: List[energydatamodel.wind.WindTurbine] | List[energydatamodel.wind.WindFarm] | NoneType = None, farm_efficiency: pandas.core.frame.DataFrame | None = None)

Bases: object

geopolygon: GeoPolygon

capacity: float | DataFrame

area: float

wind_turbines: List[WindTurbine] | List[WindFarm] | None = None

farm_efficiency: DataFrame | None = None