Energy Collection

class emflow.assets.energycollection.Site(name: Optional[str] = None, assets: List[energydatamodel.base.EnergyAsset] = <factory>, longitude: Optional[float] = None, latitude: Optional[float] = None, altitude: Optional[float] = None, tz: Optional[timezone] = None, location: Optional[energydatamodel.geospatial.GeoLocation] = None, *, collections: Optional[List[ForwardRef('EnergyCollection')]] = <factory>, timeseries: Optional[List[Union[energydatamodel.timeseries.TimeSeries, energydatamodel.timeseries.TimeSeriesTable]]] = None)

Bases: EnergyCollection

assets: List[EnergyAsset]

longitude: float | None = None

latitude: float | None = None

altitude: float | None = None

tz: timezone | None = None

location: GeoLocation | None = None

name: str | None = None

add_assets(assets: EnergyAsset | List[EnergyAsset])

remove_asset(asset: EnergyAsset)

list_assets()

get_summary()

add_collection(collection: EnergyCollection)

geometry_to_geojson(geometry)

get_asset_by_name(name: str)

list_collections()

plot_timeseries(columns: List[str] | None = None, start_date: str | None = None, end_date: str | None = None) → Axes

Plot signals from the collection’s timeseries list.

Each entry in self.timeseries is converted to a pandas DataFrame, column names are prefixed with the entry’s name (if set) to avoid collisions, and the results are concatenated before plotting.

Args:

columns: Signal names to plot. Defaults to all columns. start_date: ISO 8601 start date string for slicing (inclusive). end_date: ISO 8601 end date string for slicing (inclusive).

Returns:

The Matplotlib Axes object of the plot.

remove_collection(collection: EnergyCollection)

timeseries: t.Optional[t.List[t.Union[TimeSeries, TimeSeriesTable]]] = None

to_dataframe()

Convert data class to a pandas DataFrame.

to_geojson(exclude_none=True)

to_json(include_none: bool = False) → str

to_tree(only_named=True, show_type=False, return_tree=False)

collections: t.Optional[t.List['EnergyCollection']]

class emflow.assets.energycollection.EnergyCommunity(*, name: str | None = None, assets: ~typing.List[~energydatamodel.base.EnergyAsset] | None = <factory>, collections: ~typing.List[~energydatamodel.base.EnergyCollection] | None = <factory>, timeseries: ~typing.List[~energydatamodel.timeseries.TimeSeries | ~energydatamodel.timeseries.TimeSeriesTable] | None = None)

Bases: EnergyCollection

A Portfolio is like an EnergySystem but is used more for the purpose of trading energy rather than maintaining an energy balance.

add_assets(assets: EnergyAsset | List[EnergyAsset])

add_collection(collection: EnergyCollection)

geometry_to_geojson(geometry)

get_asset_by_name(name: str)

list_assets()

list_collections()

name: t.Optional[str] = None

plot_timeseries(columns: List[str] | None = None, start_date: str | None = None, end_date: str | None = None) → Axes

Plot signals from the collection’s timeseries list.

Each entry in self.timeseries is converted to a pandas DataFrame, column names are prefixed with the entry’s name (if set) to avoid collisions, and the results are concatenated before plotting.

Args:

columns: Signal names to plot. Defaults to all columns. start_date: ISO 8601 start date string for slicing (inclusive). end_date: ISO 8601 end date string for slicing (inclusive).

Returns:

The Matplotlib Axes object of the plot.

remove_asset(asset: EnergyAsset)

remove_collection(collection: EnergyCollection)

timeseries: t.Optional[t.List[t.Union[TimeSeries, TimeSeriesTable]]] = None

to_dataframe()

Convert data class to a pandas DataFrame.

to_geojson(exclude_none=True)

to_json(include_none: bool = False) → str

to_tree(only_named=True, show_type=False, return_tree=False)

assets: t.Optional[t.List[EnergyAsset]]

collections: t.Optional[t.List['EnergyCollection']]

class emflow.assets.energycollection.Portfolio(*, name: str | None = None, assets: ~typing.List[~energydatamodel.base.EnergyAsset] | None = <factory>, collections: ~typing.List[~energydatamodel.base.EnergyCollection] | None = <factory>, timeseries: ~typing.List[~energydatamodel.timeseries.TimeSeries | ~energydatamodel.timeseries.TimeSeriesTable] | None = None)

Bases: EnergyCollection

A Portfolio is like an EnergySystem but is used more for the purpose of trading energy rather than maintaining an energy balance.

plot_timeseries(start_date: str | None = None, end_date: str | None = None, subplots: bool = False) → Tuple[Figure, Axes] | Tuple[Figure, ndarray]

Plot signals from the collection’s timeseries list.

Each entry in self.timeseries is converted to a pandas DataFrame, column names are prefixed with the entry’s name (if set) to avoid collisions, and the results are concatenated before plotting.

Args:

columns: Signal names to plot. Defaults to all columns. start_date: ISO 8601 start date string for slicing (inclusive). end_date: ISO 8601 end date string for slicing (inclusive).

Returns:

The Matplotlib Axes object of the plot.

add_assets(assets: EnergyAsset | List[EnergyAsset])

add_collection(collection: EnergyCollection)

geometry_to_geojson(geometry)

get_asset_by_name(name: str)

list_assets()

list_collections()

name: t.Optional[str] = None

remove_asset(asset: EnergyAsset)

remove_collection(collection: EnergyCollection)

timeseries: t.Optional[t.List[t.Union[TimeSeries, TimeSeriesTable]]] = None

to_dataframe()

Convert data class to a pandas DataFrame.

to_geojson(exclude_none=True)

to_json(include_none: bool = False) → str

to_tree(only_named=True, show_type=False, return_tree=False)

assets: t.Optional[t.List[EnergyAsset]]

collections: t.Optional[t.List['EnergyCollection']]