Generating Outputs with ERA5 Data

geodata currently supports the following wind outputs using ERA5 data from the Copernicus Data Store.

• Wind generation time-series (wind)

• Wind speed time-series (windspd)

• Solar photovoltaic generation time-series (pv)

Supported ERA5 Outputs

Wind Generation Time-series

Convert wind speeds for turbine to wind energy generation.

cutout.wind(turbine: str | dict[str, str], smooth: bool = False)

Parameters

• cutout - Cutout - A cutout created by geodata.Cutout()

• turbine - str | dict - Name of a turbine known by the reatlas client or a turbineconfig dictionary with the keys ‘hub_height’ for the hub height and ‘V’, ‘POW’ defining the power curve. For a full list of currently supported turbines, see the list of turbines here.

• smooth - bool | dict - If True, smooth power curve with a gaussian kernel as determined for the Danish wind fleet to Delta_v = 1.27 and sigma = 2.29. A dict allows to tune these values.

Note: You can also specify all of the general conversion arguments documented in the convert_and_aggregate function (e.g. var_height='lml').

Example Code

ds_wind = cutout.wind(turbine="Suzlon_S82_1.5_MW", smooth=True)
ds_wind.to_dataframe(name="wind")

Wind Speed Time-series

Extract wind speeds at given height (ms-1)

geodata.convert.windspd(cutout: geodata.Cutout, **params)

Parameters

• cutout - str - A cutout created by geodata.Cutout()

• **params - Must have 1 of the following:

  • turbine - str | dict - Name of a turbine known by the reatlas client or a turbineconfig dictionary with the keys ‘hub_height’ for the hub height and ‘V’, ‘POW’ defining the power curve. For a full list of currently supported turbines, the list of turbines here.

  • hub-height - int - Extrapolation height (m) Note: You can also specify all of the general conversion arguments documented in the convert_and_aggregate function (e.g. var_height='lml').

Example Code

ds_windspd = cutout.windspd(turbine='Vestas_V66_1750kW')
ds_windspd.to_dataframe(name = 'windspd')

Solar photovoltaic generation time-series

Convert downward-shortwave, upward-shortwave radiation flux and ambient temperature into a pv generation time-series.

cutout.pv(panel, orientation, clearsky_model)

Parameters

• cutout - string - A cutout created by geodata.Cutout()

• panel - string - Specify a solar panel type on which to base the calculation. geodata contains an internal solar panel dictionary with keys defining several solar panel characteristics used for the time-series calculation. For a complete list of included panel types, see the list of panel types here.

• orientation - str, dict or callback - Panel orientation can be chosen from either latitude_optimal, a constant orientation such as {'slope': 0.0,'azimuth': 0.0}, or a callback function with the same signature as the callbacks generated by the geodata.pv.orientation.make_* functions.

• (optional) clearsky_model - string or None - Either the simple or the enhanced Reindl clearsky model. The default choice of None will choose dependending on data availability, since the enhanced model also incorporates ambient air temperature and relative humidity.

Example Code and Result

ds_pv = geodata.convert.pv(panel="KANEKA", orientation = "latitude_optimal")
ds_pv.to_dataframe(name = 'pv')

Example Output

With the full list of supported ERA5 outputs above, we can see an example of generating output ERA5 data from the Copernicus Data Store.

Setup

Let’s assume we’ve created an ERA5 cutout along the following lines:

cutout = geodata.Cutout(name="era5-europe-example",
    module="era5",
    weather_data_config="era5_monthly",
    xs=slice(30, 41.56244222),
    ys=slice(33.56459975, 35),
    years=slice(2011, 2011),
    months=slice(1,1)
)

Note that unlike datasets, we don’t need explicitly prepare a cutout. Geodata will prepare the cutout automatically if it’s not ready. We can now use this cutout to generate datasets.

Creating a Solar photovoltaic (pv) generation time-series

To create a pv generation time-series, we can use the following code with our ERA5 cutout:

ds = cutout.pv(panel="KANEKA", orientation="latitude_optimal")

Some information about the parameters:

• panel - string - Specify a solar panel type on which to base the calculation. geodata contains an internal solar panel dictionary with keys defining several solar panel characteristics used for the time-series calculation. For a complete list of included panel types, see the list of panel types here.

• orientation - str, dict or callback - Panel orientation can be chosen from either latitude_optimal, a constant orientation such as {'slope': 0.0,'azimuth': 0.0}, or a callback function with the same signature as the callbacks generated by the geodata.pv.orientation.make_* functions.

• (optional) clearsky_model - string or None - Either the simple or the enhanced Reindl clearsky model. The default choice of None will choose dependending on data availability, since the enhanced model also incorporates ambient air temperature and relative humidity.

The convert function returns an xarray dataset, which is an in-memory representation of a NetCDF file:

<xarray.DataArray 'AC power' (y: 5, time: 744, x: 47)>
array([[[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],

Coordinates:
    lon      (x) float32 30.0 30.25 30.5 30.75 31.0 ... 40.75 41.0 41.25 41.5
    lat      (y) float32 34.815 34.565 34.315 34.065 33.815
  * x        (x) float32 30.0 30.25 30.5 30.75 31.0 ... 40.75 41.0 41.25 41.5
  * y        (y) float32 34.815 34.565 34.315 34.065 33.815
  * time     (time) datetime64[ns] 2011-01-01 ... 2011-01-31T23:00:00

To convert this array to a more conventional dataframe, we can run:

df = ds.to_dataframe(name='pv')

which converts the xarray dataset into a pandas dataframe.

The result is a dataset with an observation for each time period (in this ERA5 case, hourly) and geographic point with the calculated pv generation for each observation.

Finally, we can run something like:

df.to_csv('era5_pv_data.csv')

to output the data to csv for use in other applications.