energia.library.external

energia.library.external#

Process conversion models (external libraries)

Functions

pvlib(data, coord[, sam, module_params, ...])

Calculates solar power output using weather data Relevant factors include DHI (W/m2), DNI (W/m2), GHI (W/m2), Temperature (C), Dewpoint (C), Relative humidity (%)

windpowerlib(data[, roughness_length, ...])

Calculates wind power output using weather data Relevant factors include wind speeds (m/s), temperature (K), and pressure(Pa)

Classes

DataFrame([data, index, columns, dtype, copy])

Two-dimensional, size-mutable, potentially heterogeneous tabular data.
pvlib(data: DataFrame, coord: tuple[float, float], sam: str = 'cecmod', module_params: str = 'Canadian_Solar_Inc__CS5P_220M', inverter: str = 'cecinverter', inverter_params: str = 'ABB__MICRO_0_25_I_OUTD_US_208__208V_', temperature_params: str = 'open_rack_glass_glass', aoi_model: str = 'no_loss', ac_model: str = 'sandia', spectral_model: str = 'no_loss') → list[float] | None[source]#

Calculates solar power output using weather data Relevant factors include DHI (W/m2), DNI (W/m2), GHI (W/m2), Temperature (C), Dewpoint (C), Relative humidity (%)

Parameters:

  • data (DataFrame) – weather data input with dni, dhi, wind_speed, ghi, air_temperature, dew_point, relative_humidity

  • coord (tuple[float, float]) – latitude and longitude

  • sam (str, optional) – Defaults to ‘cecmod’.

  • module_parameters (str, optional) – Defaults to ‘Canadian_Solar_Inc__CS5P_220M’.

  • inverter (str, optional) – Defaults to ‘cecinverter’.

  • inverter_parameters (str, optional) – Defaults to ‘ABB__MICRO_0_25_I_OUTD_US_208__208V_’.

  • temperature_params (str, optional) – Defaults to ‘open_rack_glass_glass’.

  • aoi_model (str, optional) – Defaults to ‘no_loss’.

  • ac_model (str, optional) – Defaults to ‘sandia’.

  • spectral_model (str, optional) – Defaults to ‘no_loss’.

Returns:

a list with solar power outputs

Return type:

list[float] | None

windpowerlib(data: DataFrame, roughness_length: float = 0.1, turbine_type: str = 'V100/1800', hub_height: float = 92, wind_speed_model: str = 'logarithmic', density_model: str = 'ideal_gas', temperature_model: str = 'linear_gradient', power_output_model: str = 'power_coefficient_curve', density_correction: bool = True, obstacle_height: float = 0, observation_height: float = 10) → list[float] | None[source]#

Calculates wind power output using weather data Relevant factors include wind speeds (m/s), temperature (K), and pressure(Pa)

Parameters:

  • data (DataFrame) – weather data input with wind_speed, air_temperature, surface_pressure

  • roughness_length (float, optional) – Defaults to 0.1.

  • turbine_type (str, optional) – Defaults to ‘V100/1800’.

  • hub_height (float, optional) – Defaults to 92.

  • wind_speed_model (str, optional) – Defaults to ‘logarithmic’.

  • density_model (str, optional) – Defaults to ‘ideal_gas’.

  • temperature_model (str, optional) – Defaults to ‘linear_gradient’.

  • power_output_model (str, optional) – Defaults to power_coefficient_curve.

  • density_correction (bool, optional) – Defaults to True.

  • obstacle_height (float, optional) – Defaults to 0.

  • observation_height (float, optional) – Defaults to 10.

Returns:

a dataframe with hourly wind power outputs

Return type:

list[float] | None

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