acept.pv_cap_factor_profiles

Module for building PV capacity factor profiles.

This module contains functions for building PV capacity factor profiles. The capacity factor is the unitless ratio of energy generated over a time period divided by the installed capacity. The installed capacity is defined as the theoretical maximum capacity of the PV system.

The PV capacity factor profiles are build using weather data for the selected region. Each building has its own PV capacity factor profile as the PV capacity factor is specific to the location of the building. The profiles contain the series of PV capacity factor values for each hour of the year.

For the computation of the PV capacity factor profiles, the library GSEE is used. GSEE (Global Solar Energy Estimator) is a solar energy simulation library designed for rapid calculations and ease of use. Renewables.ninja uses GSEE. Since the current official GSEE library is not maintained anymore, we recommend to use fork of the library from https://github.com/VeraKowalczuk/gsee. For more information about GSEE, see https://github.com/renewables-ninja/gsee or https://gsee.readthedocs.io/

Use this module to:

  • Build PV capacity factor profiles for a single year for all given buildings

  • Build PV capacity factor profiles for the typical meteorological year for all given buildings

  • Build PV capacity factor profiles for a single year for all given buildings from the DWD TRY data

  • Build PV capacity factor profiles for multiple years for all given buildings from the DWD TRY data

Use the build_pv_capacity_profile_for_year() function to build a PV capacity factor profile for a single year. This function builds PV capacity factor profiles based on the available weather data (DWD TRY or TMY) for the selected region.

Module Contents

Functions

build_pv_capacity_for_selected_years(→ str)

Build PV capacity factor profiles for all years between year_start and year_end for all given buildings from the

build_pv_capacity_for_selected_year_with_combined_data(→ str)

Build PV capacity factor profiles for a single year between for all given buildings from the

build_pv_capacity_for_selected_years_with_combined_data(...)

Build PV capacity factor profiles for all years between year_start and year_end for all given buildings from the

calculate_gsee_input_weather_from_raw_weather(...)

Calculate the GSEE input weather from the raw weather, including the diffuse fraction.

build_pv_capacity_profile_for_year(selected_shape, ...)

Build PV capacity factor profiles for a single year for all given buildings from the DWD TRY data. The profiles are

calculate_pv_capacity_profile_based_on_tmy_weather(...)

Build PV capacity factor profiles for a typical meteorological year (TMY) for all given buildings from the

get_tmy_as_input_weather_for_gsee_pv_cap(...)

Build weather profile for a typical meteorological year (TMY) from the PVGIS API using the maximal time period
acept.pv_cap_factor_profiles.build_pv_capacity_for_selected_years(selected_shape: geopandas.GeoDataFrame, buildings: geopandas.GeoDataFrame, year_start: int = DWD_MIN_YEAR, year_end: int = DWD_MAX_YEAR, building_specific_weather: bool = False, debug: bool = True) → str[source]

Build PV capacity factor profiles for all years between year_start and year_end for all given buildings from the DWD TRY data. The weather data is expected as downloaded from the DWD OPENDATA portal. The profiles will be saved in a temporary directory in the acept.acept_constants.TEMP_PATH directory as one CSV file per building per year.

Parameters:

  • selected_shape (geopandas.GeoDataFrame) – GeoDataFrame containing the shape of the area around the buildings.

  • buildings (geopandas.GeoDataFrame) – GeoDataFrame containing the buildings.

  • year_start (int) – Start year of the PV capacity factor profiles. Must be between DWD_MIN_YEAR and DWD_MAX_YEAR.

  • year_end (int) – End year of the PV capacity factor profiles. Must be between DWD_MIN_YEAR and DWD_MAX_YEAR.

  • building_specific_weather (bool) – Whether to use building specific weather data. Defaults to False.

  • debug (bool) – Whether to print debug messages. Defaults to True.

Raises:

ValueOutsideRangeError – If year_start or year_end are outside the valid range (see DWD_MAX_RANGE)

Returns:

Path to the directory containing the created CSV files.

Return type:

str

acept.pv_cap_factor_profiles.build_pv_capacity_for_selected_year_with_combined_data(selected_shape: geopandas.GeoDataFrame, buildings: geopandas.GeoDataFrame, year: int = 2011, uncompressed: bool = False, building_specific_weather: bool = False, debug: bool = True) → str[source]

Build PV capacity factor profiles for a single year between for all given buildings from the DWD TRY data. The weather data is expected as combined files for the different features - use the acept.dwd_try_data_handling.combine_dwd_try_data_and_save() function to combine the data. The profiles will be saved in a temporary directory in the acept.acept_constants.TEMP_PATH directory as one CSV file per building.

Parameters:

  • selected_shape (geopandas.GeoDataFrame) – GeoDataFrame containing the shape of the area around the buildings.

  • buildings (geopandas.GeoDataFrame) – GeoDataFrame containing the buildings.

  • year (int) – Year the PV capacity factor profiles are calculated for. Must be between DWD_MIN_YEAR and DWD_MAX_YEAR.

  • uncompressed (bool) – Whether to use uncompressed DWD TRY data files. Defaults to False.

  • building_specific_weather (bool) – Whether to use building specific weather data. Defaults to False.

  • debug (bool) – Whether to print debug messages. Defaults to True.

Returns:

Path to the directory containing the created CSV files.

Return type:

str

acept.pv_cap_factor_profiles.build_pv_capacity_for_selected_years_with_combined_data(selected_shape: geopandas.GeoDataFrame, buildings: geopandas.GeoDataFrame, year_start: int = DWD_MIN_YEAR, year_end: int = DWD_MAX_YEAR, uncompressed: bool = False, building_specific_weather: bool = False, debug: bool = True)[source]

Build PV capacity factor profiles for all years between year_start and year_end for all given buildings from the DWD TRY data. The weather data is expected as combined files for the different features - use the acept.dwd_try_data_handling.combine_dwd_try_data_and_save() function to combine the data. The profiles will be saved in a temporary directory in the acept.acept_constants.TEMP_PATH directory as one CSV file per building per year.

Parameters:

  • selected_shape (geopandas.GeoDataFrame) – GeoDataFrame containing the shape of the area around the buildings.

  • buildings (geopandas.GeoDataFrame) – GeoDataFrame containing the buildings.

  • year_start (int) – First year the PV capacity factor profiles are calculated for. Must be between DWD_MIN_YEAR and DWD_MAX_YEAR.

  • year_end (int) – Last year the PV capacity factor profiles are calculated for. Must be between DWD_MIN_YEAR and DWD_MAX_YEAR.

  • uncompressed (bool) – Whether to use uncompressed DWD TRY data files. Defaults to False.

  • building_specific_weather (bool) – Whether to use building specific weather data. Defaults to False.

  • debug (bool) – Whether to print debug messages. Defaults to True.

Raises:

ValueOutsideRangeError – If year_start or year_end is outside the allowed range (see DWD_MAX_RANGE).

Returns:

Path to the directory containing the created CSV files.

acept.pv_cap_factor_profiles.calculate_gsee_input_weather_from_raw_weather(weather: xarray.Dataset | pandas.DataFrame, rad_diffuse_col: bool = False) → xarray.Dataset | pandas.DataFrame[source]

Calculate the GSEE input weather from the raw weather, including the diffuse fraction.

Parameters:

  • weather (xarray.Dataset | pandas.DataFrame) – The raw weather data as xarray.Dataset or pandas.DataFrame

  • rad_diffuse_col (bool) – Whether the diffuse radiation column exists or has to be calculated.

Returns:

The GSEE input weather

Return type:

xarray.Dataset | pandas.DataFrame

acept.pv_cap_factor_profiles.build_pv_capacity_profile_for_year(selected_shape: geopandas.GeoDataFrame, buildings: geopandas.GeoDataFrame, year: int | None, debug: bool = True)[source]

Build PV capacity factor profiles for a single year for all given buildings from the DWD TRY data. The profiles are saved in a temporary directory in the acept.acept_constants.TEMP_PATH directory as one CSV file per building.

Parameters:

  • selected_shape (geopandas.GeoDataFrame) – GeoDataFrame containing the shape of the area around the buildings.

  • buildings (geopandas.GeoDataFrame) – GeoDataFrame containing the buildings.

  • year (int | None) – Year the PV capacity factor profiles are calculated for. Must be between DWD_MIN_YEAR and DWD_MAX_YEAR. If None, the PV capacity factor profiles are calculated for the typical meteorological year (TMY).

  • debug (bool) – Whether to print debug messages. Defaults to True.

Returns:

Path to the directory containing the created CSV files.

acept.pv_cap_factor_profiles.calculate_pv_capacity_profile_based_on_tmy_weather(selected_shape: geopandas.GeoDataFrame, buildings: geopandas.GeoDataFrame, building_specific_weather: bool = False, debug: bool = True)[source]

Build PV capacity factor profiles for a typical meteorological year (TMY) for all given buildings from the using the PVGIS weather API. The profiles will be saved in a temporary directory in the acept.acept_constants.TEMP_PATH directory as one CSV file per building.

Parameters:

  • selected_shape (geopandas.GeoDataFrame) – GeoDataFrame containing the shape of the area around the buildings.

  • buildings (geopandas.GeoDataFrame) – GeoDataFrame containing the buildings.

  • building_specific_weather (bool) – Whether to use building specific weather data. Defaults to False.

  • debug (bool) – Whether to print debug messages. Defaults to True.

acept.pv_cap_factor_profiles.get_tmy_as_input_weather_for_gsee_pv_cap(lat: float, lon: float) → pandas.DataFrame[source]

Build weather profile for a typical meteorological year (TMY) from the PVGIS API using the maximal time period of 2005 - 2020 (see PVGIS_MIN_YEAR and PVGIS_MAX_YEAR).

Parameters:

  • lat (float) – latitude of the location as a float.

  • lon (float) – longitude of the location as a float.

Returns:

A pandas DataFrame with the weather profile.

Return type:

pandas.DataFrame