This example shows you how to download ERA5 weather data from the Climate Data Store (CDS) and store it locally. Furthermore, it shows how to convert the weather data to the format needed by the pvlib and windpowerlib.
In order to download ERA5 weather data you need an account at the CDS. Furthermore, you need to install the cdsapi package. See here for installation details.
When downloading the data using the API your request gets queued and may take a while to be completed. All actual calls of the data download are therefore commented to avoid unintended download. Instead, an example netcdf file is provided.
In [1]:
from feedinlib import era5
In [2]:
latitude = 52.47
longitude = 13.30
Besides a location you have to specify a time period for which you would like to download the data as well as the weather variables you need. The feedinlib provides predefined sets of variables that are needed to use the pvlib and windpowerlib. These can be applied by setting the variable parameter to "pvlib" or "windpowerlib", as shown below. If you want to download data for both libraries you can set variable to "feedinlib".
Concerning the start and end date, keep in mind that all timestamps in the feedinlib are in UTC. So if you later on want to convert the data to a different time zone, the data may not cover the whole period you intended to download. To avoid this set your start date to one day before the start of your required period if you are East of the zero meridian or your end date to one day after your required period ends if you are West of the zero meridian.
In [3]:
# set start and end date (end date will be included
# in the time period for which data is downloaded)
start_date, end_date = '2017-01-01', '2017-12-31'
# set variable set to download
variable = "pvlib"
If you want to store the downloaded data, which is recommended as download may take a while, you may provide a filename (including path) to save data to.
In [4]:
target_file = 'ERA5_pvlib_2017.nc'
Now we can retrieve the data:
# get windpowerlib data for specified location
ds = era5.get_era5_data_from_datespan_and_position(
variable=variable,
start_date=start_date, end_date=end_date,
latitude=latitude, longitude=longitude,
target_file=target_file)
2020-01-12 20:53:56,465 INFO Welcome to the CDS
2020-01-12 20:53:56,469 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels
2020-01-12 20:53:57,023 INFO Request is queued
2020-01-12 20:53:58,085 INFO Request is running
2020-01-12 21:48:24,341 INFO Request is completed
2020-01-12 21:48:24,344 INFO Downloading request for 5 variables to ERA5_pvlib_2017.nc
2020-01-12 21:48:24,346 INFO Downloading http://136.156.132.153/cache-compute-0002/cache/data7/adaptor.mars.internal-1578858837.3774962-24514-9-8081d664-0a1e-48b9-951c-bc9b8e2caa44.nc to ERA5_pvlib_2017.nc (121.9K)
2020-01-12 21:48:24,653 INFO Download rate 400.6K/s
In [5]:
latitude = [52.3, 52.7] # [latitude south, latitude north]
longitude = [13.1, 13.6] # [longitude west, longitude east]
In [6]:
target_file = 'ERA5_example_data.nc'
# get pvlib data for specified area
ds_berlin = era5.get_era5_data_from_datespan_and_position(
variable=variable,
start_date=start_date, end_date=end_date,
latitude=latitude, longitude=longitude,
target_file=target_file)
2020-01-12 22:55:35,301 INFO Download rate 1.6M/s
2020-01-12 22:03:08,085 INFO Welcome to the CDS
2020-01-12 22:03:08,086 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels
2020-01-12 22:03:08,756 INFO Request is queued
2020-01-12 22:03:09,809 INFO Request is running
2020-01-12 22:55:34,863 INFO Request is completed
2020-01-12 22:55:34,864 INFO Downloading request for 5 variables to ERA5_example_data.nc
2020-01-12 22:55:34,864 INFO Downloading http://136.156.132.235/cache-compute-0006/cache/data5/adaptor.mars.internal-1578862989.052999-21409-23-831562a8-e0b2-4b19-8463-e14931a3f630.nc to ERA5_example_data.nc (720.7K)
If you want weather data for the whole world, you may leave latitude and longitude unspecified.
# get feedinlib data (includes pvlib and windpowerlib data)
# for the whole world
ds = era5.get_era5_data_from_datespan_and_position(
variable="feedinlib",
start_date=start_date, end_date=end_date,
target_file=target_file)
In [7]:
era5_netcdf_filename = 'ERA5_example_data.nc'
In [8]:
# reimport downloaded data
import xarray as xr
ds = xr.open_dataset(era5_netcdf_filename)
ds
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Let's first plot the downloaded weather data points on a map.
In [9]:
# get all weather data points in dataset
from shapely.geometry import Point
import geopandas as gpd
points = []
for x in ds.longitude:
for y in ds.latitude:
points.append(Point(x, y))
points_df = gpd.GeoDataFrame({'geometry': points})
# read provided shape file
region_shape = gpd.read_file('berlin_shape.geojson')
# plot weather data points on map
base = region_shape.plot(color='white', edgecolor='black')
points_df.plot(ax=base, marker='o', color='red', markersize=5);
Now let's convert the weather data to the pvlib format.
With the area parameter you can specify wether you want to retrieve weather dataframes for a single location, a region within the downloaded region or the whole downloaded region.
In case area is not a single location, the index of the resulting dataframe will be a multiindex with levels (time, latitude, longitude). Be aware that in order to use it for pvlib or windpowerlib calculations you need to select a single location.
In [10]:
# for single location (as list of longitude and latitude)
single_location = [13.2, 52.4]
pvlib_df = era5.weather_df_from_era5(
era5_netcdf_filename=era5_netcdf_filename,
lib='pvlib',
area=single_location)
pvlib_df.head()
Out[10]:
In [11]:
# for whole region
era5.weather_df_from_era5(
era5_netcdf_filename=era5_netcdf_filename,
lib='pvlib').head()
Out[11]:
In [12]:
# specify rectangular area
area = [(13.2, 13.7), (52.4, 52.8)]
era5.weather_df_from_era5(
era5_netcdf_filename=era5_netcdf_filename,
lib='pvlib',
area=area).head()
Out[12]:
In [13]:
# specify area giving a Polygon
from shapely.geometry import Polygon
lat_point_list = [52.3, 52.3, 52.65]
lon_point_list = [13.0, 13.4, 13.4]
area = Polygon(zip(lon_point_list, lat_point_list))
era5.weather_df_from_era5(
era5_netcdf_filename=era5_netcdf_filename,
lib='pvlib',
area=area).head()
Out[13]:
In [16]:
# export to csv
pvlib_df.to_csv('pvlib_df_ERA5.csv')
The conversion to the windpowerlib format is analog to the pvlib conversion.
In [17]:
# for whole region
windpowerlib_df = era5.weather_df_from_era5(
era5_netcdf_filename=era5_netcdf_filename,
lib='windpowerlib',
area=single_location)
windpowerlib_df.head()
Out[17]:
Furthermore, it is possible to specify a start and end date to retrieve data for. They must be provided as something that can be converted to a timestamp, i.e. '2013-07-02'.
In [21]:
# get weather data in pvlib format for July
start = '2017-07-01'
end = '2017-07-31'
era5.weather_df_from_era5(
era5_netcdf_filename=era5_netcdf_filename,
lib='pvlib',
area=single_location,
start=start,
end=end).head()
Out[21]:
The following shows you in short how to use the weather data for feed-in calculations and mainly serves as a test wether the conversion works correctly. More detailed explanation on feed-in calculations using the feedinlib can be found in the notebooks run_pvlib_model.ipynb and run_windpowerlib_turbine_model.ipynb.
In [19]:
from feedinlib import Photovoltaic
system_data = {
'module_name': 'Advent_Solar_Ventura_210___2008_',
'inverter_name': 'ABB__MICRO_0_25_I_OUTD_US_208__208V_',
'azimuth': 180,
'tilt': 30,
'albedo': 0.2}
pv_system = Photovoltaic(**system_data)
feedin = pv_system.feedin(
weather=pvlib_df,
location=(52.4, 13.2))
feedin.plot()
Out[19]:
In [20]:
from feedinlib import WindPowerPlant
turbine_data = {
'turbine_type': 'E-101/3050',
'hub_height': 135
}
wind_turbine = WindPowerPlant(**turbine_data)
feedin = wind_turbine.feedin(
weather=windpowerlib_df)
feedin.plot()
Out[20]:
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