1.1 Getting Data: Historic Solar Radiation

In this example we are going to fetch GHI and Clearsky GHI for 5 days in the past for the Sydney Opera House.

In [1]:

from solcast import historic

from solcast import historic

To test the API you can use unmetered locations, in the sdk:

In [2]:

from solcast.unmetered_locations import UNMETERED_LOCATIONS
sydney = UNMETERED_LOCATIONS['Sydney Opera House']

from solcast.unmetered_locations import UNMETERED_LOCATIONS sydney = UNMETERED_LOCATIONS['Sydney Opera House']

We are going to use the historic.radiation_and_weather API:

In [3]:

help(historic.radiation_and_weather)

help(historic.radiation_and_weather)

Help on function radiation_and_weather in module solcast.historic:

radiation_and_weather(latitude: float, longitude: float, start: str, end: str = None, duration: str = None, **kwargs) -> solcast.api.Response
    Get historical irradiance and weather estimated actuals for up to 31 days of data
    at a time for a requested location, derived from satellite (clouds and irradiance
    over non-polar continental areas) and numerical weather models (other data).
    Data is available from 2007-01-01T00:00Z up to real time estimated actuals.
    
    Args:
        latitude: in decimal degrees, between -90 and 90, north is positive
        longitude: in decimal degrees, between -180 and 180, east is positive
        start: datetime-like, first day of the requested period
        end: optional, dateime-like, last day of the requested period
        duration: optional, ISO_8601 compliant duration for the historic data.
            Must be within 31 days of the start_date.
    
    See https://docs.solcast.com.au/ for full list of parameters.

In [5]:

res = historic.radiation_and_weather(
    latitude=sydney['latitude'], 
    longitude=sydney['longitude'],
    output_parameters='clearsky_ghi,ghi',
    start='2022-06-01T14:45:00.000Z',
    duration='P5D',
    period='PT5M'
)

res = historic.radiation_and_weather( latitude=sydney['latitude'], longitude=sydney['longitude'], output_parameters='clearsky_ghi,ghi', start='2022-06-01T14:45:00.000Z', duration='P5D', period='PT5M' )

we can also use the same function with the end parameter instead of the duration, the results will be equivalent:

In [6]:

res2 = historic.radiation_and_weather(
    latitude=sydney['latitude'], 
    longitude=sydney['longitude'],
    output_parameters='clearsky_ghi,ghi',
    start='2022-06-01T14:45:00.000Z',
    end='2022-06-06T14:45:00.000Z',
    period='PT5M'
)

assert (res.to_pandas() == res2.to_pandas()).all().all()

res2 = historic.radiation_and_weather( latitude=sydney['latitude'], longitude=sydney['longitude'], output_parameters='clearsky_ghi,ghi', start='2022-06-01T14:45:00.000Z', end='2022-06-06T14:45:00.000Z', period='PT5M' ) assert (res.to_pandas() == res2.to_pandas()).all().all()

Let's transform the result in a DataFrame with the res.to_pandas() and plot the time series with matplotlib. Since we are retrieving data for Sydney, we can cast the times to the local timezone too:

In [10]:

df = res.to_pandas()
df.index = df.index.tz_convert('Australia/Sydney')
df.plot()

df = res.to_pandas() df.index = df.index.tz_convert('Australia/Sydney') df.plot()

Out[10]:

<Axes: xlabel='period_end'>

In [ ]: