NOTE: You will need to register your user account to obtain an API key https://solcast.com.au/api/register. Without an API key you will not be able to successfully obtain valid API results.
Setup System/User environment variable. Details on advanced editing StackOverflow superuser walkthrough
WinKey + R
Copy and Paste the following text to the Open: input text box for the Run Dialog
%windir%\System32\rundll32.exe sysdm.cpl,EditEnvironmentVariables
That will present this screen
Add a user or system environment variable to hold the Solcast API key. User environment variables will only be available to your particular user, system environment variables are shared for all users on the system
After you have added the environment variable you will see the key listed in the current variables
NOTE: To reference this key you will need to reopen your shell prompt to read these variables again from the system (cmd, command.com, powershell, etc)
Open a terminal prompt
- mac OS: Spotlight search for
terminal - Linux: Open
bash
nano .bash_profile
If you do not have nano it is simpler text editor than vi. Use your package manager to download and install or use vi. The preferred package manager for mac OS is Homebrew and once installed on your system you can issue similar commands to Linux apt-get and yum with the brew package manager.
Add the Solcast API Key to your user profile variables.
This how to demonstrates working with using the Solcast API to load pandas data frames and then plot different chart data. Tested and built with Python 3.6.2
This is a demonstration project that shows an example of how to use the solcast-py library to load a Pandas data frame and then display a single field graphically using MatPlotLib. A familiarity with Python is not required.
If you are not familiar with the python module pip, please refer to this PIP for Python
pip install --upgrade -r requirements.txt
Alternatively you may need to use sudo the -H instructs sudo to install dependencies to local users home directory instead of system
sudo -H pip install --upgrade -r requirements.txt
If you are receiving an error when installing the netcdf4 package on Mac such as:
ValueError: did not find netCDF version 4 headers
or
ValueError: did not find hdf5 version 4 headers
These two packages need to be installed onto your Mac with homebrew. Click this link to install homebrew here. These packages are located in the homebrew/science section. You will need to "tap" this section to access it. More information is found here.
NOTE: Installing hdf5 on mac will take a long time. Give it time.
- First as stated above you will need an API key to make valid API requests to the Solcast system.
- Second for all current library calls you will need a valid Lat/Lng coordinate in the EPSG:4326 format. If you are familiar with modern web maps you most likely have used the expected format or a decimal point that expresses a position on the Earth.
Clarification as I often forget the coordinate planes of Latitude and Longitude along with bounds.
The Solcast API expects East for Longitude and South for Latitude to be expressed as a negative numbers
Example Locations on the Globe
| Name | Latitude | Longitude |
|---|---|---|
| Sydney, Australia | -33.865143 | 151.209900 |
| Mumbai, India | 19.228825 | 72.854118 |
| Tokyo, Japan | 35.6895 | 139.69171 |
| Paris, France | 48.864716 | 2.349014 |
| Los Angeles, USA | 34.052235 | -118.243683 |
In the file main.py is a minimal set of commands to obtain the following charts. The break in the continuity between the Estimated Actuals and the Forecast predicted values on the time scale is due to that the time period falls between an in place Estimated Actual result being recorded and a next period available Forecast. Currently all period results are broken into 30 minute timestamp periods. This howto uses the period_end fields as the indexed field for the DataFrames (x - axis on the following charts)
Within this project workspace is folder solcast_frames that provides some solcast-py library helper translators to pandas DataFrames. In the main.py file these helper classes are referenced below.
# This line is only for using the matplotlib
import matplotlib.pyplot as plt
from solcast_frames.latlng import LatLng
from solcast_frames.radiationframehandler import RadiationFrameHandler
from solcast_frames.powerframehandler import PowerFrameHandler
plt.interactive(False) # Turn this off to create plotsThese following python classes are now available to use LatLng, RadiationFrameHandler, PowerFrameHandler
First you should create a location to obtain results for solcast forecasts. Example below.
location = LatLng(lat=-33.86785, lng=151.215256, name="Sydney", tag="No wild Koalas", timezone="Australia/Sydney")The LatLng helper class if no keyword arguments are supplied will set your location to (0,0) on the globe
The LatLng class contains a desc() method to give you a string output of its current details if needed i.e. print(location.desc())
To obtain and plot the current estimates and forecast from the Solcast API for PvPower use the following code
# Plot the power `pv_estimate`
# Required fields latlng valid position, capacity as integer > 0
# Adding in an optional keyword argument of an azimuth of 0
# The following optional keyword arguments are recognized by the solcast-py library
# azimuth | range: [-180 to 180] default: 0 in Southern Hemisphere, 180 in Northern Hemisphere
# tilt | range: [0 to 90] | default: 23
# install_date | format: yyyyMMdd Will be ignored if a loss_factor is supplied
# latest | [True, False] | default False
power_estimated_actuals = PowerFrameHandler.estimated_actuals(location, 5000, azimuth=0)
power_estimated_actuals.pv_estimate.plot()
# Plot the power `pv_estimate`
# Required fields latlng valid position, capacity as integer > 0
# The following optional keyword arguments are recognized by the solcast-py library
# azimuth | range: [-180 to 180] default: 0 in Southern Hemisphere, 180 in Northern Hemisphere
# tilt | range: [0 to 90] | default: 23
# install_date | format: yyyyMMdd Will be ignored if a loss_factor is supplied
# loss_factor | [0 to 1] | default 0.9
fx_solcast_power = PowerFrameHandler.forecast(location, 5000)
fx_solcast_power.pv_estimate.plot()
plt.show()Power Estimated Actuals with Forecast (pv_estimate over period_end)
To obtain and plot the current estimates and forecast from the Solcast API for Radiation use the following code
# Plot the radiation `ghi` field
# Required fields latlng valid position
# The following optional keyword arguments are recognized by the solcast-py library
# latest | [True, False] | default False
radiation_estimated_actuals = RadiationFrameHandler.estimated_actuals(location)
radiation_estimated_actuals.ghi.plot()
# Plot the radiation `ghi` field
# Required fields latlng valid position
fx_solcast_radiation = RadiationFrameHandler.forecast(location)
fx_solcast_radiation.ghi.plot()
plt.show()Radiation Estimated Actuals with Forecast (ghi over period_end)
Details of Solcast Python Client Library Solcast Python API client library
The Solcast Python library is designed as a synchronous web service and does not include the graphing or advanced data options of Pandas/MatPlotLib.
The PowerFrameHandler.py and RadiationFrameHandler.py classes in this howto under solcast_frames example convert the list value resultsets into pandas dataframes aligned and indexed by the period_end DateTime field (all datetimes are expressed in UTC timezone).
Example data results for Power DataFrames with columns available:
period pv_estimate
period_end
2017-10-02 19:30:00+00:00 00:30:00 0.000000
2017-10-02 20:00:00+00:00 00:30:00 27.628064
2017-10-02 20:30:00+00:00 00:30:00 243.625359
Example data results for Radiation DataFrames with columns available:
cloud_opacity dhi dni ebh ghi period
period_end
2017-10-02 13:00:00+00:00 26 0 0 0 0 00:30:00
2017-10-02 12:30:00+00:00 31 0 0 0 0 00:30:00
2017-10-02 12:00:00+00:00 0 0 0 0 0 00:30:00
air_temp azimuth cloud_opacity dhi dni dni10 dni90 ebh ghi ghi10 ghi90 period zenith
period_end
2017-10-02 19:30:00+00:00 17 -97 17 1 0 0 0 0 1 1 1 00:30:00 94
2017-10-02 20:00:00+00:00 17 -93 33 13 10 4 27 2 15 12 19 00:30:00 88
2017-10-02 20:30:00+00:00 17 -88 7 45 196 43 275 33 79 57 86 00:30:00 82
Integrating with pvlib
In the file main.py is a minimal set of commands to obtain the following charts. The pvlib library is a powerful open source tool that is used in computing and forecasting photovoltaic solar cell arrays. There are numerous options and details that can be configured, the following example compares pvlib forecasts with Solcast forecasts and how to obtain datasets that can be compared and graphed.
# This line is only for using the matplotlib
import matplotlib.pyplot as plt
from solcast_frames.latlng import LatLng
from solcast_frames.radiationframehandler import RadiationFrameHandler
import timeit
import pandas as pd
import datetime
# import pvlib forecast models
from pvlib.forecast import GFS, NAM, NDFD, HRRR, RAP
plt.interactive(False) # Turn this off to create plots
#
# Following code is from http://pvlib-python.readthedocs.io/en/latest/forecasts.html
#
# Changing the location pvlib default model uses Tucson for examples
location = LatLng(lat=32.2, lng=-110.9, name="Tucson", tag="Cactus Land", timezone="US/Arizona")
print(location.desc())
start_time = timeit.default_timer()
radiationForecast = RadiationFrameHandler.forecast(location)
elapsed = timeit.default_timer() - start_time
print("Solcast Radiation Forecast Location: %s Time: %s (seconds)" % (location.name, '%.6f' % elapsed))
# specify time range with timezone
start = pd.Timestamp(datetime.date.today(), tz=location.timezone)
end = start + pd.Timedelta(days=7)
start_time = timeit.default_timer()
# fx is a common abbreviation for forecast
fx_model = GFS() # From Forecast models http://pvlib-python.readthedocs.io/en/latest/api.html#forecast-models
fx_data = fx_model.get_processed_data(location.lat, location.lng, start, end)
elapsed = timeit.default_timer() - start_time
print("pvlib (GFS) Radiation Forecast Location: %s Time: %s (seconds)" % (location.name, '%.6f' % elapsed))
plt.plot(fx_data.ghi, label="ghi - PVLIB (GFS)")
plt.plot(radiationForecast.ghi, label="ghi - Solcast", linestyle='dashdot')
plt.plot(fx_data.dhi, label="dhi - PVLIB (GFS)")
plt.plot(radiationForecast.dhi, label="dhi - Solcast", linestyle='dashdot')
plt.plot(fx_data.dni, label="dni - PVLIB (GFS)")
plt.plot(radiationForecast.dni, label="dni - Solcast", linestyle='dashdot')
plt.legend()
plt.show()Radiation Forecast pvlib vs Solcast ghi dhi, dni
