GSEE
is a small solar energy simulation library designed for speed and ease of use. Renewables.ninja PV data is generated with GSEE
.
Works only with Python 3. Required libraries:
Simply install with pip
:
pip install gsee
The recommended way to get the required numpy and pandas libraries is to use the Anaconda Python distribution.
The following submodules are available:
pv
: electric output from PV a paneltrigon
: functions to calculate irradiance on an inclined planebrl_model
: an implementation of the BRL model, a method to derive the diffuse fraction of irradiance, based on Ridley et al. (2010)
A model can be imported like this: import gsee.pv
A plant simulation model implements a model class (e.g. PVPlant
) with the relevant settings, and a run_model()
function that take time series data (a pandas Series) and runs a default instance of the model class, but can also take a model
argument to specify a custom-configured model instance.
In this example, data
must be a pandas.DataFrame with columns global_horizontal
(in kW/m2), diffuse_fraction
, and optionally a temperature
column for ambient air temperature (in degrees Celsius).
result = gsee.pv.run_model(
data,
coords=(22.78, 5.51), # Latitude and longitude
tilt=30, # 30 degrees tilt angle
azim=180, # facing towards equator,
tracking=0, # fixed - no tracking
capacity=1, # 1kW
)
location = (22.78, 5.51)
plane_irradiance = gsee.trigon.aperture_irradiance(
data['direct_horizontal'], data['diffuse_horizontal'],
location, tracking=2
)
To install the latest development version directly from GitHub:
pip install -e git+https://github.com/renewables-ninja/gsee.git#egg=gsee
Contact Stefan Pfenninger for questions about GSEE
. GSEE
is also a component of the Renewables.ninja project, developed by Stefan Pfenninger and Iain Staffell. Use the contact page there if you want more information about Renewables.ninja.
If you use GSEE
or code derived from it in academic work, please cite:
Stefan Pfenninger and Iain Staffell (2016). Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy 114, pp. 1251-1265. doi: 10.1016/j.energy.2016.08.060
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