gsee -- global solar energy estimator

gsee is a lightweight library designed for speed and ease of use. Renewables.ninja PV data is generated with gsee.

Requirements

Only tested on Python 3.

Required libraries:

• pyephem
• numpy
• pandas

Installation

The recommended way to get numpy and pandas is to use the Anaconda Python distribution, then install gsee:

pip install -e git+https://github.com/renewables-ninja/gsee.git#egg=gsee

Background

This is a collection of tools to estimate output from solar power plants.

trigon contains functions to calculate irradiance on an inclined plane. brl_model is an implementation of a method to derive the diffuse fraction of irradiance, based on Ridley et al. (2010). pv is a model to derive power output from solar irradiance.

Examples

Aperture irradiance on a panel with 2-axis tracking

locations = (22.78, 5.51)
plane_irradiance = gsee.trigon.aperture_irradiance(data['direct_horizontal'],
                                                   data['diffuse_horizontal'],
                                                   location, tracking=2)

Plant models

Currently available: pv

• pv: based on published PV module performance data, see pv.py for details

A model can be imported like this: import gsee.pv

A 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.

Citation

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

License

BSD-3-Clause