PySoda

PySoda is an irradiance-based synthetic Solar Data generation tool to generate realistic sub-minute solar photovoltaic (PV) power time series. Soda emulates the weather pattern for a certain geographical location using 30-min averaged irradiance and cloud type information from the National Solar Radiation Database (NSRDB)

Installation (Python)

Use Git to install pysoda in your current python environment

git clone https://github.com/Ignacio-Losada/SoDa.git
cd SoDa
pip3 install -r requirements.txt
python3 setup.py develop

Installation with Git (Conda)

Use Git to install a conda environment for pysoda

git clone https://github.com/Ignacio-Losada/SoDa.git
cd SoDa
conda env create -f environment.yml
python3 setup.py develop

Basic Usage

Once PySoda is installed, solar time series can be generated as follows.

First, you'll need to create an object with the coordinates of interest

import soda
lat = 33.9533
lon = -117.3962

site = soda.SolarSite(lat,lon)

Then, obtain the closest NSRDB point to the specified coordinates and retrieve the neccesary irradiance values. We recommend retrieving the 30-min average NSRDB irradiance data to obtain the best results

year = "2015"
leap_year = False
interval = "30"
utc = False
df = site.get_nsrdb_data(year,leap_year,interval,utc)

You'll also need to specify the solar panel configuration and obtain the 30-min averaged solar time series

clearsky = False
capacity = 1
DC_AC_ratio = 1.2
tilt = 33
azimuth = 180
inv_eff = 96
losses = 15
array_type = 0

pwr = site.generate_solar_power_from_nsrdb(clearsky,capacity,DC_AC_ratio,tilt,azimuth,inv_eff,losses,array_type)

Finally, you can generate stochastic solar time series for a given date at different time resolutions, e.g. 5 seconds

date = "2015-01-10"
resolution = "5S"
solar_data = site.generate_high_resolution_power_data(resolution, date)

This function will return a pandas dataframe with the solar generation. And we compare our results

Citing SoDa

If you find SoDa useful in your work, we kindly request that you cite the following publication:

@inproceedings{,
  author = {Ignacio Losada Carreno and Raksha Ramakrishna and Anna Scaglione and Daniel Arnold and Ciaran Roberts and Sy-Toan Ngo and Sean Peisert and David Pinney},
  title = {SoDa: An Irradiance-Based Synthetic Solar DataGeneration Tool},
  booktitle = {2020 IEEE International Conference on Smart Grid Communications (SmartGridComm)},
  year = {2020},
  month = {November},
  pages = {1-6},
  doi = {}
}

License