pvtools 🕶
A set of python
🐍 tools to process and model PV
I put together a set of tools used to clean data and model bifacial systems using pvfactors, pvlib and bifacialvf
Install
As of 02/2019: bifacialvf has not yet merged this PR, the simulate function cannot take arbitrary metereological data on the form of pandas DataFrames. So we are force to install a custom fork of bifacialvf from here. This is way, a formal release of capetools to PyPI is not possible rght now, so we have to install by cloning from github.
The recommended method is to install capetools on a conda envirnment. Ideally create a conda env with python 3.6 and then clone and install using pip.
conda create --name=your_env_name python=3.7
git clone https://github.com/tcapelle/capetools/
Now you can install from PyPi:
pip install pvtools
or on editable mode, git clone this repo, and from within the repo install using:
pip install -e .
Getting started 💪
from pvtools.imports import *
from pvtools.utils.missing import *
from pvtools.utils.tmy import read_pvgis
from pvtools.modelling.mypvfactors import *
from pvtools.modelling.mybifacialvf import *PATH = Path.cwd().parent/'data'
fname = PATH/'pvgis_tmy_chambery.csv'We will ingest a PVGIS downloaded file for Chambery
gps_data, months, tmy_data = read_pvgis(fname)tmy_data.head().dataframe tbody tr th {
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| temp | humidity | ghi | dni | dhi | infra | ws | wd | pressure | |
|---|---|---|---|---|---|---|---|---|---|
| time(UTC) | |||||||||
| 2012-01-01 00:00:00 | 2.87 | 88.28 | 0.0 | -0.0 | 0.0 | 259.63 | 1.33 | 170.0 | 99504.0 |
| 2012-01-01 01:00:00 | 3.59 | 90.07 | 0.0 | -0.0 | 0.0 | 268.30 | 1.39 | 166.0 | 99508.0 |
| 2012-01-01 02:00:00 | 4.32 | 91.86 | 0.0 | -0.0 | 0.0 | 276.97 | 1.45 | 162.0 | 99511.0 |
| 2012-01-01 03:00:00 | 5.04 | 93.64 | 0.0 | -0.0 | 0.0 | 285.64 | 1.51 | 167.0 | 99517.0 |
| 2012-01-01 04:00:00 | 5.76 | 95.43 | 0.0 | -0.0 | 0.0 | 294.32 | 1.57 | 171.0 | 99524.0 |
We can quickly look at missing data:
plot_missing(tmy_data)
as expected, no missing data !
Simulations
pvfactors
params = system_def(n_pvrows=3); params{'n_pvrows': 3,
'pvrow_height': 1.6218180900789148,
'pvrow_width': 2.02,
'tracking': False,
'axis_azimuth': 0,
'surface_tilt': 38,
'surface_azimuth': 180,
'albedo': 0.4,
'gcr': 0.5,
'rho_front_pvrow': 0.075,
'rho_back_pvrow': 0.075,
'cut': {0: {'front': 1, 'back': 7},
1: {'front': 1, 'back': 7},
2: {'front': 1, 'back': 7}}}
data = get_data(fname, params)pvarray = run_pvfactors_simulation(data, params)ax = plot_idx(pvarray)
ax.set_xlim(-2, 10)(-2, 10)
res_pvfactors = individual_report(pvarray, index=data.index)res_pvfactors.head().dataframe tbody tr th {
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| qinc_0 | qinc_1 | qinc_2 | qinc_3 | qinc_4 | qinc_5 | qinc_6 | qinc_front | qinc_back | |
|---|---|---|---|---|---|---|---|---|---|
| time(UTC) | |||||||||
| 2019-01-01 00:00:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2019-01-01 01:00:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2019-01-01 02:00:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2019-01-01 03:00:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2019-01-01 04:00:00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
res_pvfactors['21 June 2019'].plot();
bifacialvf
res_bifacialvf = run_bifacialvf_simulation(data) 1%| | 84/8760 [00:00<00:22, 387.11it/s]
*********
Running Simulation for TMY3: Chambery
Location: Chambery
Lat: 45.637001 Long: 5.881 Tz -1.0
Parameters: beta: 0 Sazm: 180 Height: 0.5 rtr separation: 8.0 Row type: interior Albedo: 0.4
Saving into output.csv
100%|██████████| 8760/8760 [00:28<00:00, 307.76it/s]
Finished
res_bifacialvf['21 June 2019'].plot();
Coontributing 👇
Read nbdev documentation please.
