PV PLR analysis
Project description
pvplr: Performance Loss Rate Analysis Pipeline
The pipeline contained in this package provides tools used in the Solar Durability and Lifetime Extension Center (SDLE) for the analysis of Performance Loss Rates (PLR) in real world photovoltaic systems. Functions included allow for data cleaning, feature correction, power predictive modeling, PLR determination, and uncertainty bootstrapping through various methods doi:10.1109/PVSC40753.2019.8980928. The vignette "Pipeline Walkthrough" gives an explicit run through of typical package usage.
This material is based upon work supported by the U.S Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE-0008172. This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University.
Authors
Alan Curran, Suraj Kumar, Raymond Wieser, Ben Pierce, Tyler Burleyson, William Oltjen, Sascha Lindig, David Moser, Roger French, Solar Durability and Lifetime Extension research center
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file pvplr-2024.7.29.tar.gz
.
File metadata
- Download URL: pvplr-2024.7.29.tar.gz
- Upload date:
- Size: 15.8 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.12.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | a57d8544821f9c22b2795ee891477b51182f0099b7c76ca35d20ab8142dec1a7 |
|
MD5 | f7d3986eb50ac28e1409795e4dfecccc |
|
BLAKE2b-256 | 23dae35d8844dd1a5e08752f1135909745abde9ff0d2bcdcf7679b4e86332475 |
File details
Details for the file pvplr-2024.7.29-py3-none-any.whl
.
File metadata
- Download URL: pvplr-2024.7.29-py3-none-any.whl
- Upload date:
- Size: 15.7 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.12.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3a5d8cdee5537bc2fbd25a1d2442b529bbc97138a75f98e90b7b83cad7ec520b |
|
MD5 | 94b71fa8cc3fdb6bce2849d6827fb0af |
|
BLAKE2b-256 | 06971b9b11dbb940e414d899eed3417b4f836cee436e0496fdd06c1af6450d17 |