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Solar Data Tools is an open-source Python library for analyzing PV power (and irradiance) time-series data. It was developed to enable analysis of unlabeled PV data, i.e. with no model, no meteorological data, and no performance index required, by taking a statistical signal processing approach in the algorithms used in the package’s main data processing pipeline. Solar Data Tools empowers PV system fleet owners or operators to analyze system performance a hundred times faster even when they only have access to the most basic data stream—power output of the system.
Solar Data Tools provides methods for data I/O, cleaning, filtering, plotting, and analysis. These methods are largely automated and require little to no input from the user regardless of system type—from utility tracking systems to multi-pitch rooftop systems. Head over to our Getting Started pages in our documentation for a demo!
As of April 2026, we have a new webinar posted to YouTube! Check it out here:
You can also check the notebooks folder in this repo for more examples.
This work is supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office Award Number 38529.
Install & Setup
Recommended: Install with pip
In a fresh Python virtual environment, simply run:
$ pip install solar-data-tools
or if you would like to use MOSEK, install the optional dependency as well:
$ pip install "solar-data-tools[mosek]"
Install with conda
[!WARNING]
solar-data-toolsis now available on conda-forge! You can specify the channel using the-cflag as shown in the examples below. The use of the slacgismo channel is deprecated and packages on that channel will not be up-to-date with the latest releases.
Creating the environment and directly installing the package and its dependencies from the appropriate conda channels:
$ conda create -n pvi-user solar-data-tools -c conda-forge
Starting the environment:
$ conda activate pvi-user
Stopping the environment:
$ conda deactivate
Or alternatively install the package in an already existing environment:
$ conda install solar-data-tools -c conda-forge
Solvers
CLARABEL
By default, the CLARABEL solver is used to solve the signal decomposition problems. CLARABEL (as well as other solvers) is compatible with OSD, the modeling language used to solve signal decomposition problems in Solar Data Tools. Both are open source and are dependencies of Solar Data Tools.
MOSEK
MOSEK is a commercial software package. Since it is more stable and offers faster solve times,
we provide continuing support for it (with signal decomposition problem formulations using CVXPY). However,
you will still need to obtain a license. If installing with pip, you can install the optional MOSEK dependency by running
pip install "solar-data-tools[mosek]".
If installing from conda, you will have to manually install MOSEK if you desire to use it as
conda does not support optional dependencies like pip.
More information about MOSEK and how to obtain a license is available here:
Usage
Users will primarily interact with this software through the DataHandler class. By default, Solar Data
Tools uses CLARABEL as the solver all signal decomposition problems. If you would like
to specify another solver (such as MOSEK), just pass the keyword argument solver to DataHandler.pipeline with the solver of choice.
import pandas as pd
from solardatatools import DataHandler
pv_system_data = pd.read_csv('path/to/your/data.csv', index_col=0, parse_dates=True)
dh = DataHandler(pv_system_data)
dh.run_pipeline(power_col='dc_power')
If everything is working correctly, you should see a run summary like the following
total time: 25.99 seconds
--------------------------------
Breakdown
--------------------------------
Preprocessing 6.76s
Cleaning 0.41s
Filtering/Summarizing 18.83s
Data quality 0.21s
Clear day detect 0.44s
Clipping detect 15.51s
Capacity change detect 2.67s
You can also find more in-depth tutorials and guides in our documentation.
Contributing
We welcome contributions of any form! Please see our Contribution Guidelines for more information.
Citing Solar Data Tools
If you use Solar Data Tools in your research, please cite:
Recommended citation
Sara A. Miskovich, Bennet E. Meyers, et al., "Solar Data Tools: a Python library for automated analysis of unlabeled PV data," Journal of Open Source Software, 10(110), 8478, 2025, doi: 10.21105/joss.08478
Citing technical details (e.g., SDT algorithms)
Bennet E. Meyers, PVInsight (Final Technical Report), SLAC Report SLAC-R-1155, 2021, doi: 10.2172/1897181
Bennet E. Meyers, Elpiniki Apostolaki-Iosifidou and Laura Schelhas, "Solar Data Tools: Automatic Solar Data Processing Pipeline," 2020 47th IEEE Photovoltaic Specialists Conference (PVSC), Calgary, AB, Canada, 2020, pp. 0655-0656, doi: 10.1109/PVSC45281.2020.9300847.
Citing a specific version
You can also cite the DOI corresponding to the specific version of Solar Data Tools that you used. Solar Data Tools DOIs are listed at here.
Versioning
We use Semantic Versioning for versioning. For the versions available, see the tags on this repository.
Authors
- Bennet Meyers - Initial work and Main research work - Bennet Meyers GitHub
See also the list of contributors who participated in this project.
Copyright Notice
COPYRIGHT © 2025 Alliance for Sustainable Energy, LLC and SLAC National Accelerator Laboratory. All rights reserved. This work is supported in part by the U.S. Department of Energy, Office of Basic Energy Sciences under contract DE-AC02-76SF00515.
Usage Restrictions
Neither the name of the Leland Stanford Junior University, SLAC National Accelerator Laboratory, U.S. Department of Energy nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
1 maintainer
Release files for solar-data-tools 2.1.5
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Total release size: 154.0 MB
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