solartoolbox 0.4.0

A research toolbox for solar analysis

SolarToolbox

solartoolbox is a package containing tools for dealing with analysis of solar energy data. Its specific focus is on signal processing approaches and addressing variability from a spatiotemporal perspective. Tools here might be useful for dealing with distributed data sets, or performing analyses that rely on a spatially distributed set of measurements.

Installation

The package can be most easily installed via PyPi with the following command:

pip install solartoolbox

Structure of the Library

The codes are organized into a few subpackages and several function libraries. The subpackages are meant to contain tools that are related to a specific task.

Packages

dataio
A package with codes for accessing a couple of distributed irradiance datasets that I've worked with and for converting them to a common format for use with the other codes. Current datasets:

• HOPE and HOPE-MELPITZ
• NRCAN High Resolution Datasets

Some of these tools are meant to be used via the command line and some via function call. This area of the package may be in need of some cleanup to improve consistency.

visualization
Tools for visualizing various types of data or constructing common plots that might be useful for these analyses. Right now this only contains a function for decorating the frequency axis of plots with common timescales. This is an area that could use some expansion in the future.

demos
Data and demonstration codes (including as jupyter notebooks) that demonstrate the functionality of the package. An explanation the included data is warranted.

• Anonymized Plant Combiner Data
  • Anonymized combiner time series data from ~20 MW (sample_plant_1.h5) and ~30 MW (sample_plant_2.h5) solar plants in the United States.
  • Field utm actually contains the UTM-like (East/North) centroid positions of individual combiners, anonymized with an arbitrary offset. Columns are E and N and units are meters.
  • Fields data_a, data_b through data_e contain the combiner current measurements for five hours of operation throughout the year with known high variability. Sampling period is 10 seconds. The absolute time stamps are arbitrary and do not correspond to any real time. Data are normalized for anonymization.
  • Combiner ids are used as column names for the data time series and correspond to the matching index of utm
  • See cmv_demo.ipynb and field_demo.ipynb for examples using this data.
• HOPE Melpitz Campaign Data
  • Subset of data from the HOPE-Melpitz campaign of time series from 50 distributed irradiance sensors. For details on this data, refer to: Macke et al. (2017) and Dataset Website
    • hope_melpitz_1s contains data sampled at 1s time resolution.
      • Covers a single hour of data (9:15 - 10:15 UTC on Sept 8, 2013).
    • hope_melpitz_10s contains data sampled at 10s time resolution, acquired by temporally averaging time series data from the original dataset.
      • Covers 4 full days, from Sept 8 - Sept 11, 2013.
  • In both cases, data were first postprocessed using only removal of nulls (-9999) and linear interpolation to fill gaps left by the nulls, with a maximum interpolation window of 5s. See dataio.hope_campaign for details on those postprocessing steps.
  • Fields are latlon, utm, and data.
  • Numerical sensor IDs match those from the original dataset, and original timestamps are preserved in the data field. All timestamps are UTC.
  • See dataio\hope_campaign.py for details on the original dataset.
  • See signalproc_demo.py for examples using this data.

Function libraries in solartoolbox (root level)

cmv
Functions for computing the cloud motion vector from a distributed irradiance dataset. Two methods from literature are available:

• Jamaly and Kleissl (2018)
• Gagne et al. (2018)

signalproc
Functions for performing signal processing on time series. The two primary parts of this are computations of averaged transfer functions between an input and output signal (e.g. calculation of coherence) and code for computing the Cloud Advection Model (CAM).

spatial
Functions for dealing with spatially distributed locations. This includes conversion between lat/lon and UTM coordinates, along with some vector operations needed to deal with other parts of the analysis. Examples include computing vectors between all locations in a distributed location set and projecting those vectors parallel/perpendicular to a cloud motion direction.

stats
A set of functions for calculating various quantities on datasets.

• Common statistical error metrics (RMSE, MBE, MAE, etc)
• Lagging cross-correlation via correlate()
• Variability metrics (Variability Score, Variability Index, DARR)
• Quantile summary (e.g. for synthesizing a clear day from the 90th percentile of each hour of the day over a 30 day window)

field Functions for predicting the position of field components on the basis of cloud motion.

Common format for H5 files used for Data Storage

I've tried to format the multisite time series measurements in a way that's conveinent for loading the files and working with the data. This came about from my initial work analyzing the HOPE Campaign, which used 100 individual point measurements of GHI scattered through a region near Jülich, Germany.

All data is collected into a single H5 file containing multiple fields. I use pandas and specifically pandas.read_hdf() for getting the data into python.

• latlon: The latitude/longitude of the individual measurement sites
• utm: The UTM coordinates of the individual measurement sites
• data: Global Horizontal Irradiance
• data_tilt: Global Tilted Irradiance (if available)

Location Data

Data about the location of each individual site is stored in the H5 file. Two possible keys are used depending on the projection. Both are available when possible. The key latlon represents the site in a latitude coordinate system. The key utm will contain the positions using UTM (or similar) projection that attempts to place the layout into a rectilinear coordinates. Upon use of pandas.read_hdf() the data will be brought into a DataFrame object.

• The index of the DataFrame is the site id. The HOPE datasets use an integer for the id, while NRCAN uses a string.
• Columns are labelled lat and lon and contain the lat and lon in degrees for each of the distributed sensors (or E, N in the case of utm).

Irradiance Data

Measurements consist of the individual sensor time series with a shared time index. Upon use of pandas.read_hdf() the data will be brought into a DataFrame object. Each individual sensor has its own column.

• Index of the DataFrame is the timestamp referenced to a timezone
• Columns contain the time series for each individual sensor, and are keyed by the site id (HOPE - integer, NRCAN - string).

Contributing

This project is happy to accept contributions and hear from users. The best way to interact right now is to open an issue on GitHub. This is the best way to ask a question, make a suggestion about a feature or describe a bug that you've encountered.

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for full details.

History

Solartoolbox began as a library of tools developed to support my own research activities on solar energy. It was always shared publicly to encourage use by interested parties and make a small contribution to open science and promoting reproducibility in the field. As a result, most initial releases lacked full documentation and the structure of the library underwent some significant changes as they adapted to my own changing needs from project to project. Some artifacts of that history may still be present in the code and certainly are reflected by the commit history.

Beginning with Version 0.3.1 and the introduction of the field analysis package, I began to see the potential for broader interest in the tools which may lead to a greater need to accommodate other users. As such, I began to improve documentation and testing with that release and hope to reach a more stable and consistent structure for the library. The expectation is that the packages cmv and field will be the most broadly useful to the research community and have been the focus of additional testing, documentation and tutorial development.

See Changelog.md for more details.

Relationship with other packages

This package is not meant to replace or compete with well established packages like pvlib or pvanalytics. Instead, the focus is to serve as a complement to those packages, especially in offering functionality that would otherwise be out of scope for their mission. When overlap occurs, functionality developed here will be contributed to those more mature packages if they are deemed in scope or suitable by the maintainers of those packages.

For example, the pvlib-python port of the Wavelet Variability Model was initially developed as part of this package, but was later contributed to pvlib-python in the scaling module thereof.

Author

Joe Ranalli
Associate Professor of Engineering
Penn State Hazleton
[e-mail]
https://jranalli.github.io/