Spatial analysis tools for solar energy research

solarspatialtools is a python package containing implementations of various spatial algorithms for solar energy data. While excellent open source solar energy packages already exist (e.g. pvlib-python and pvanalytics), the complexity of some high-level analyses found in the academic literature makes them as a poor fit for the scope of existing packages. This package fills that gap by implementing techniques that we hope can facilitate common spatial tasks for solar energy researchers and provide a platform for consistency and efficiency improvements in these calculations.

Two examples of what are believed to be the most generally useful techniques this package implements are:

• Processing the cloud motion vector from a distributed sensor network.
• Verifying the locations of field components (e.g. combiners) within a distributed network of measurements

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

pip install solarspatialtools

If you wish to run the demo jupyter notebooks locally, you can specify that the additional dependencies should also be installed when you install the package:

pip install solarspatialtools[demos]

Alternatively, you could manually install jupyter in your environment.

A number of example codes are available in the demos folder. These are meant to demonstrate what are believed to be the most useful functions in the package and applications of the functions to real sample data included with the library.

The algorithm implementations in this package primarily involve spatially distributed analyses of solar energy data, necessitating processing of multiple simultaneous time series. Most codes will assume that data is provided using a common format, based upon pandas DataFrame objects. The demo codes use the following convention for the two most important data variables.

• pos or pos_utm - Data for the location of each sensor should be stored in a DataFrame with the sensor id as the index and the latitude and longitude (or UTM coordinates) as columns.
• ts_data - Sensor time series data should be stored in a DataFrame with the timestamp as the index and the sensor id as the columns. The index of pos needs to match the columns of ts_data so that the correspondence between the locations and the time series can be maintained.

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.

Full API documentation is available at ReadTheDocs: https://solarspatialtools.readthedocs.io/en/stable/

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.

synthirrad
Tools for generating synthetic irradiance timeseries and performing downscaling operations on time series data.

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

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.

• Ranalli and Hobbs (2024)
• Ranalli and Hobbs (2024, IEEE PVSC, forthcoming)

This is an open source project and appreciates participation, engagement and contribution from community members. Development on the library is active and the project seeks to provide a useful tool for the research community. The project is currently maintained by an individual researcher, and the process for contributions is not as formalized as it might be for larger projects.

If you've found a bug or have an idea for a new feature, please open an issue on GitHub. Questions can be asked in the GitHub discussions.

Code contributions are also welcome! Please follow the instructions in GitHub's getting started guide to open a pull request.

Changes to the contribution guidelines and policies may be made in the future in response to growth of the project and community.

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

SolarSpatialTools began as a library of tools developed to support my own research activities on solar energy. Initial publication took place under the name of solartoolbox. 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.

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.

Joe Ranalli
Associate Professor of Engineering
Penn State Hazleton
jar339@psu.edu
https://jranalli.github.io/