solartoolbox 0.3.4

A research toolbox for solar analysis

solartoolbox

Solartoolbox is a collection of tools that are used for my research on solar energy and data analysis of solar variability. I offer apologies in advance, because I'm not a developer, but a solar energy researcher, so this isn't meant to be a perfect API and may not exhibit best practices for software development or programming. Rather, these tools are primarily published for my own use, but are shared publicly if they may be valuable to other investigators or those who try to replicate my work.

The primary features at present relate to working with multisite datasets for variability analysis, including via frequency domain approaches.

Structure of the Library

The codes are currently broken up in a way that made the most sense to me

Packages

dataio
A package with codes for accessing datasets that I've been working with and 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 code. There needs to be some cleanup done there to get things more universal, but for now the codes are able to get the job done.

visualization
Tools for visualizing various types of data or constructing common plots that might be useful for these analyses.

demos
Some demonstration codes and jupyter notebooks to demonstrate usage of the tools.

Function libraries

solartoolbox (root) General tools or wrappers for other functions.

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
• 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 with the key latlon as stated above. 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.

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).

Significant Changelog

Version 0.2

First public release

Version 0.2.1

Add wrapper for pvlib.clearsky_index to handle pandas type

Version 0.2.2

Change input to camfilter to handle references that don't coincide with the site itself. This change breaks code!

Version 0.2.3

Add methods for calculating delay between signals to signalproc

Version 0.2.4

Add some additional options to CMV code

Version 0.2.5

Add field analysis.

Version 0.3.1

A non-backwards-compatible major revision to incorporate field analysis and add more comprehensive testing.

Version 0.3.2

A non-backwards-compatible major revision to create a major speedup on the CMV.

Version 0.3.3

Breaks backwards compatibility. Major speed improvements to field, via computation of the transfer function delays.

Version 0.3.4

Fixes to the way that field handles and excludes nan values.

Author

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