This document summarizes several data analytics projects from DuraMAT's Capability 1. It discusses (1) the goals of using data analytics to provide data mining and visualization capabilities without producing data, (2) a project to design an algorithm to reliably distinguish clear sky periods from GHI measurements to improve degradation rate analysis, (3) building interactive degradation dashboards to analyze PVOutput.org data and make backend tools more visual, and (4) additional analyses of contact angle and I-V curves. Future directions include relating accelerated testing to field data, collaborating with other analytics efforts, and being open to new project ideas.

1. DuraMAT Capability 1:
Data Analytics
Anubhav Jain
Lawrence Berkeley National Laboratory
August 28, 2018

2. Goals of the data analytics thrust
Field deployment
Data Hub Data analytics
Materials
Forensics &
Characterization
Predictive
simulationModule testing
Techno-economic
analysis
Data analytics is a cross-
cutting thrust that works with
DuraMat partners to provide
software, visualization, and
data mining capabilities.
This thrust does not produce
data itself!

3. Project 1: Clear Sky Detection (background)
Jordan, D. C., Deline, C., Kurtz, S. R.,
Kimball, G. M. & Anderson, M. Robust
PV Degradation Methodology and
Application. IEEE J. Photovoltaics 8,
525–531 (2018).
In many analyses, data filtering /
preprocessing greatly affects final
results!
D. Jordan et al. showed that clear
sky filtering produces degradation
rates closer to expectation and
20% different than without filtering!
Generally clear
Scattered cloudsPersistent clouds
Can we design an algorithm that automatically and
reliably distinguishes clear sky periods based on GHI
measurements – across locations and data frequencies?
Approach: use satellite data to modify a published clear
sky detection technique (Reno and Hansen, 2016) to
generalize across locations and data frequencies.

4. Project 1: Clear Sky Detection (results)
Using satellite clear sky labels as a guide,
we can design an “optimized” clear sky
detection algorithm with no parameters
that works better than existing pvlib
across sites and data frequencies!
Heatmaps
plot F0.5
scores, or
classification
accuracy, of
clear sky
algorithms
Visual inspection confirms that clear sky
classifications from the optimized
algorithm are more relevant and correct.
Next: integration into pvlib and/or rdtools
default
optimized
Sample GHI
data and clear
sky
classifications
for BMS site,
30 minute data
frequency

5. Project 2: Degradation dashboards (background)
Backend tools like rdtools (NREL, kWh Analytics) expose many powerful functions for
processing and analyzing data through programmatic (e.g., Python, MATLAB) APIs.
How can we make some of these tools more visual, interactive, and exploratory?
Jordan, D. C., Deline, C., Kurtz, S. R.,
Kimball, G. M. & Anderson, M. Robust
PV Degradation Methodology and
Application. IEEE J. Photovoltaics 8,
525–531 (2018).
www.github.com/NREL/rdtools

6. Project 2: Degradation dashboards (results)
We have built interactive dashboards that
analyze data from PVOutput.org
Goal: make the backend tools interactive!

7. Misc. - contact angle analysis, I-V curve analysis
Automated contact angle
analysis for evaluating anti-
soiling coatings
E x t r a c t i o n o f I - V c u r v e
parameters, detection of
m i s m a t c h , a n d l e a r n i n g
“features” of I-V curves

8. Future directions
• Relating combined accelerating stress testing (C-AST)
and field measurements
• Working with other data analytics efforts in the field
(e.g., Case Western)
• Open to other project ideas!
– contact: ajain@lbl.gov
• Collaborators: Benjamin Ellis, Mike Deceglie, Birk Jones,
Josh Stein, Dirk Jordan, Laura Schelhas, Stephanie
Moffitt, Margaret Gordon