Poster given at the DuraMat Fall workshop, Aug 28, 2018

1. Discover, Develop, and De-Risk module materials,
architectures, accelerated testing protocols,data analytics,
and financial models to reduce the LCOE of solar energy
Unexpected mismatching during
midday requires further
investigation
RdTools collaboration (collaboration with NREL and kWh Analytics)
• We are building a robust clear sky detection method for Rdtools, an open-source
software toolset for calculating degradation rate of PV systems.
Previous clear sky detection work
• Work by SNL researchers Reno and Hansen1 provides detection implemented in PVLib
• Our goal is to automatically learn the best PVLib parameters by scoring PVLib clear sky
labels versus known clear sky labels across locations and data frequencies
• Known clear sky labels determined from NSRDB, which provides satellite data across
continental US from 1998-2015 at a 4x4 km resolution
Capability 1: Data Management & Analytics
Benjamin Ellis1, Robert White2, Mike Deceglie2, Birk Jones3, Josh Stein3, Jonathan Trinastic4, Anubhav Jain1
1Lawrence Berkeley National Lab, 2National Renewable Energy Lab, 3Sandia National Lab, 4 U.S. Department of Energy Sunshot program
• Ensure DuraMat data infrastructure supports analytics tasks
• Provide data analytics, machine learning, and software
support to PV researchers within collaborative projects
• Develop PV analysis software toolkits and predictive models
to estimate performance and degradation
• Help researchers use and understand PV toolkits, e.g.,
through interactive web sites and visualizations
• Formalizing data standards and best practices with other
capabilities / collaborate on DuraMat Data Hub
• Developed clear sky classification software based on GHI
• Developing a PV degradation web dashboard
• In progress: I-V curve analysis, image analysis, and combined
accelerated stress testing analysis
• Data Hub design complete, data sets collected from most
capabilities
• New clear sky models may allow for site-agnostic
classification of clear / cloudy days based on GHI
measurements alone and with no tunable parameters
• Currently seeking collaborators with PV data in need of data
analysis and machine learning
Capability Goals Accomplishments Outcomes and Impact DuraMAT Capabilities
1. Data Management & Analytics, DuraMAT Data Hub
2. Predictive Simulation
3. Advanced Characterization & Forensics
4. Module Testing
5. Field Deployment
6. Techno-Economic Analysis
Capability Development
IV curve data analytics Mismatching trends at SNL
Clear sky detection Clear sky detection results
Timeline
PV degradation dashboard
Future work
Field
deployment
DataHub Data analytics
Materials
Forensics &
Characterization
Predictive
simulation
Module
testing
Techno-economic
analysis
Data analytics and DuraMAT Data Hub
• Develop and advertise software tools for analytics, modeling
and visualization of stored data sets
• Be able to combine analyses across capabilities and projects
Capabilities and researchers
upload and disseminate data
Discover new data sets and
software tools to enhance research
Capabilities, Data Hub, and analytics
teams can communicate on data storage
and software tools
Data analytics
• Directly collaborate on research projects
• Provide data mining, analytics, visualization, and
machine learning support
• Capabilities and analytics team can prototype software
tools and provide feedback
• Software developed during research process will be
made freely available to other PV researchers
IV curve analytics software (collaboration with SNL)
• Sandia National Labs is collecting in-situ, string level IV curves
• We are developing open-source software to provide PV researchers with consistent
and transparent methods for IV data preprocessing, cleaning, and feature extraction
• The software detects typical IV parameters (e.g. Rs, Rsh, Voc, Isc, Pmax, etc) along with
detection of mismatch in string-level curves
Mismatch detection and parameter extraction
• This analytics software is currently being used to investigate mismatching and
degradation in systems at SNL
• Automatic identification of mismatching along IV curves is useful for monitoring
performance, calculating degradation degradation, and diagnosing failures and faults
• Extracted IV parameters can also be used for PV modeling and diagnostics; the
extracted values closely agree with those measured by IV tracing system
Data Hub development
• Hub will host data ranging from time-series performance
data to spectroscopic studies to literature surveys and
fundamental materials properties
• Establish data and metadata standards and best practices
• Implement advanced sorting, filtering, querying, and
aggregation methods to link data sets from multiple
specializations, projects, and experiments
1. M. J. Reno and C. W. Hansen, “Identification of periods of clear sky irradiance in time series of GHI
measurements,” Renew. Energy, vol. 90, pp. 520–531, 2016.
2. 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)
Automatic mismatch identification IV parameter extraction
Mismatch occurrence versus irradiance
Occurrence of mismatch per time of day
Late afternoon mismatch likely
due to shading
Cumulative mismatch over time
Future work
We have several other projects underway, including:
• Image analysis for evaluating contact angles of anti-soiling coatings
• Relating combined accelerating stress testing (C-AST) and field measurements
• Working with other data analytics efforts in the field (e.g., Case Western)
• Analyzing temperature data across the U.S. (e.g., to determine string sizing)
Frequent mismatching during
high irradiance periods in both
strings is unexpected
Investigating if modules are
obstructed or systematic faults
Two strings with same modules
display different behavior in
cumulative mismatch detected –
we are currently investigating
the cause (e.g. different system
locations/configurations or
faulty hardware)
Optimization algorithms and resutls
• Gaussian processes and genetic algorithms search PVLib parameter space by
predicting regions of low error based on previous score/parameter pairs
• We determine a new set of pvlib parameters that greatly outperform the defaults
• Next: integration into pvlib and/or rdtools
Web dashboards for exploratory data analysis
• PV analysis tools are typically implemented as software packages, e.g., in Python or
MATLAB. However, it can be difficult for some users to conduct exploratory analyses
with these tools.
• We are developing a web dashboard that connects together data (here, from
PVOutput.org) with state-of-the-art analysis tools (e.g., rdtools degradation models)
D. Jordan et al. showed2 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?
Approach: use satellite data to modify a published clear
sky detection technique1
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.
default
optimized
Sample GHI
data and clear
sky
classifications
for BMS site,
30 minute data
frequency
Automated contact angle
analysis for evaluating anti-
soiling coatings
Interested in a data analytics
project collaboration?
Contact us: ajain@lbl.gov
This work was funded as part of the Durable Modules
Consortium (DuraMAT), an Energy Materials Network
Consortium funded by the U.S. Department of
Energy, Office of Energy Efficiency & Renewable
Energy, Solar Energy Technologies Office.