This document summarizes a presentation about quantifying uncertainty when tuning satellite-derived solar irradiance data to ground measurements. It discusses how the length of ground data used for tuning impacts uncertainty levels in the tuned satellite data. Analysis of multiple sites showed that uncertainty decreases as more months of ground data are used, as it reduces the effects of seasonal variations. With less than a year of ground data, seasonal effects can amplify uncertainty, but results stabilize with a year or more of data.

1. Copyright © 2016 Clean Power Research, L.L.C
v052814
Quantifying Uncertainty with
Satellite-to-Ground Tuning
Adam Kankiewicz
Sandia/EPRI 5th PV Performance Modeling Workshop
May 9th 2016

2. Copyright © 2016 Clean Power Research, L.L.C.
Presentation Outline
2
 Motivation
 Ground data
 Satellite irradiance modeling
 How and why we tune satellite data
 Tuning uncertainty
 Key takeaways

3. Copyright © 2016 Clean Power Research, L.L.C.
3
www.solaranywhere.com

4. Motivation: I Have X Amount of Ground Data.
Can You Perform a (Viable) Tuning Study???
How does length or time period of ground data
influence tuning study uncertainty?
GHI(W/m2)

5. Ground-based Solar Resource Monitoring
 Necessary to understand local
variability effects
 Ground truth for tuning process
 Have to place into long term
reference frame for proper
resource context!
Image courtesy of GroundWork Renewables, Inc.

6. Long Term Resource Reference Frame
Satellite data provides the consistent, long
term reference frame needed to derive reliable
estimates of P50, P90, variability, etc.

7. Satellite Data Modeling
Clear sky and cloudy sky errors need to be independently
targeted in any solar satellite data tuning process!
Clear Sky Irradiance
Radiative Transfer Model
+
Cloudy Sky Irradiance
Cloud Modulation

8. Satellite Ground Tuning Methodology
Measure-correlate-predict (MCP) and Model Output Statistics (MOS)
corrections often ignore individual satellite irradiance errors
Clear sky tuning
Cloudy sky tuning

9. Satellite DNI/DHI Rebalancing
Time of Day
4 8 12 16 20
DNI
DHI
GHI Original
Rebalanced
GHI = COS(Z)*DNI + DHI
Not rebalancing data can improperly skew POAI
calculations in energy simulations (PVsyst, SAM, etc.)
4 8 12 16 20
Time of Day
GHI

10. Study Methodology
 Data inputs:
• Hourly averaged irradiance ground data (14 SURFRAD and ISIS sites)
• Hourly averaged SolarAnywhere irradiance data
 The satellite-to-ground tuning process is applied to fixed segments
(1-24 months) of ground data which are rolling by a one month
interval over 5 years
 The tuning results are applied to 5 years of satellite data and
residual error metrics are calculated
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3 month example

11. Individual Site Results: Albuquerque, NM
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Seasonal influences can be dramatic with less than a year of data
Error results stabilize significantly with year + timeframes

12. Individual Site Results: Goodwin Creek, MS
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Seasonal influences can be dramatic with less than a year of data
Error results stabilize significantly with year + timeframes

13. Individual Site Results: Penn State, PA
13
Seasonal influences can be dramatic with less than a year of data
Error results stabilize significantly with year + timeframes

14. Overall Results
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Similar trends at all locations

15. Overall Results
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Decreasing envelope of uncertainty with increased
month selection independent of location

16. Key Takeaways
 Seasonal impacts can be amplified with less than a year
of ground data
 We can provide uncertainty for tuning studies based on
X amount of ground data
 See further results presented at IEEE PVSC
16

17. The information herein is for informational purposes only and represents the current view of Clean Power Research, L.L.C. as of the date of this presentation.
Because Clean Power Research must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Clean Power Research,
and Clean Power Research cannot guarantee the accuracy of any information provided after the date of this presentation. CLEAN POWER RESEARCH, L.L.C.
MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
Thank you
Skip Dise
SolarAnywhere Prod. Manager
johndise@cleanpower.com
Adam Kankiewicz
Solar Research Scientist
adamk@cleanpower.com
Please feel free to contact us for any details or clarification related to presentation
Tom Staples
Senior Account Executive
tstaples@cleanpower.com

18. Impact of Non-Average Years on Tuning
18
Ground site Year Residual MBE Variance from long term annual average
Penn State
2010 0.67% 1.48%
2011 0.55% -4.16%
2012 -1.52% 1.46%
2013 0.39% -2.29%
Goodwin
Creek
2010 0.01% 4.40%
2011 0.21% 1.15%
2012 0.17% 1.90%
2013 1.25% -4.33%
Albuquerque
2010 -0.59% 0.15%
2011 0.97% -0.84%
2012 1.11% -3.91%
2013 0.93% -1.39%
CPR tuning is not affected by above or
below average solar irradiance years