Evaluation of regression-based models for performance assessment

1. BOYD PRO
EVALUATION OF REGRESSION‐BASED MODELS
FOR PERFORMANCE ASSESSMENT
SANDIA‐EPRI 2016 PV Systems Symposium
9May 2016

2. MOTIVATION
Understand project performance
Leverage operational data
Reconcile operational performance with
preconstruction estimates
Revise production estimate
2

3. Can we rely less on the preconstruction model?
• Photovoltaic (PV) Simulation tools such as PVSyst,
Solar Advisor Model (SAM), Helioscope, etc., are
used to estimate the production of a PV project
• These tools are indispensable for preconstruction
estimates where no operational data are available
• After plant operation commences, the production
estimate is revised by updating the simulation tool
design parameters and loss assumptions based on
the updated drawings and operational performance
EXISTING TOOLS AND METHODS FOR SOLAR
PHOTOVOLTAIC ASSESSMENT
10 May 2016
3
Sandia‐EPRI |

4. y = 19,900x ‐ 59,318
R² = 0.9318
0
20,000
40,000
60,000
80,000
100,000
120,000
0 2 4 6 8 10
MERRA 50 Meter Monthly Wind Speed (m/s)
INDUSTRY STANDARDS FOR WIND
PERFORMANCE ASSESSMENT
10 May 2016
4
Sandia‐EPRI |
Can this be applied to solar projects?
Simple “True‐Up” Method1: Linear regression analysis
of availability‐corrected production to wind speed
1 “Simple Method for Evaluating Future Wind Farm Performance” Eric White, AWST and Steve Jones,
DNV, AWEA Windpower 2007, Los Angeles

5. PV performance
is dependent upon
• Irradiance
• Temperature
• Wind Speed
• Soiling / Snow
Suitable solar resource
datasets require
• Consistent long‐term
reference
• Long period of record,
overlap with
production period
• Adequate spatial
resolution
ADAPTATION OF METHOD
10 May 2016
5
Sandia‐EPRI |

6. Simple linear: Global
Horizontal Irradiance
(GHI) versus Gross
megawatt‐hour (MWh)
Temperature‐
corrected MWh
Multilinear*
(American Society for
Testing and Materials
[ASTM] E2848)
Monthly
versus Daily
REGRESSION MODELS TESTED
10 May 2016
6
Sandia‐EPRI |

7. INITIAL RESULTS
10 May 2016
7
Sandia‐EPRI |
y = 10602x + 602428
R² = 0.969
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
4500000
5000000
0 100 200 300 400
Total kWh
Mean of Referemce Data GHI (W/m^2)
y = 366.17x + 17692
R² = 0.9405
y = ‐0.7275x2 + 680.7x ‐ 8513.2
R² = 0.9684
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
0 100 200 300 400
Total kWh
Mean of Referemce Data GHI (W/m^2)
Site A (Central California): Monthly Site A (Central California): Daily
y = 5388.5x + 276448
R² = 0.926
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
2000000
0 100 200 300
Total kWh
Mean of Referemce Data GHI (W/m^2)
y = 201.62x + 5270.6
R² = 0.8594
y = ‐0.3821x2 + 338.22x ‐ 3339.6
R² = 0.8842
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
0 100 200 300 400
Total kWh
Mean of Referemce Data GHI (W/m^2)
Site B (Ontario, Canada): Monthly Site B (Ontario, Canada): Daily

8. • Evaluation of “Sunniness”
• Period of production
versus long term
• Reference data versus
production estimate*
• Availability: Estimated
versus Actual
• Revised P50 Estimate
• Backcast P50 using
regression equation
• Apply long‐term
availability estimate
INTERPRETATION
10 May 2016
8
Sandia‐EPRI |
33,000,000
33,500,000
34,000,000
34,500,000
35,000,000
35,500,000
36,000,000
36,500,000
37,000,000
2013
2007
1997
2008
2002
2004
1999
2001
2009
2012
2014
2003
2011
2010
2000
2006
2005
12,200,000
12,400,000
12,600,000
12,800,000
13,000,000
13,200,000
13,400,000
13,600,000
1999
1998
2007
1997
2005
2004
2001
2012
2008
2006
2003
2009
2010
2013
2014
2002
2011
2000

9. • Known challenges
• Data acquisition
• Availability correction
• Suitable reference data
• Refinements / areas of
investigation
• Availability and lost
energy calculation
• Additional reference
datasets
ONGOING WORK
10 May 2016
9
Sandia‐EPRI |
Image Source: NREL

10. Known Limitations
• Requires a well‐correlated
and consistent reference
dataset
• May not resolve source of
overproduction /
underproduction beyond
resource and availability
Advantages
• Simple and quick method
• Answers “big picture”
questions with respect to
production
• Estimate new long‐term
P50
• Simple assessment of
uncertainty
DISCUSSION
10 May 2016
10
Sandia‐EPRI |

11. QUESTIONS?
BOYD PRO
ProBH@bv.com
CHARITH TAMMINEEDI
TammineediC@bv.com
11

12. www.bv.com