machine learning for supporting automated performance ... › websections... · smart buildings...

dr.ir. Roel Loonen r.c.g.m.loonen@tue.nl

Smart Buildings Symposium – TU Delft – 07/02/2020

Machine learning for supporting automated performance analysis of rooftop PV

Install and forget

► PV systems can fail in many ways:

• Delamination, hotspots, connectors, ageing, hailstorms, etc.

► Sub-optimal PV performance can be detrimental for return-on-investment and sustainability targets

► O&M is essential, but often, there are no clear signs if a system is malfunctioning or not

► Huge opportunity for data analytics and large-scale system monitoring

2

How to quantify performance?

3

• Efficiency

• Yield per installed capacity (kWh/Wp)?

How to quantify performance?

4

How to quantify performance?

5

Typical building applications vs. STC

• Non-optimal tilt and orientation

• Low light conditions

• Temperature effects

• Partly shaded sites

• Year-to-year variability

How to quantify performance?

6

• Efficiency

• Yield per installed capacity (kWh/Wp)

• Performance ratio (PR)

Performance ratio

7

Tool for performance assessment:

PR =𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶 ∗𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐺𝑆𝑇𝐶

=

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑟𝑎𝑡𝑖𝑛𝑔

Performance ratio

8

Tool for performance assessment:

PR =𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶 ∗𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐺𝑆𝑇𝐶

=

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑟𝑎𝑡𝑖𝑛𝑔

PR is originally invented to be able to assess the quality of themodules. Typical values: 0.7 - 0.9.

Performance ratio

9

Tool for performance assessment:

PR =𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶 ∗𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐺𝑆𝑇𝐶

=

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑟𝑎𝑡𝑖𝑛𝑔

PR is originally invented to be able to assess the quality of themodules. Typical values: 0.7 - 0.9.

It compensates for the effect of different irradiance conditions,but still depends on other variables, such as:Temperature, Degradation, Incident angle, Spectral properties etc.

Performance ratio

10

Tool for performance assessment:

PR =𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶 ∗𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐺𝑆𝑇𝐶

=

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑟𝑎𝑡𝑖𝑛𝑔

PR is originally invented to be able to assess the quality of themodules. Typical values: 0.7 - 0.9.

It compensates for the effect of different irradiance conditions,but still depends on other variables, such as:Temperature, Degradation, Incident angle, Spectral properties etc.

We intend to use it to compare real life on-site performance of multiple PV systems

11

Tool for performance assessment:

PR =𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶 ∗𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐺𝑆𝑇𝐶

=

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑟𝑎𝑡𝑖𝑛𝑔

Measured power

Performance ratio

PR is originally invented to be able to assess the quality of themodules. Typical values: 0.7 - 0.9.

It compensates for the effect of different irradiance conditions,but still depends on other variables, such as:Temperature, Degradation, Incident angle, Spectral properties etc.

We intend to use it to compare real life on-site performance of multiple PV systems

12

Tool for performance assessment:

PR =𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶 ∗𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐺𝑆𝑇𝐶

=

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑟𝑎𝑡𝑖𝑛𝑔

Measured power

STC power corrected with actual irradiance

Performance ratio

PR is originally invented to be able to assess the quality of themodules. Typical values: 0.7 - 0.9.

It compensates for the effect of different irradiance conditions,but still depends on other variables, such as:Temperature, Degradation, Incident angle, Spectral properties etc.

We intend to use it to compare real life on-site performance of multiple PV systems

13

Tool for performance assessment:

PR =𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶 ∗𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐺𝑆𝑇𝐶

=

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑟𝑎𝑡𝑖𝑛𝑔

Performance ratio

PR is originally invented to be able to assess the quality of themodules. Typical values: 0.7 - 0.9.

It compensates for the effect of different irradiance conditions,but still depends on other variables, such as:Temperature, Degradation, Incident angle, Spectral properties etc.

We intend to use it to compare real life on-site performance of multiple PV systems

14

Available data:

On site:- Psite measured [W]- Site details (Tilt, orientation,

etc)

Tool for performance assessment:

PR =𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶 ∗𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐺𝑆𝑇𝐶

=

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑆𝑇𝐶

PR is originally invented to be able to assess the quality of themodules. Typical values: 0.7 - 0.9.

It compensates for the effect of different irradiance conditions,but still depends on other variables, such as:Temperature, Degradation, Incident angle, Spectral properties etc.

We intend to use it to compare real life on-site performance of multiple PV systems

Performance ratio

15

Available data:

On site:- Psite measured [W]- Site details (Tilt, orientation,

etc)

At TU/e Solar Measurement Station:- Irradiation data (GHI,DNI, DHI)- Solar position (Azimuth, Zenith)

Tool for performance assessment:

PR =𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶 ∗𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐺𝑆𝑇𝐶

=

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑆𝑇𝐶

PR is originally invented to be able to assess the quality of themodules. Typical values: 0.7 - 0.9.

It compensates for the effect of different irradiance conditions,but still depends on other variables, such as:Temperature, Degradation, Incident angle, Spectral properties etc.

We intend to use it to compare real life on-site performance of multiple PV systems

Performance ratio

16

Available data:

On site:- Psite measured [W]- Site details (Tilt, orientation, etc)

At TU/e Solar Measurement Station:- Irradiation data (GHI,DNI, DHI)- Solar position (Azimuth, Zenith)

G site calculated

Tool for performance assessment:

PR =𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑃𝑆𝑇𝐶 ∗𝐺𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝐺𝑆𝑇𝐶

=

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑆𝑇𝐶

PR is originally invented to be able to assess the quality of themodules. Typical values: 0.7 - 0.9.

It compensates for the effect of different irradiance conditions,but still depends on other variables, such as:Temperature, Degradation, Incident angle, Spectral properties etc.

We intend to use it to compare real life on-site performance of CIGS and C-Si.

Performance ratio

17

Analemma Daily sun path

Sun position at noon in the summer

Sun position at noon in the winter

Visualization

18

Analemma Daily sun path

Sun position at 3 PM in the

winter

Sun position at

3 PM in the summer

Visualization

19

Analemma Analemma - PR

Visualization

20

Analemma Analemma - PR on a shaded site

Visualization

21

Analemma Analemma - PR on a shaded site

OBSTRUCTION

Visualization

22

Analemma Analemma - PR on a shaded site

OBSTRUCTION

OBSTRUCTION

Visualization

Creating analemma from measurement data

23

TU/e SMS

Eliminating datapoints with cloud-shadingBinarizing training dataTrain SVM – find unshaded solar positionsBring back all datapoints and classify them

Performance Ratio

PR =

𝑃𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑𝐺𝑠𝑖𝑡𝑒 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑

𝑃𝑆𝑇𝐶𝐺𝑆𝑇𝐶

=𝜂𝑠𝑖𝑡𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝜂𝑆𝑇𝐶

Evaluate datapoints without local shading

Training Prediction Assessment

24

Shad

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at

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on

Shad

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ee in

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orn

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ree

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even

ing

Conclusions

25

► The proposed method is scalable and is ready to be integrated in automated workflows

• Works with only data that is commonly available

• No human intervention needed

► Support vector machines (SVM) are powerful for pattern detection in problems with geometrical features

Thank you!

26 r.c.g.m.loonen@tue.nl

27

28

c-Si sites

29

CIGS sites