Short-term solar forecasting based on sky images

Methodology Cloud detection Image projection Irradiance Cloud Motion Summary

Short-term solar forecasting based on sky imagesPotential and challenges

Thomas Schmidt1 Detlev Heinemann1 Elke Lorenz2

1Carl-von-Ossietzky University of OldenburgInstitute of Physics, University of Oldenburg, Energy Meteorology Group

E-Mail [email protected]

2Fraunhofer Institute für solare Energiesysteme ISEHeidenhofstraße 2, 79110 Freiburg

T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

Motivation

Forecast methods

1 Hour

15 Minutes

1 Minute

10 Seconds

1 Second

Point Meter 1 km 10 km 1000 km

Sky imagerStatistical methods Numerical weather prediction

SatelliteI traditional methods lack of

spatial and temporalresolution for small-scaleapplications

I statistical methods /timeseries analysis cannotpredict changes in cloudstate

Motivation

Forecast methods

1 Hour

15 Minutes

1 Minute

10 Seconds

1 Second

Motivation

Forecast methods

1 Hour

15 Minutes

1 Minute

10 Seconds

1 Second

Motivation

Forecast methods

1 Hour

15 Minutes

1 Minute

10 Seconds

1 Second

Satellite

I for small-scale applicationspower fluctuations / rampshave to be addressed

I requires high temporal andspatial resolution

I -> demand for accurateand reliable forecasts

Sky imager forecast model

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Cloud positionCloud height

Solar geometry

Lens function

Image analysis

K-NN model

Measurements

Clear sky irradiance

Global horizontal irradiance

Image features

historical

Real time

Irradiance modeling

POA IrradianceModule temperature

Plant meta data

Power output

PV Power

Cloud motion

Forecast

1. Image analysis

2. Cloud detection

3. Cloud projection

4. Shadow projection

5. Irradiance modeling

6. Cloud Motion -> Forecast

7. PV power modeling

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

K-NN model

Measurements

Image features

historical

Real time

Irradiance modeling

Plant meta data

Power output

PV Power

Cloud motion

Forecast

1. Image analysis

2. Cloud detection

3. Cloud projection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

K-NN model

Measurements

Image features

historical

Real time

Irradiance modeling

Plant meta data

Power output

PV Power

Cloud motion

Forecast

1. Image analysis

2. Cloud detection

3. Cloud projection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

K-NN model

Measurements

Image features

historical

Real time

Irradiance modeling

Plant meta data

Power output

PV Power

Cloud motion

Forecast

1. Image analysis

2. Cloud detection

3. Cloud projection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

K-NN model

Measurements

Image features

historical

Real time

Irradiance modeling

Plant meta data

Power output

PV Power

Cloud motion

Forecast

1. Image analysis

2. Cloud detection

3. Cloud projection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

K-NN model

Measurements

Image features

historical

Real time

Irradiance modeling

Plant meta data

Power output

PV Power

Cloud motion

Forecast

1. Image analysis

2. Cloud detection

3. Cloud projection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

K-NN model

Measurements

Image features

historical

Real time

Irradiance modeling

Plant meta data

Power output

PV Power

Cloud motion

Forecast

1. Image analysis

2. Cloud detection

3. Cloud projection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

K-NN model

Measurements

Image features

historical

Real time

Irradiance modeling

Plant meta data

Power output

PV Power

Cloud motion

Forecast

1. Image analysis

2. Cloud detection

3. Cloud projection

Cloud detection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

I Cloud detection based on binarysegmentation (cloud/sky)

I does not account for differentoptical properties (e.g.transmissivity)

I inhomogeneous brightnessdistribution -> misclassifications incircumsolar area are likely

Cloud detection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

Cloud detection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

Cloud projection

I projection based oncamera model (fish eyelens distortion, cameraorientation)

I cloud distance d to camerais a function of CBH andpixels incidence angle:d = CBH ∗ tan(θ)

I perspective errors increaseto the border of the image

I resolution decreases to theborder of the image

Cloud projection

CBH: 1500 m

Cloud projection

CBH: 1500 m

Cloud projection II

Camera model results for Vivotek FE8172V in OldenburgI left: field of view radius up to 30 km depending on CBHI right: pixel resolution decreases rapidly

Shadow projection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

I shadow projection includes sun position and ray tracing is appliedI accurate shadow projection depends strongly on correct CBH estimationI depending on sun position and CBH the covered area varies throughout the

Shadow projection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

Shadow projection

Camera image

Cloud detection

Shadow projection

Clear Sky Reference

Solar geometry

Lens function

Image analysis

Shadow projection II

Sky Imager

Δd = shadow displacement errorΔd = Δh x ( tan(α) + tan(θ) )

Cloud layer

displaced cloud layer

Δh = cloud base height error

θ = sun zenith angle α = camera incidence angle

Fig.1: Illustration of shadow projection with two different CBHFig.2: Results from camera model

-> Good CBH estimations necessary (ceilometers, triangulation, ...)

Shadow projection II

Sky Imager

Δd = shadow displacement errorΔd = Δh x ( tan(α) + tan(θ) )

Cloud layer

displaced cloud layer

Δh = cloud base height error

θ = sun zenith angle α = camera incidence angle

Fig.1: Illustration of shadow projection with two different CBHFig.2: Results from camera model

-> Good CBH estimations necessary (ceilometers, triangulation, ...)

Shadow projection III

Fig.3: Shadow projection for Oldenburg with CBH = 1500m Fig.4: Shadow projection for Oldenburg with CBH = 1000m

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Multiple sky imager in City of Ulm

I Where to install cameras if a large area shoud be covered?I -> compute shadow projection for different camera configuration, CBH and

sun positions

1500 m CBH at 12 UTC

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sun positions

1500 m CBH at 12 UTC 750 m CBH at 12 UTC

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sun positions

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sun positionsI coverage and overlapping depends strongly on CBH and daytime

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Irradiance

I Aim: Compute surface solar irradiance from shadow informationI Different approaches used:

I statistical (adapting/learning from historic to near-real time measurements)I modeling -> radiative transfer using cloud properties and position

I here: a binary mapping (shadow/no shadow) based on historical data is usedGHI = DNIbinary ∗ cos(θ) + DHIconstant

I -> errors are introduced if irradiance does not follow binary approach (on/off)

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IrradianceI Aim: Compute surface solar irradiance from shadow informationI Different approaches used:

I statistical (adapting/learning from historic to near-real time measurements)I modeling -> radiative transfer using cloud properties and position

I here: a binary mapping (shadow/no shadow) based on historical data is usedGHI = DNIbinary ∗ cos(θ) + DHIconstant

I -> errors are introduced if irradiance does not follow binary approach (on/off)

09:40:00 09:45:00 09:50:00 09:55:00Time in UTC

University of Oldenburg - 2014-07-31

DiffuseDirect

Fig.: Example timeseries (1Hz resolution) of DNI and DHI measurements

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Cloud motion

Forecast

Shadow projection Irradiance

Fig.1: Forecast scheme

2013-04-19 12:05:45 UTC

Fig.2: Cloud motion

I Cloud forecasts based on cloudmotion of frozen cloud field

I Cloud motion vectors (CMV) arederived from subsequent images

I Block matching + Crosscorrelation

I Particle image velocimetry (PIV)I Optical FlowI ...

I Optical Flow computed for anumber of pixels, then averaged toglobal motion

I Assumption: homogeneous singlecloud layer motion + nodevelopment

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Cloud motion

Forecast

2013-04-19 12:05:45 UTC

Fig.2: Cloud motion

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Cloud motion

Forecast

2013-04-19 12:05:45 UTC

Fig.2: Cloud motion

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Cloud motion

Forecast

2013-04-19 12:05:45 UTC

Fig.2: Cloud motion

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Cloud motion

Forecast

2013-04-19 12:05:45 UTC

Fig.2: Cloud motion

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Cloud motion II

I curved CMV estimated on raw images are transformed to straight CMV onprojected cloud map

Cloud motion example(left: raw, center: binary cloud decision, right: projected cloud map)

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Cloud motion II

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Example Forecast

I 25 minute ahead forecasting with1 s / 10 m resolution

I location shown 7 km north fromcamera position

I atmospheric situationI single opaque cloud layer,

homogeneous flowI defined cloud edges

I Potential: clouds and cloud gapscan be predicted if modelsimplifications hold true

I Challenge: timing errors from CBHand CMV uncertainties

I Challenge: irradiance level errorsfrom binary shadow -> irradiancemapping

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Potential and challenges

Potential

I image based cloud/irradiance/power forecasts can predictcloud/cloud gap arrival

I large areas can be covered with single camerasI cameras can be combined to cover whole cities

Challenges

I complex atmospheric conditions require more complex modelingI fish eye lenses introduce perspective errors and reduced resolution

at image bordersI covered area varies with varying CBH and sun positionI accuracy depends on accuracy in cloud detection, CMV and CBH

information and irradiance modeling

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Potential and challenges

Potential

I image based cloud/irradiance/power forecasts can predictcloud/cloud gap arrival

I large areas can be covered with single camerasI cameras can be combined to cover whole cities

Challenges

I complex atmospheric conditions require more complex modelingI fish eye lenses introduce perspective errors and reduced resolution

at image bordersI covered area varies with varying CBH and sun positionI accuracy depends on accuracy in cloud detection, CMV and CBH

information and irradiance modeling

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References

I Schmidt, T., Kalisch, J., Lorenz, E., Heinemann, D.: “Evaluating the spatio-temporalperformance of sky imager based solar irradiacne analysis and forecasts.”Atmospheric Chemistry and Physics 16 (5): 3399–3412, 2016

I Schmidt, T., Kalisch, J., Lorenz, E., Heinemann, D.: “Retrieving direct and diffuseradiation with the use of sky imager pictures.“ presentation at EGU General Assembly2015, Vienna, Austria, 2015

I Schmidt, T., Kalisch, J., Lorenz, E., Heinemann, D., Becker, G.:“Kürzestfristvorhersagen für eine 1-MW PV Anlage basierend aufWolkenkamerabildern.”, Tagungsband des 31.Symposium PhotovoltatischeSolarenergie, Bad Staffelstein, 2016

I Peters, D., R. Völker, T. Kilper, M. Calais, T. Schmidt, C. Carter, K. von Maydell, and C.Agert.: “Model-Based Design and Simulation of Control Strategies to Maximize the PVHosting Capacity in Isolated Diesel Networks - Using Solar Short-Term Forecasts forPredictive Control of Diesel Generation.”, 2016, Proceedings of 32nd EuropeanPhotovoltaic Solar Energy Conference and Exhibition.

I Anagnostos D. G., Schmidt T., Goverde H., Kalisch J., Catthoor F., Soudris D.: “PVEnergy Yield Nowcasting Combining Sky Imaging with Simulation Models’.’, EuropeanPhotovoltaic Solar Energy Conference (PVSEC), Hamburg, 14.-18. September 2015.

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Thank you for the attention!Questions, comments?

Short-term solar forecasting based on sky images

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